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The Latest Cybersecurity News & Insights From Around The World
Lunar Cyber Launches Token Exposure Monitoring as Infostealers Target Developer and AI Credentials
New capability identifies, attributes and validates API keys, OAuth tokens and other machine credentials stolen from developer and employee endpoints.
Security Risk Advisors Launches SCALR AI as a Free SOC AI Platform for Security Teams
Security Risk Advisors (SRA), the authors of the free VECTR platform, announce today another great free platform launch: SCALR AI. Security teams can get SCALR AI for free, delivered through the Azure Marketplace.
Has security taken a back seat to productivity?
We told everyone to adopt AI, and they did. Almost anyone can now produce polished, professional work in seconds. The newest shortcut behind that speed is skills: small files that hand an AI agent a new ability. Skills are also where the risk now sits. One file can hold dozens of instructions and actions, so anything buried inside travels with it, and the agent follows all of it without asking. Most of those agents run unwatched, so the speed you gained is now exposure nobody in the business is measuring.
Secure What Agents Do, Not Just What They Say
Salt Security and CrowdStrike give joint customers visibility across the full agentic path, from the model to the system where the action lands An employee at a bank asks an AI assistant a routine question. How much money is in my savings account? The assistant answers correctly. The prompt was legitimate. The response was accurate. A model-layer security control inspects both and finds nothing wrong, because nothing is wrong with either. Now look at what happened in between.
The Trillion-Dollar AI Bet Needs a Security Strategy
AI agents have now proven, in the real world, that autonomy without a security model is a liability. At Arctic Wolf, we’ve spent years watching that same lesson play out with cloud migrations, remote work, and every other rush of new technology, and this is that pattern showing up again, just faster.
How Zenity Implements the 2026 OWASP Top 10 for LLM Applications
Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know Every AI security framework names the risks you have to control. Zenity is built to implement those controls at runtime. Here's the 2026 OWASP Top 10 for LLM Applications, entry by entry, with the gaps marked honestly. Paste a booby-trapped instruction into a chat window, and nothing much happens.
Hot Take: Over-permissioned agents will be the next big breach.
The next big breach won't be a hacker. It'll be an over-permissioned agent. Someone approved an agent action at some point, for a reason that made sense at the time. But access persisted long after a task was performed. Stale permissions without human intervention could turn into exposure. Listen to perspectives from people who see this problem from different roles.
The Truth About Insider Threats: AI, Paranoia & Hybrid Work | MSP Series
Every organization thinks insider threat is someone else's problem — until it isn't. Join host Alex Courson as she sits down with two leading experts who have spent their careers on the front lines of insider risk: Shawnee Delaney (former CIA-trained DIA case officer and CEO of Vaillance Group) and Peter Hadjigeorgiou (Field CISO at Teramind). Whether you run a business, work in IT, or manage people in a remote, hybrid, or in-office environment, this conversation is for you. We dive deep into AI in the workplace, workplace paranoia, and how organizations need to evolve to drive real change.
How To Control Which WordPress Abilities an MCP Client/AI Agent Can Use
Connecting ChatGPT, Claude, or another AI agent to WordPress can turn simple instructions into real website actions. An agent can create posts, update pages, upload media, manage orders, and handle other tasks when the right MCP tools are available. The interesting part starts when you decide how much access to give it. A content management agent may need posts and media, while a WooCommerce support agent may need orders and customer information.
What Is Prompt Injection, Really? Why the Textbook Answer Cannot Tell You If You Have an Incident
Prompt injection is three things happening at once. The definition written in 2022 described a model that followed an instruction hidden in the text it was asked to translate. The definition needed in 2026 describes an agent that read a support ticket and then queried a customer table it had never touched, using a service account nobody had revoked. Those are the same attack.
Prompt Injection Examples: 10 Real Attacks, and Which Ones Would Work on Your Agent
Prompt injection has not changed since 2022. What the model can do has. The instruction that hijacked a translation bot four years ago and the one that opened a homeowner’s windows last year are the same request: do something you are already allowed to do. The first system could only talk. The second could operate devices. Same sentence, different consequence. Here are ten real attacks, in order, and for each one the thing the system was allowed to do that made it work.
AI Model Governance: Framework, Roles, Controls and Implementation Checklist
AI models rarely become a governance problem because of the model alone. The real risk often emerges from what surrounds it: the data it receives, the decisions it influences, the systems it can access, and the people accountable for its outcomes. That makes AI model governance a lifecycle discipline, not simply a model approval process. Even NIST’s AI Risk Management Framework treats governance as a function that cuts across the entire AI lifecycle.
One Identity Launching New AI-Powered Support Portal
Starting on the 31st of August 2026, the One Identity Support Portal moves to a new AI-powered customer experience. This short video prepares you with what to expect.
Why Your WAF Isn't Enough: Runtime Protection for AI Agents and APIs
Most security leaders believe their API attack surface is covered. A Web Application Firewall (WAF) sits in front of the application. An API gateway manages authentication, rate limiting, and schema validation. Some teams add a bot management layer on top. This looks like defense in depth. In practice, it repeats the same layer, the perimeter, multiple times. Most API breaches do not start with a WAF bypass.
1Password signs OpenAI open letter calling for collective action on cyber defense
OpenAI’s open letter on collective cyber defense warns that defenders have a limited window to strengthen security. It urges organizations to fix their highest-risk weaknesses, build least privilege and strong access controls, verify fixes, and make agentic identities traceable and accountable.
Warning: Malicious AI Tools Are Spreading in the Criminal Underground
Criminals are now selling malicious AI tools for use in cyberattacks, according to researchers at Trellix. These tools dramatically lower the barrier for unskilled crooks to launch sophisticated attacks. “In the first half of 2026, the Trellix research team identified multiple distinct AI-related offerings across major underground forums,” the researchers write.
The Three Questions Every AI Telemetry Claim Should Survive
Coding-agent telemetry, today, cheaply, answers four real questions: which agents are running and operated by whom, what an agent invoked, what happened in a session in order, and whether a run looks abnormal. Part 1 of this series covers that case in full. This part is about the fifth question every security team eventually asks, the one no amount of instrumentation answers on its own: can this record be trusted enough to build a control on it?
What we learned about AI agent security by monitoring our agents
AI agents comprise models, instructions, data, and tools, so thoroughly investigating potential security risks requires evidence from several components. As Datadog teams build AI agents for internal workflows, we use Datadog AI Guard to monitor how they handle each component during a session. We’ve found that application logs may capture an agent’s final API call without showing which prompt, retrieved content, or tool result led to the action.
AI Agent Threat Response: Why Pre-Runtime Controls Matter More Than Runtime Detection
AI agent threat response starts before runtime. See why pre-runtime credential controls stop agent misuse that runtime detection can only observe.
When AI adoption outpaces IT visibility
At 1Password, we started expanding our use of AI with a familiar IT playbook. We identified the problems we wanted to solve and the tools that could help us achieve those goals. The plan was straightforward: enable teams, move quickly, learn what worked, and build the visibility needed to manage the cost.
How to start the AI-accelerated defense
Early on in the AI adoption boom, I gained a reputation for just throwing everything at it to see what would stick. That wasn’t the most effective strategy, and my token usage was crazy high. There are a ton of talks and posts on all the cool ways you can use AI for detection engineering, but I didn’t see any that showed you where to begin.
When 700 Agents Coordinate Without Being Told To
Two reports landed yesterday on the July incident in which OpenAI agents left an isolated test environment and reached Hugging Face production systems. OpenAI published a thirty-seven page technical post-mortem. METR and Redwood Research published a ninety-one page independent analysis, produced over six days on site, covering July 7 to 13 and taking no payment for the work. The coordination numbers are what drew attention.
GPT-5.6 Sol Shows Why a Better Model Isn't a Uniformly Safer Model
Veracode Research’s latest secure-coding test finds GPT-5.6 Sol with a 15-point Python gain beneath modest aggregate movement, evidence that cyber capability and secure-code generation do not move in lockstep. OpenAI calls GPT-5.6 Sol its “strongest cybersecurity model yet.” Veracode’s extension test finds it scoring only two percentage points higher overall on secure-code generation than GPT-5.5, but it scores 15 points higher in Python.
The Missing Piece in Your AI Strategy: Governance
AI governance is failing to keep pace with AI adoption inside businesses. When 100 people at a technology event were asked whether they used AI, every hand went up, but when asked about AI governance, every hand went down.
The AI challenge most companies don't have
A few months ago, I attended a GC AI Summit hosted by Harvard Law School. As expected, there was plenty of discussion about AI tools, governance frameworks, emerging regulations, and the future of the legal profession. One topic of discussion stood out above the others: Most organizations only need to think about how they deploy AI, whereas we have to think about how we deploy AI and how we develop AI.
Top 7 Technology Strategies Growing Businesses Need to Stay Competitive
Growing businesses face mounting pressure to modernize their operations while competitors race ahead with emerging technologies. The gap between those who adapt and those who fall behind widens each quarter, making strategic technology adoption no longer optional but essential for survival. Seven core strategies have emerged as critical differentiators in today's market, each addressing specific operational challenges that determine whether a company scales successfully or stagnates. Understanding these approaches reveals why some organizations thrive while others struggle to keep pace.
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Why AI is becoming harder to budget for
For most business technology, the cost is relatively easy to understand. You buy a licence, agree a contract or pay for a certain level of usage, and you have a reasonable idea of what you will spend over the year. AI is making that much harder. As businesses move beyond individual AI subscriptions and start using AI across more of their operations, costs can vary considerably depending on which models are being used, how often they're being used and what they're being asked to do.
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Identity Everywhere: Bringing Infrastructure Identity to Agentic IT
Every era of computing eventually exposes the assumptions that made the last era work. For decades, networking succeeded because it was mostly identity agnostic. Packets moved because they had addresses. Routers and switches forwarded traffic because the network knew where something was going, not necessarily who or what was behind it. That model helped build the internet and modern enterprises. But it is not enough for the era we are entering now.
Aikido Security achieves ISO 42001:2023 certification for AI governance
Aikido Security has achieved ISO 42001:2023 certification, the international standard for AI management systems, a step few security vendors have taken so far. The certification confirms that Aikido runs a structured, continuously improving governance system for managing the risks introduced by its AI-enabled features, across our entire platform.
Zombie APIs Are Costing You More Than You Think: A Risk Quantification Guide
Zombie APIs are API versions or endpoints that were once known and documented, but were never properly retired. A team ships v2 of an API, tells everyone to migrate, and assumes v1 is dead. In reality, v1 is still running on a server somewhere, still accepting requests, and still connected to production data. This is different from unmanaged APIs, which were never documented in the first place. Zombie APIs were documented once.
AI Assurance: The Third Head of Your AI Governance Watchdog
In July 2026, two AI stories broke that appeared unrelated on the surface. But were they really? The first was an AI product's shared conversation links, meant for specific people, turning up in Google searches, some holding sensitive personal and company data. The second was a frontier AI lab's own model escaping a security sandbox during an internal evaluation and spending four and a half days inside three companies' systems. One involved ordinary users making a common mistake.
Agent Incident Response: Containment Is the Easy Part
Containment guidance for agent incidents already exists and it is largely correct. Revoke the tokens, freeze the orchestration tier, cut egress, set the vector store to read-only. Those steps take minutes and any competent team will find them. The difficulty sits either side of containment. Deciding what kind of incident this is takes longer than stopping it, establishing what the agent did before you stopped it takes longer still, and both depend on preparation that has to exist beforehand.
AI/LLM Penetration Testing in 2026: The Complete Guide
Most organisations now run at least one LLM in production, and a growing number run agents that call tools and act without a human in the loop. The security testing those systems receive was designed for deterministic software. AI applications fail differently. The payload is natural language, the same input can be safe nine times and unsafe on the tenth, and the malicious instruction often arrives inside a document or tool description rather than from the user.
Why Your AI Application Is Exposed
Imagine getting three separate security reports back for your new enterprise AI assistant: On paper, the application looks ready for production, but in reality, a threat actor bypasses your guardrails in minutes. How? By using the AI model as an intermediary. The attacker steers the LLM to invoke the internal utility tool, thereby bridging an untrusted prompt directly to the backend execution sink.
Secure AI Agents, Everywhere: Why Prompt Injection Is Only Part of the Problem
Ask AI to Choose a prompt Write a TLDR of this post Explain the security risk Summarize what CISOs should know The rules have changed. In every AI deployment, the agent itself is now part of the threat model, and that's a first for enterprise security. Prompt injection gets most of the attention, and for good reason: it doesn't require access to source code, credentials, or network infrastructure. It exploits the fundamental mechanism by which language models process instructions.
Inside SECNAP: An Agentic MDR Platform Built on LimaCharlie
Joshua Strickland from SECNAP shows how his team built a full agentic MDR platform on top of LimaCharlie. SECNAP layers its own customer portal and SOC workflows directly on LimaCharlie's API, giving AI agents the same access to telemetry and response actions as a human analyst. Joshua will walk you through the customer portal, the SOC dashboard, and a live attack simulation on a sandboxed machine, including how AI agents handle tier one and tier two triage with Sonnet and Opus, and how a human analyst reviews and approves response actions before anything ships.
Inside the AI-Accelerated Cyber Underground
Cyberattacks take shape long before a breach through exposed systems, vulnerable software, stolen credentials, underground tools, and attacker experimentation. In this webinar, Emma Stevens, Threat Intelligence Researcher at Bitsight, and Qionglu Lei, Senior Product Marketing Manager at Bitsight, explore what Bitsight research reveals about AI-enabled attacker behavior and the changing cyber underground.
The Problems We Solve Are Not Sexy
The problems we solve are not sexy. That is Garrett Hamilton on Inflection Point: Digital Intelligence Podcast, and he means it as a badge of honor. Problems that have been around for 30 years are rarely glamorous. They are also the ones that take the business down when they go wrong. Why is it more important than ever to tackle these problems?
What is DLL hijacking, and why your new AI plugin might be the easiest way in
DLL hijacking is a decades-old Windows vulnerability class (Mitre Att@ck T1574) getting new life from AI plugins bolted onto legacy desktop apps. Attackers plant a malicious DLL where a vulnerable app will load it instead of the real one, inheriting that app's privileges. To detect it, watch for DLLs loaded by name from writable folders. To prevent it, you have to fix the app's load-order logic or blocking untrusted DLLs at the endpoint.
The Competitive Edge of AI Procurement Solutions for Modern Enterprises
Modern enterprises operate in an environment where efficiency, cost control, and agility can directly influence business growth. Procurement, once viewed primarily as an administrative function, has become an important strategic area for organizations looking to improve financial performance and build stronger supplier relationships. As procurement processes become more complex, businesses are increasingly turning to AI Procurement Solutions to simplify operations and make better purchasing decisions.
New Bitsight Research Shows AI Abuse Is Moving Beyond the Jailbreak Prompt
Jailbreak prompts (i.e. prompts designed to remove or bypass the guardrails and rules that govern AI systems, like LLMs) prompts have been circulating for years. At first, a lot of it was pretty simple: copy a prompt, tell the model to ignore its rules, and see what happens. It was also largely noisy, unverified, and often didn’t work. But the noise was still telling us something. Threat actors were beginning to study AI systems the same way defenders were, and over time, the goal started to change.
Vanta on the Street
AI is part of everyday life now, which means trust and privacy aren’t just security team questions anymore. So naturally, we needed to know how New Yorkers are handling it. Vanta on the Street is back.
Coding Agents Just Reopened Your Software Supply Chain Blind Spot
Most organizations spent years hardening their software supply chain. The model is familiar: dependencies flow through a controlled repository, policies determine what is allowed, scanning catches what slips through, and every action is logged for auditability. It works because human developers operate within environments that enforce these rules. AI coding agents break that assumption entirely.
What Is Data Loss Prevention (DLP)? Types, Use Cases, and Best Practices
Protecting sensitive data is no longer just a compliance objective. It has become a prerequisite for adopting cloud services, enabling AI, and maintaining customer trust. Yet many organizations still struggle to balance innovation with effective data governance. A 2025 Forrester Total Economic Impact study commissioned by Microsoft found that organizations using mature data protection capabilities, including data loss prevention (DLP), achieved a 30% reduction in the likelihood of data breaches.
What makes a good AI coworker? With OpenAI's Codex product lead
Zero-Shot Learning is a podcast about how AI is built, secured, and deployed. Hosted by Nancy Wang, 1Password CTO, and Dev Tagare, Senior Director of Engineering at Google Gemini, it offers a builder’s view of the architecture and complex decisions involved in shipping AI.
Agentic AI Security: Credentials and Permissions Define the Blast Radius
In July 2026, researchers at Noma Labs coaxed GitHub's new Agentic Workflows into leaking data from a private repository. It wasn’t from malware. They created a plausible-looking issue in a public repository containing instructions for the agent to retrieve information from other repositories in the organization. After testing variations of the prompt, they found that adding one word, "Additionally," was enough to get past GitHub's guardrails.
Code is the easy part with Rohan Varma from OpenAI | Zero-Shot Learning
As a product leader who went from working on Cursor to OpenAI’s Codex, Rohan Varma got a personal preview of a shift most developers are just beginning to catch up to. His conversation with 1Password CTO Nancy Wang upends the idea that AI helps developers write code faster, reimagines code reviews, and explores how developer skills expand once agents take over implementation. In this episode: Zero-Shot Learning is a builder-to-builder podcast about how AI systems are designed, deployed, and secured. Subscribe for more.
State AI Laws Change Faster Than Compliance Programs
Colorado passed the first comprehensive state AI law in May 2024, and organizations spent the following year building impact assessment processes against it. Those obligations never took effect. The statute was delayed twice, blocked by a federal court, then repealed and replaced by a narrower framework before its own effective date arrived. Anyone who built a compliance program to that specific statute prepared for a regime that never existed.
10 Shadow AI Detection Tools for Enterprise Data Security
As artificial intelligence becomes deeply woven into daily workflows, employees are adopting unapproved AI apps at an unprecedented pace. According to Teramind’s Shadow AI Behavior Report, a staggering 89% of workplace AI usage occurs outside enterprise-governed channels, leaving 86% of organizations blind as to how corporate data flows in and out of these tools.
How a Solo Garden Designer Cut Concept Time in Half with AI Tools
I used to spend entire evenings sketching rough layouts for clients who just wanted "something prettier than the current yard." By the time I produced three decent options, the client had already moved on or decided the project could wait. That cycle was exhausting and expensive for both of us.
How to Use AI to Turn Images into Engaging Video Content
Video has become one of the most effective ways to communicate online. From social media posts to product promotions, businesses and creators increasingly rely on video to capture attention and reach wider audiences. However, producing video content can take significant time and resources. Writing scripts, recording footage, editing clips, and adding effects can quickly turn a simple idea into a lengthy production process.
Human Error Remains at the Core of AI-Enabled Social Engineering
AI is making social engineering attacks significantly more effective, according to a new report from cyber insurance firm Resilience. These attacks were behind more than 85% of losses in the first half of 2026, compared to less than 20% during H1 2024. “Losses tied to phishing, social engineering, and transfer fraud have climbed from 17.7% of incurred losses in H1 2024 to 85.3% in H1 2026, the single largest increase in the report’s five half-year comparison,” Resilience says.
Why Self-Healing Is the Only Way to Secure at Frontier AI Speed
For twenty years, the software security playbook has worked the same way. You find the vulnerability, score it, open a ticket, assign it to a human, wait for the fix, ship the patch, and prove it happened. Every step in that sequence assumes humans can review each fix individually and still keep up. Frontier AI broke that assumption. The exploit window has collapsed from weeks to hours. Attackers reason across your codebase, chain their findings, and ship exploits before a CVE is even published.
Agent Immunization: A New Model for Building Trusted AI Agents
The riskiest thing an AI agent does all day isn’t writing code. It’s shopping. Every few minutes, it reaches out for a package, an AI asset, or a tool, and pulls it in with no real way to check what’s inside. We think the fix is agent immunization: security that lives inside what an agent consumes, builds, and ships, not a wall built around it.
How Aikido finds more vulnerabilities than Claude Security at half the cost
Claude Mythos is arguably the strongest cybersecurity model that Anthropic has built. But we know that model capability is only part of what determines how well an AI vulnerability product performs. To test that, we put Anthropic’s Claude Security, which runs on Mythos, and Aikido Code Security Audit head-to-head on the exact same target to see which harness can deliver the best coverage and at what cost. Code Security Audit is part of Aikido’s AI Code Analysis suite.
What OpenTelemetry Can Actually Tell You About Your AI Agents
The distance between what OpenTelemetry was built for and what AI governance is asking of it shows up in a single number. Distributed tracing descends from Dapper, the 2010 Google paper that gave the industry the vocabulary of traces and spans. Dapper sampled one trace in 1,024. That is ample for finding a latency regression, because a regression recurs and the next sample catches it.
AI Supply Chain Security: Why an SBOM Cannot Cover It
A software bill of materials works because software changes through a build. Someone bumps a dependency, the pipeline runs, the manifest updates and a scanner compares the new list against known vulnerabilities. Every part of that loop assumes a rebuild is the thing that changes behavior. AI systems break that assumption at the point it matters most. Editing a system prompt changes what a model does, swaps no dependency, triggers no build and produces no new manifest.
From AI Findings to Action: How Security Teams Should Triage AI-Discovered Vulnerabilities
Security teams didn’t need a headline to tell them that vulnerability volumes continue to be problematic. The CVE database now contains over 354,000 records. Annual disclosure rates have climbed steadily for more than a decade. And remediation backlogs have long been recognized not as an aberration, but as a fixture of the job.
Auto Triage with a Brain
Most agentic triage solutions hand you a verdict and stop right there. Maybe you get an alert ranking, maybe a severity score, maybe some light investigation, but you, as the analyst, still have to do the cleanup manually. There is still a mountain of evidence that needs to be gathered by hand, and a mountain of cases to dig through on your own.
Why Is a Reranker Needed in RAG If We Have a Retriever?
Enterprise RAG pipelines have a recall stage and a precision stage. The retriever handles recall. The reranker handles precision. Skipping the reranker, or misplacing security controls around it, is where most accuracy and data exposure problems begin. The retriever’s job is to pull back every document that might be relevant. The reranker’s job is to find, from that candidate set, the documents that actually answer the question.
Report: AI Chatbots Are More Effective at Building Trust Than Human Scammers
A study has found that AI chatbots can be more effective at social engineering than human scammers, WIRED reports. The researchers looked at a form of romance scam commonly known as “pig butchering,” in which scammers spend weeks or months building a relationship with the victim before tricking them into sending money for a phony investment scheme.
Top 14 Agent Observability Tools
Agent observability tools capture traces, metrics, logs, and evaluations across AI agent workflows. They help teams reconstruct execution paths, inspect tool calls and handoffs, diagnose failures, and monitor latency, cost, and output quality. This guide also covers complementary security platforms that discover agents, enforce runtime policies, or control the privileges agents receive. Agents don’t fail in straight lines.
How SLED can win the cybersecurity race with agentic AI
Adversaries are using AI to launch cyber attacks in record time, forcing security teams to measure responses in minutes instead of months. Phishing campaigns built with large language models (LLMs) achieve click-through rates 4.5 times higher than traditional methods,1 and the average time between initial compromise and lateral movement has fallen to just 29 minutes.2 This is a 65% increase from the prior year.2 State and local governments and higher education institutions are at an inflection point.
How autonomous pentesting kills false positives
Ask any security engineer what they actually think about their vulnerability scanner, and you will get a version of the same answer. They trust maybe 20% of what shows up in the patching queue. The rest gets a suspicious glance, and a slow death in a backlog. That is the real cost of a false positive. It is quiet, it compounds, and it hollows the tool out from the inside. It is also the reason autonomous pentesting came to replace hypotheses with confirmed exploits.
What Counts as One AI Asset? Getting the Unit Right
Two teams inventory the same organization and return different numbers. One counts forty-one AI assets, the other counts one hundred and twelve. Neither is wrong, because they counted different things, and nobody had decided what a row represents. Guidance on building an AI inventory covers which fields a row should carry and skips what a row is. That question determines the count, the risk scores, the regulatory classification and whether two inventories can ever be reconciled.
AI Prompt Data Leakage: How to Secure Sensitive Data in LLMs
As generative AI adoption surges, so does a dangerous new enterprise risk: AI prompt data leakage — the unintentional exposure of confidential corporate data to third-party Large Language Models via user prompts. Why does it happen? Driven by productivity pressure and the need to speed up their work, employees routinely bypass traditional DLP controls.
Propagating User Identity From AI Agents to Your Tools: Amazon Bedrock AgentCore Gateway and JFrog Artifactory
Join us at swampUP New York, September 1-3, for our joint session Trusted AI Delivery at Scale: Securing Every Artifact from Curation to Cloud, where we walk the full chain of custody from the moment a package enters your organization to the moment your agent runs on Amazon Bedrock AgentCore. Register here. AI agents are becoming real users of internal systems. They open pull requests, run queries, and pull and publish artifacts in repositories like JFrog Artifactory.
AI Isn't Creating New Cyberattacks. It's Changing How They Operate
Artificial Intelligence has quickly become one of the most important conversations in cybersecurity. Much of that conversation focuses on what attackers might create next: AI-generated malware, deepfakes, autonomous attacks, or entirely new categories of threats. Those risks matter, but focusing only on new attack techniques misses a much larger transformation already taking place. The real impact of AI is not only what attackers can create. It is how efficiently they can operate.
Attribute-Based Access Control: How ABAC Works, Examples and Use Cases
Access control has become significantly more complex as enterprises adopt cloud platforms, AI applications, and distributed workforces. A user’s identity alone is no longer enough to determine whether they should access sensitive data. Factors such as device posture, data sensitivity, location, and business context all influence the right decision. This shift is driving widespread adoption of attribute-based access control, a model that evaluates multiple attributes before granting access.
What is AI DLP?
AI DLP (artificial intelligence data loss prevention) is a category of data protection that inspects the prompts and file uploads an employee sends to a generative AI tool, before that content reaches the model, which is what separates it from file-based or network-based DLP.
The Complete Re-evaluation of AI Security
The OpenAI model that escaped its testing environment and compromised Hugging Face exposed a much bigger cybersecurity problem: are our existing defences actually designed for autonomous AI attacks? In this Razorwire Raw, James Rees looks beyond the original incident at what happens when AI can identify vulnerabilities, exploit them and move through systems at a speed human defenders simply can't match. From AI security and sandboxing to defensive AI and the possible return of honeypots, it may be time to reconsider what defence in depth looks like in the age of AI.
How To Build An AI Risk Management Framework
Every AI approval a security team makes feels reasonable in isolation. A security architect signs off on a generative AI writing tool for marketing. An engineering lead spins up an agent to triage support tickets. A finance team connects a copilot to its planning software. Individually, none of these decisions looks risky.
From signals to systemic risk: Building Risk AI
Security and engineering teams contend with a constant stream of signals about vulnerabilities, incidents, misconfigurations, identity risks, control gaps, and other findings across their environments. But an individual finding’s severity does not always reflect its potential organizational impact.
Detect vulnerabilities in LLM applications with Datadog's AI-native SAST
AI coding tools help developers build and deploy LLM applications quickly, but this speed comes with new security risks. Traditional static application security testing (SAST) tools that are pattern based weren’t designed to detect LLM-specific issues such as prompt injection sinks and exposed system prompts. These vulnerabilities often don’t become apparent until applications are already running in production, when remediation is more difficult and expensive.
Decommissioning AI Agents: What to Look For in the Tooling
Gartner predicted in mid-2025 that more than forty percent of agentic AI projects would be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. Treat the figure as a forward-looking estimate rather than a measurement, since canceled projects tend to be quietly renamed, absorbed or left to lapse rather than formally closed.
The Structure of Sophos Fusion
Sophos Fusion isn't another platform. It's an AI-native cyber defense system that connects security controls, intelligence, data, services, and human expertise into one coordinated defense. Watch Raja Patel explain the foundation of Sophos Fusion and how it's built for the AI era.
Machine-Speed Credential Abuse: What the ChainDrop npm Worm Changes
ChainDrop hijacked 444 npm packages and 2B monthly downloads via a Claude Code hook. AI agents have collapsed the gap between credential theft and abuse.
Secure AI Written Code Before It Ships: Salt Code
AI coding assistants are transforming how enterprise software gets built. Developers at every level are prompting their way to production-ready APIs, MCP integrations, and agentic workflows faster than any security team can review them. The problem is that none of those assistants knows your internal security standards, regulatory obligations, or risk tolerance. The result is insecure patterns shipping unnoticed, vulnerabilities discovered downstream when fixes are costly, and compliance becoming a guessing game on every commit.
451 Research report: How agentic AI is redefining identity security
In the short time that AI agents have been a part of the enterprise, they have upended many of our bedrock assumptions about the nature of identity, access, development, and work itself. At 1Password, we’ve been in the trenches of the agentic revolution; we’ve seen its positive impact on productivity, and the serious concerns it raises about security.
The Rise of the 'Non-Human Insider': When AI Agents Become the Threat
For years, cybersecurity has had a familiar villain: the external attacker. The hacker breaking through the firewall, stealing credentials or exploiting an unpatched vulnerability. It is the scenario we have trained for, built defenses around and spent decades trying to prevent. But the next major breach may not begin with someone breaking into your environment at all, it may begin with an AI agent that already has access.
Report: One-Quarter of Breaches Are Enabled by AI-Driven Attacks
A new report commissioned by IBM has found that one in four breaches is now AI-enabled, up 56% from last year. “Most AI-driven attacks reported in the study targeted critical infrastructure sectors (62%), with financial services and energy organizations experiencing the highest concentration, raising the risk of broader systemic disruption,” the report says. “Financial services breaches were reported to cost on average $6.3 million, while energy breaches cost on average $5.2 million.
Give your agents capabilities
Accelerating security solutions for small businesses Tagore offers strategic services to small businesses. A partnership that can scale Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.
Agentic First Security -- Customer Brown Bag - August 20th, 2026
Join Jeremy Powell, CISO of Sumo Logic, to learn how AI-powered agents are reshaping modern security operations. Discover the key principles, governance, and best practices for building an agentic security program that enhances analyst productivity, accelerates threat response, and strengthens organizational resilience.
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Episode II: Attack of The Claudes
These weren't the test systems Anthropic were looking for.
2026 H1 Threat Review: AI, Ransomware & Emerging Cyber Threats
Cyber threats continue to evolve, and the first half of 2026 delivered no shortage of new challenges. Forescout Research – Vedere Labs' 2026 H1 Threat Review examines the latest trends in vulnerabilities, ransomware, threat actor activity, supply chain security, and AI-driven attacks. Discover the key findings, emerging attack patterns, and practical recommendations security teams can use to better understand and reduce risk.
AI Risk Management: Defining, Measuring, & Mitigating the Risks of AI
AI is merging into the modern workplace at roughly the pace computers did in the 1980s, and the risks are evolving just as fast. IBM and Ponemon found that 97% of organizations hit by an AI-related security incident lacked basic access controls, and 63% had no AI governance policy at all. In this video, Yakir breaks down the seven categories of AI risk every GRC leader needs to understand, and what separates knowing you have a control gap from knowing what it will cost you.
TITAN AI Demo Series: Driftnet Power-Filtering for Threat Hunters
Threat hunting often means following a lead without knowing where it will take you. Your search tools shouldn’t make you choose between exploring a hunch and losing your original results. Driftnet gives threat hunters multiple ways to refine artifact searches as they investigate. Use Add Filter to explore a narrower view without changing the underlying query, Add to Query to make that refinement persistent, or exclude artifacts to quickly zero in on what remains.
Remediation Agents, Demystified: Why Fixing Beats Finding
Six new security issues for every one issue remediated. That's the ratio Snyk research has found, and it's why the AI Security Engineers Community gave an hour of livestream time to fixing rather than finding. Play Video: Remediation Agents Demystified: Your AI Teammate for Fixing Security Bugs Remediation Agents Demystified paired a fireside chat with a live demo.
AI adoption and third-party risk implications: How to close the governance gap
Accelerating security solutions for small businesses Tagore offers strategic services to small businesses. A partnership that can scale Tagore prioritized finding a managed compliance partner with an established product, dedicated support team, and rapid release rate. Standing out from competitors Tagore's partnership with Vanta enhances its strategic focus and deepens client value, creating differentiation in a competitive market.
We Had 13 Engineers Spend Three Months Finding Vulnerabilities with LLMs
Blame for all flaws belongs to the flawed human author. Historically, the bottleneck for finding security bugs in software was human bandwidth. As pointed out in this great post by Tom Ptacek, it appears that large language models are exceptionally good at finding them with simple prompting. This adds substantial bandwidth to the effort of finding bugs.
Benchmaxxing: When the Benchmark Becomes the Target
Public benchmarks in AI provide important signals and allow for regression testing, directional validation of model updates, and public discussion of capabilities and limitations. But the more attention a benchmark receives, the stronger the incentive to optimize for it. Once a score becomes the goal, teams start benchmaxxing: optimizing for the benchmark rather than the capability it is meant to measure. This is a familiar problem in the AI space.
ChatGPT Security Risks for Enterprises: Real Incidents, Controls and Best Practices
Security teams often evaluate ChatGPT by examining its outputs. The greater risk, however, lies in the information employees submit before the model generates a single response. As generative AI becomes a big part of daily business operations, prompts increasingly contain confidential customer data, proprietary source code, legal documents, and strategic plans.
Threat Detection for AI for Enhanced Security Monitoring
Securonix has released a Value Package with OOTB policies for detecting threats across AI platform, identities, agents, data movement, endpoints, and network activity.
AI Governance in Financial Services: The Use Case Sets the Rules
An AI governance framework tells a financial institution to inventory its systems, assess risk and document decisions. Consumer protection law tells it something different and considerably harder, which is that a model unable to produce specific reasons for a credit denial cannot lawfully be used to make one. That distinction is what separates AI governance in financial services from AI governance generally.
Browser AI Events in the SOC: What to Send and What to Suppress
Browser-layer AI monitoring produces events, and the natural next step is forwarding them to the security operations center. Consider what they arrive into. Industry research for 2026 puts false positives at close to half of all alerts, with around forty-two percent going entirely uninvestigated. Browser AI events are behavioral anomaly alerts, and behavioral anomaly alerts are the category analysts already deprioritize, precisely because they are noisy by nature.
What is shadow AI?
Shadow AI is the use of generative AI tools by employees without the knowledge, approval or oversight of an organization's IT or security teams.
How to Choose a React Native Development Company for AI-Driven Mobile Projects
AI-driven mobile apps grow more complex every month. The gap between a team that can actually ship one and a team that merely claims they can is wider than most product managers expect. Wrong hires cost you four to six months of rework on top of the initial build, a brutal, avoidable tax. So if you're planning a mobile product that uses machine learning, on-device inference, or real-time AI features, vendor selection deserves far more rigor than skimming a portfolio and firing off a request for proposal.
The EU AI Act's Missing Standards: What to Do Before They Arrive
Organizations preparing for the EU AI Act keep asking which standard to certify against, and the honest answer is that the ones that will matter are not finished. No harmonized standard has been cited in the Official Journal, and nothing available today confers presumption of conformity with the Act's requirements for high-risk systems.
Multi-Agent AI Systems: When Separation of Duties Dissolves
Every enterprise control framework assumes the entity that requests an action and the entity that approves it are different. Multi-agent workflows quietly dissolve that assumption. Three agents each holding modest, individually reasonable permissions can compose an action none of them was authorized to take, and no single permission grant looks wrong in a review. That is the distinguishing property of multi-agent systems rather than a harder version of single-agent risk.
How to write an AI usage policy
To write an AI usage policy, define its scope, list approved and prohibited tools, name the data types banned from prompts, write clear rules, align to recognized frameworks and set enforcement. A written policy is the decision layer; pairing it with technical controls is what turns those decisions into protection.
Frontier AI Application Security: Every Second Counts
Somewhere in the last few months, the math of application security quietly broke. Anthropic’s Claude Mythos Preview didn’t just analyze code, it found a 27-year-old vulnerability in OpenBSD, a 16-year-old bug in FFmpeg, and a 17-year-old remote code execution flaw in FreeBSD, entirely on its own. Then it went further: it built working exploits for them. No human guidance. No months of manual research. And by Anthropic’s own account, this is only a preview of what’s coming.
Managing LLM Code Security at Scale with Hybrid SAST
The amount of code being generated in the era of AI is staggering, and some non-trivial percentage of that code is insecure. According to the 2026 GenAI Code Security Report, roughly 44% of AI generated code test produced a known vulnerability. Organizations are more reliant than ever on cybersecurity programs that can scale at the velocity of AI while still managing risk with guardrails, governance, and compliance standards.
How Businesses Can Adopt AI Tools Without Compromising Security
Someone in marketing starts using an AI writing tool. A finance team member feeds spreadsheets into an AI summariser because it saves an hour every Friday. A manager wires up a chatbot to handle basic customer questions. None of it goes anywhere near IT first, and most businesses only find out after the fact, if they find out at all.
OpenMatter Network to Take Verification Message to Belgrade Blockchain Week 2026
Head of Operations and Partnerships Chris Biele to lead sessions on secure scientific collaboration, agentic AI and the need to move from trust to cryptographic proof.
The risk of using abandoned packages in the age of LLMs
This post is an unfortunate affirmation of our prior research into abandoned open-source packages, where we found that 11% of the most-downloaded packages have been abandoned and not actively maintained, becoming invisible vulnerabilities to your scanner. Today we share a zip-slip vulnerability we found in extract-zip (CVE-2026-19693), an npm package with over 20 million weekly downloads.
After Mythos: How Security and Risk Teams Take Action and Prove Resilience
This is a practical session for SecOps, GRC, TPRM, and cyber risk teams on connecting exposure, supply chain risk, and business impact into a resilience story leadership can trust.
Managing AI Agent Identity at Scale: The Lifecycle Nobody Triggers
Gartner projects the average Fortune 500 organization will run more than one hundred fifty thousand agents by 2028, against fewer than fifteen in 2025. Thirteen percent of organizations believe their agent governance is adequate today. The management approach that works for fifteen agents is memory and a spreadsheet, and neither survives four orders of magnitude.
Real-Time AI Security Monitoring: Why One Assessment Expires
A penetration test on a web application stays broadly valid until someone changes the application. An assessment of an AI system starts expiring immediately, because the system changes without anyone at your organization touching it. The same prompt can return a different answer tomorrow, and the provider can revise the model underneath you without notice.
How to Connect WordPress to ChatGPT Using MCP Server | Secure MCP Server
The Secure MCP Server turns your WordPress website into an MCP (Model Context Protocol) server, allowing ChatGPT and other compatible AI clients to securely interact with WordPress. ���� �������� ����������, ������'���� ����������: • How to install the Secure MCP Server plugin on WordPress• How to configure the MCP Server• How to connect your WordPress website to ChatGPT• How to authorize ChatGPT to access WordPress• How to test the MCP connection• How ChatGPT can interact with your WordPress website through MCP• How to view and monitor audit logs of MCP activity.
Evaluating AI systems at Corelight
AI system evaluation is the process of continuously assessing AI system capabilities, limitations, and performance through quantitative and qualitative measures. Across the system lifecycle, evals provide continuous assurance: Validating system behavior before deployment and detecting drift, bias, and reliability issues in production.
How to stop sensitive data leaking into ChatGPT, Copilot and other GenAI tools
AI productivity tools are creating a prompt-level data leakage problem: 77% of employees paste data into generative AI tools, and 82% of that activity comes from unmanaged accounts, according to the LayerX Enterprise AI and SaaS Data Security Report 2025. Every paste into ChatGPT, Copilot or another GenAI tool is a potential exposure of sensitive data that traditional file-focused controls were never built to see.
Whos watching your AI A security leader panel on runtime visibility and accountability
AI is making decisions across your environment — calling APIs, accessing data, and taking action. Most organizations know it's happening, but far fewer can see it, let alone stop it. In this panel discussion, security leaders will share what they've learned deploying and governing AI workloads at scale on AWS, and what it takes to close the gap between deployment and accountability.
Episode 21 - Building AI Harnesses to Unify Detection and Response
Corelight Senior Security Engineer Jordan Hair joins Richard Bejtlich to break down how defense teams can leverage agentic AI harnesses to transform traditional security operations. By wrapping deterministic code around large language models, Hare created automated agents for alert triage, threat hunting, and detection engineering that shrink routine investigations from 45 minutes down to seconds.
American Cyber Mercenaries - The 443 Podcast - Episode 383
This week on the podcast, we discuss a new White House memorandum that creates a program to authorize American private companies to begin conducting offensive cyber operations. Before that, we discuss a vulnerability write up for a Citrix Netscaler flaw before covering yet another prompt injection vulnerability in a popular AI tool.
Solving the SOC Data Problem: How Modern SIEM Platforms Cut Noise Without Cutting Visibility
Security teams have a data problem, not a detection problem. Most SOCs today aren't short on logs - they're drowning in them. Every firewall, endpoint, identity provider, and cloud workload generates a steady stream of events, and somewhere inside that noise sits the handful of signals that actually matter. The challenge isn't collecting more data. It's finding the right data fast enough to act on it.
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In AI, No One Can Hear the Sandbox Scream
OpenAI was running a cyber-capability evaluation against advanced models, including GPT-5.6 Sol and a more capable pre-release model with reduced cyber refusals. The environment was meant to be constrained and the model still found brute forced through it.
AI Governance Tools and the Audit Trail Problem
An AI governance platform demo shows you the present. Compliance posture at seventy-nine percent, four controls needing attention, a register of systems with owners attached. Every figure describes today, and the demo is persuasive precisely because today is legible. An audit asks a different question.
Why AI Can't Replace Strategic Security Thinkers #cybersecurity #ai
Will AI replace cybersecurity jobs? AI already helps with repetitive work such as analysing logs and producing documentation, but information security still relies on judgement, strategic thinking and understanding risk.
OpenTelemetry and AI Governance: Where the Standard Stops
OpenTelemetry graduated from the Cloud Native Computing Foundation in May 2026, which formally settled a question the industry had answered informally years earlier. It is the standard way applications emit telemetry, second only to Kubernetes in contributor volume, and native across every major observability backend. A security and governance company has a specific reason to care.
Why Everyone Wants to Own AI Standards #AI #Tech
AI governance is becoming a regulatory race as standards bodies compete to define how organisations manage AI and AI agents. Growing compliance requirements risk creating duplicated frameworks, more noise and a commercial battle over which standard becomes dominant.
Inside the LiteLLM hack: 153GB, 433,909 Files, 2,488 Organizations
Attackers dumped everything they harvested from LiteLLM builds during a 40-minute window in March. Here is what is inside and what it says about where secrets live.
AI Security Policy in Practice: How to Define What AI Can and Cannot Do
Most organizations that try to write an AI security policy start with two lists. Approved tools and banned tools. But, that list is inevitably out of date within a month. Employees adopt AI features embedded in everyday software faster than any review board can evaluate them, and a blanket ban does not stop the behavior, instead it pushes people toward personal accounts and unmanaged services.
Uncovering Shadow AI Before It Becomes Your Biggest Risk | WatchGuard Technologies Webinar Series
Your employees have already adopted AI. The question is whether your organization knows where, how, and with which data. While many companies are encouraging AI innovation, a new security challenge is emerging in parallel: Shadow AI. Employees are connecting AI tools faster than security teams can evaluate them, moving sensitive data into unmanaged applications, and creating blind spots that traditional security controls were never designed to see. Even organizations with mature AI strategies are discovering that sanctioned AI is only part of the story.
Why AI Discovery Must Be the First Step in Enterprise AI Security
Every enterprise security leader is being asked the same question by their board: are we secure against AI risk? Most cannot answer it with confidence, and the reason is rarely a lack of tools. It is a lack of visibility. AI has spread through enterprises faster than almost any technology before it. Developers wire large language model APIs into internal tools. Business teams stand up copilots and chat assistants. Data science teams build retrieval pipelines against customer and financial data.
Secure all your internal vibe-coded applications - in one click
AI has enabled employees across every team to build applications faster than ever before. But that speed is also what's keeping every CISO up at night: any employee can build an application, deploy it to the public Internet, and accidentally expose internal work or company data. Today, we're launching new tools to make it easy to keep your applications hosted on Workers private.
How Cloudflare detects MCP traffic and helps secure it
Most companies designed their resource permissions with a human user in mind. A senior engineer may be able to deploy to production, query a sensitive database, or revoke another user's access. Those privileges come with risk, but that risk has traditionally been bounded by two assumptions: the engineer will use human judgment, and the engineer can only act at human speed.
Unmasking the Invisible: How to Identify AI Tools, Hidden Devices, and Other Unknown Assets on Your Network
You cannot protect what you do not know exists. That statement has been true throughout the history of cybersecurity, but it has become even more important as organizations adopt new technologies and employees gain access to powerful AI tools. While many organizations focus on defending against external threats, they often overlook a growing problem much closer to home: devices, applications, and services operating within their environment that nobody knows about.
A Hands-On Look at Photogenerator.ai: What Happened When I Tested This AI Photo Generator
I needed product shots and headshots fast. No studio. No budget for a photographer. So I opened Photogenerator.ai and spent several sessions testing it as an everyday user. This is what the experience actually felt like. No polished claims. Just notes from the process.
What an AI Compliance Audit Involves, Stage by Stage
An AI compliance audit is less mysterious than its absence from most planning suggests. Someone outside the organization reads what you wrote down, then samples real systems to test whether the organization does what the documents describe. The distance between those two things is where findings come from. Three different exercises get called an AI audit, and they run differently. Certification against a management standard follows a defined two-stage process.
OWASP LLM Top 10 2026: the model will be fooled, the question is what breaks
The OWASP GenAI Security Project published the 2026 edition of its Top 10 for LLM Applications. Prompt Injection stayed at number one. Sensitive Information Disclosure stayed at number two. Read the headlines and you would conclude that not much moved. Something did move, and it is not in the rankings. The project leads open by telling you to stop trying to build a model that cannot be fooled, and to build the system around it so that when the model is fooled, nothing important breaks.
Your AI Agents Are Using Your Credentials
AI agent security is an identity problem, but it often starts as a secrets and credential problem. Do your AI agents operate using static API keys, tokens, and other reusable credentials? They might bypass traditional identity controls, creating a governance blind spot.
Static credentials are still AI's easiest way in
Netwrix's 2026 research found a 4x gap in breach rates between organizations where AI has significantly grown their identity count and those where it hasn't. Static credentials are AI’s easiest way in: passwords, keys, and tokens that never expire and never get checked. AI didn't invent the over-privileged credential, it just found the fastest way to use one.
Shadow AI: The New Frontier of Shadow IT
As a CISO advisor, I am observing a familiar pattern gaining a new, critical dimension. What we historically identified as "Shadow IT", the use of unapproved SaaS and tools, is rapidly evolving into "Shadow AI." Employees are increasingly leveraging AI bots for drafting, analysis, code generation and strategic decision-making.
We built an AI PR reviewer. The hard part was teaching it to say nothing.
Most AI code review tools fail the same way. They work, in the sense that comments appear on the pull request. Then you read the comments and they are 80% “consider extracting this into a helper”, “missing test coverage”, “this variable name could be clearer”, and within a couple of weeks everyone has learned to scroll past anything the bot wrote. These are not false positives. They are true and irrelevant, which costs the same attention and is harder to argue with.
AI can think. AI can act. Secure the entire AI workflow.
Securing AI systems today means securing the whole AI process, from access, to prompt, to action. Explore A10's AI security offering, which ties the capabilities of AI Firewall, MCP discovery/protection, AI gateway, and ThreatX, all together into one cohesive end-to-end AI security solution.
A Prompt Is Not a Boundary: Lessons From the AI Eval Incidents
Three organizations had their production systems compromised by an AI model in April, and found out in late July when the model's developer called them. None of them had detected the activity. One was a security company whose own package scanner was the entry point. Anthropic published that account on July 30, nine days after OpenAI disclosed a related incident of its own.
Does Cyber Insurance Cover AI Incidents?
The answer changed on a specific date. Until the start of 2026, most organizations were covered for AI losses by silence rather than by grant, because policies neither affirmed nor excluded AI and the question would have been argued at claim time. On January 1, 2026 the standard forms organization introduced generative AI exclusion endorsements for commercial general liability, and carriers began attaching them at renewal.
Best AI security tools for small and mid-sized businesses in 2026
The best AI security tools for small and mid-sized businesses do more than detect risky AI use: they show which generative AI tools employees actually use, they let you govern which AI apps are allowed, monitored or blocked, they stop sensitive data from leaving in a prompt, and they defend against harmful prompts, including prompt injection. Most organizations now run AI without that visibility or control. AI use has moved into the mainstream.
The Agentic AI Security Adoption Matrix: Autonomy Scales Where Control Exists
Agentic AI is crossing a threshold. It is no longer just generating content or answering questions. It is beginning to plan, decide, and execute actions across tools, systems, and workflows. That shift unlocks real efficiency, but it also changes the security equation. When an AI system can act, it becomes part of your operational attack surface. It can be influenced, misdirected, or exploited. It can make mistakes at machine speed. And if it has permissions, it can create real impact.
How Cato AI Security Keeps Up With Claude
Claude is moving quickly. Cato is innovating alongside it. With inference hooks, skills posture, and seamless deployment, teams can adopt new AI capabilities with security controls that are ready from day one. Claude’s rapid innovation is reshaping what everyday employees can do with AI. It is no longer just helping people write faster or summarize information; it is becoming a hands-on work companion that can research, reason, build, and take action across business workflows.
Why Securing AI Agents Is More Critical Than Ever
AI agents offer unprecedented capabilities, speed, automation, deep context, and hyper-personalization, that will transform how we work. However, these same capabilities make AI agents significantly more dangerous than traditional software when hijacked by cybercriminals. You simply cannot rely on yesterday's risk management playbooks to handle today's AI-driven threats.
AI Can't Do CTEM Alone (And Neither Can You)
AI can meaningfully power Continuous Threat Exposure Management (CTEM), but only for specific stages of the cycle: prioritization, validation, and remediation routing. AI can’t replace the underlying data integration work, and it can’t turn CTEM into a single product, because Gartner defines CTEM as a continuous five-stage program (scoping, discovery, prioritization, validation, mobilization), not a tool you install.
AI Security Posture Management: What It Covers and What It Misses
AI Security Posture Management arrived as a term before it arrived as a definition. Vendors announced products under the label through 2025 and in volume at RSA Conference 2026, each describing a somewhat different scope, and buyers now evaluate a category whose boundaries depend on who is selling. The lineage is evident, since AI-SPM follows cloud and data security posture management, and the inherited assumptions are where the difficulty starts.
Ep. 73 - EU AI Act-What Actually Lands on August 2nd, and What Slipped to 2027
The EU AI Act's August 2nd, 2026 deadline just changed shape. Host Tova Dvorin and offensive security engineer Adrian Cully separate what actually lands—Article 50 transparency duties and GPAI enforcement powers—from the high-risk obligations that slipped to December 2027. Inside: Article 15 writes MITRE ATLAS and the OWASP LLM Top 10 into binding law, the DORA / NIS2 / AI Act overlap that makes one incident reportable three times, penalties up to 7% of global turnover, and the five things a CISO should do this week. Part 1 of 2.
Is your AI system secure enough? MITRE ATLAS Is Now Law.
For the first time anywhere, the MITRE ATLAS framework and the OWASP Top 10 for LLM applications are written into binding law. Article 15 names data poisoning, model poisoning, adversarial examples, model evasion and confidentiality attacks as threat classes you must have technical measures against—and must be able to evidence to a regulator. The question is no longer whether you have thought about AI security. It is whether you can prove your AI system holds.
Coding Agent Risk for CISOs: Blast Radius, Governance, and Where to Start
Claude Code, Cursor, GitHub Copilot, and Gemini CLI are running on developer machines across your enterprise right now. They're browsing the web, writing to your filesystem, committing code to your repositories, and calling external APIs under the identity of your engineers. Most security teams have no visibility into any of it. This isn't a future problem.
Black Hat Proved AI Agents Are Already the Attack Surface
Enterprise AI agents stopped being a pilot project a while ago. They read email, touch source code, operate browsers, and increasingly make decisions inside production systems, which means the security model built for chatbots and prompts no longer covers what is actually happening inside the enterprise. Black Hat USA 2026 turned out to be the week that gap became impossible to ignore.
The Agent Baseline: 35 controls, but where should you start?
Two weeks ago, we published the Agent Baseline alongside Docker and Keycard. In it, we describe six security outcomes, 35 controls, and an open reference architecture for running AI agents at the enterprise level. Last week, we stress-tested it: we took it to a panel at Black Hat and spent about one hour being asked hard questions about it. Play Video: Snyk x Docker x Keycard | Agent Baseline Panel @ Black Hat 2026 The most useful question came from someone who had actually already read it.
Smart AI Security. Full Data Control.
AI agents move fast. Your data security should too. See how Netskope gives AI the guardrails it needs to innovate at speed without putting your data at risk.
AI Agents & Cybersecurity: Why AI Agents Are New Trust Boundaries | A10 Networks
AI Agents & Cybersecurity: Why AI Agents Are New Trust Boundaries AI agents are more than just another virtual machine—they are autonomous entities delegated with power, introducing entirely new trust boundaries into modern network architectures. In this discussion, A10's Arjoyita Roy and Jamison Utter discuss why traditional security controls fall short when managing AI agents and why cybersecurity professionals must design new security perimeters and controls around these evolving AI workloads.
Top 10 AI Agents for Legal Research in 2026 (Case Law, Statutes and Drafting)
Legal research rewards the tool that shows its sources, not the one that sounds most confident. The right pick turns less on brand than on whether your priority is model choice, case-law depth, or deployment security. The wrong platform hands you a fabricated citation, a confidential contract fed to a model that trains on it, or a bill for the wrong vendor's LLM. This guide ranks the ten agents US legal teams are shortlisting in 2026.
How Can Scrum Masters Integrate AI Tools Effectively Into Daily Work?
Scrum Masters help teams optimize workflow and improve product value. But with frequent business demands, priority shifts, and the rapid adoption of AI by companies, Scrum professionals need to integrate AI tools into their daily work to help enhance their team's productivity to the maximum. When you start your SSM certification journey with organizations like Simpliaxis, you can learn how to incorporate AI into the Scaled Agile Framework and lead Agile teams by becoming a certified SAFe Scrum Master licensed under Scaled Agile.
AI Pentesting in Action: Astra's Autonomous Platform Demo
AI Led Pentesting is redefining how organizations approach application security. As software development accelerates with AI-assisted coding, cloud-native applications, and rapidly evolving attack surfaces, traditional penetration testing is struggling to keep pace. In this detailed video, we explore why the future of security testing needs to be continuous, intelligent, and autonomous led by AI.
EU AI Act: risk tiers, timeline, and compliance requirements
The EU AI Act is a regulatory framework governing the development, deployment, and use of artificial intelligence within the European Union. The European Commission proposed it to keep AI systems placed on the EU market safe and transparent and to make sure they respect fundamental rights. The Act sets obligations for AI providers, users, and other stakeholders to address risks associated with AI while still supporting innovation and investment in the sector.
What Data Should Employees Keep Out of AI Tools?
Paying for a subscription does not automatically prevent the provider from storing company information or using it to train its models.
Verified loops: Building AI agent trust and accountability
AI agents have crossed an important line from making suggestions to taking actions. They can read a repository, call internal systems, change code, open a pull request, and keep working while the human moves on.
Kroll Perspectives: AI-Accelerated Cyber Risk Management
Has your organization been exposed? How can you act on the exposures that matter most? What’s the best way to demonstrate measurable risk reduction?
Reporting AI Risk to the Board: What Directors Want to See
Directors ask for AI risk reporting because oversight failure is personally actionable. Under the Caremark line of cases, a board that cannot demonstrate it monitored a material risk carries exposure of its own, and AI has moved into that category for most enterprises. The request is rarely curiosity about the technology. The framing determines what belongs in the pack.
NIST AI RMF vs ISO 42001: Choosing Your AI Governance Framework
NIST AI RMF and ISO/IEC 42001 answer different questions, so the choice is rarely about which one is better. One gives you a risk process your engineering teams can run. The other gives you a management system an auditor can certify. Organizations that treat them as rival options usually pick the wrong one for the problem in front of them.
Fix It Once with ScreenMeet - Scale It Everywhere with Tanium Atlas
See how a ScreenMeet session's AI summary lets Tanium Atlas turn a one-off fix into a safe, fleet-wide deployment.
The Invisible Expansion of the Attack Surface: Shadow AI, MCP, and Third-Party Risk
AI adoption is moving faster than most of us anticipated and, more importantly, faster than most organizations can govern it. Organizations are implementing the use of AI-enabled applications, browser extensions, coding assistants, and automated agents to enable employees to work faster. In many cases, these tools are adopted without security review, procurement approval, or a clear understanding of where organizational data is being sent.
From standard sales user to AWS root in 5 minutes: An AigentX Case Study - Agentic AI Penetration Testing
A recent grey-box Salesforce assessment began with a low-privileged standard user account. From that starting point, AigentX mapped native Salesforce APIs and custom objects, identified cloud credentials exposed through misconfigured Field-Level Security, and demonstrated a path beyond Salesforce into the organization’s connected cloud infrastructure.
As AI Comes for Your Data, Is Your Recovery Ready? Cyber Resilience in the AI Era
AI is already writing code, connecting SaaS platforms, and interacting with sensitive business data. In this podcast, 11:11 Systems explores what organizations must do to prepare for the risks AI can introduce across data security, recovery, and cyber resilience. Watch Jim Jones, Sr.
Navigating the AI Revolution: The Real-World Impact of AI on IT Operations
Artificial intelligence is reshaping the IT landscape at an unprecedented speed. In this podcast, 11:11 Systems cuts through the noise to explore the real-world impact of AI on IT operations.
Ready for AI? Why the Right Infrastructure Is the Secret to Success
Artificial intelligence is transforming the IT landscape, but many organizations still face a major infrastructure gap. In this podcast, 11:11 Systems explores why the right foundation is critical for building secure, reliable, and scalable AI strategies.
Networking: The Last Piece in the AI Puzzle
Artificial intelligence is revolutionizing the way we work, but it can also consume massive amounts of network resources. In this podcast, 11:11 Systems explores the critical networking foundation required to support AI workloads with the low latency, high bandwidth, and operational visibility they demand.
It Was Hard Before AI. It's Harder Now.
Security used to mean locking the doors and the windows. Networks are open now, and every new tool, integration, and workflow adds another way in. A device can report EDR installed and still carry an exclusion someone added on a Sunday two years ago. Deployment is easy to confirm. How the control is running is the harder question. Garrett Hamilton and Moudy Elbayadi get into it on Inflection Point: Digital Intelligence.
Falcon AIDR: Complete Endpoint AI Coverage
AI has moved beyond the browser. It now runs in the terminal, the IDE, and desktop apps with your users' full privileges, where most security tools can't see it. In the AI era, the endpoint is where AI executes. This video shows how CrowdStrike Falcon AI Detection and Response (AIDR) extends the Falcon sensor you already run to the AI interaction layer. One sensor and one browser extension deliver visibility and control over every AI interaction on the endpoint and in the browser, from the Falcon console you already use.
LLM Prompt Security Best Practices
An employee pastes a customer contract into ChatGPT to summarize it. Nothing gets attached, nothing crosses the network in a file, and no alert fires. That is the gap most LLM security advice does not address. Prompt security is not the same problem as prompt injection or model hardening. It is a data problem consisting of what enters a prompt, what an agent does with it, and what comes back out.
Building an AI-Driven Security & Healthcare Enterprise in India | Anirban Mukherji | Uttoron 2026
Our Founder & CEO Mr. Anirban Mukherji delivered an insightful session at Uttoron 2026, the Annual Business Conclave, sharing his personal and professional journey from a small team to leading a homegrown technology firm focused on identity security, privacy, and AI solutions tailored for India.
Defeating the Agentic Attacker: Agentic Threat Prevention in Action
Agentic attacks are changing the speed and scale of the defensive challenge. This post explains the challenge, Cato’s new purpose-built capability for addressing it, and what we observed in an end-to-end demonstration.
How AI Governance Reduces Security Risks
AI governance reduces security risk by enforcing least-privilege access, protecting the data and credentials AI systems handle and making every AI action auditable. This matters because most organizations deploy AI faster than they can govern it. Employees adopt unsanctioned tools, and autonomous AI agents are created under existing user identities. Each one adds unmonitored machine identities that expand your attack surface – the exact gap that governance closes.
Episode II: Attack of The Claudes
Two weeks after Hugging Face disclosed an AI-driven intrusion into part of its production infrastructure, Anthropic said it had found three incidents from its own cyber evaluations. Hugging Face traced its compromise through a data-processing pipeline, where a malicious dataset abused a remote-code dataset loader and a template-injection issue in a dataset configuration.
What Mythos Means for Your Vulnerability Management Team
Modern exposure management has evolved beyond vulnerability scanning and alert volume into a discipline focused on measurable risk reduction. As the exposure management market matures, security leaders are adopting cyber exposure management platforms that unify signals across vulnerability, cloud, application, and attack surface tools to prioritize what truly matters.
Show, Don't Tell: What Evo Continuous Offensive Security Found in a Real Enterprise SaaS
Autonomous AI attacks have definitively moved from the research demos everyone's been awed by to standard operating procedure. For anyone paying enough attention, this is not necessarily news: the Five Eyes Alliance warned everyone back in June that AI will bypass cybersecurity in months, not years, with adversary breakout times that can now be measured in seconds. Gartner itself predicted something similar, expecting the window to exploitation to be cut in half as early as next year.
Mythos: When Al becomes the attacker, the network becomes the first line of defense.
Cyber defense in the age of Mythos Advanced AI has fundamentally shifted the security landscape, shrinking the window for vulnerability exploitation from weeks to hours. When standard patching workflows can't keep pace, your network becomes your most critical line of defense. In this video, we explore how Corelight transforms network traffic into actionable security insights to power your SOC. The best data drives the best defense. Discover how to improve your SOC outcomes by up to 300% over legacy data.
EU AI Act Compliance Roadmap: What Enterprises Must Document and When
The EU AI Act reached a turning point this summer, and the headlines got it half right. Obligations for high-risk AI systems were postponed to December 2027 under the Digital Omnibus, adopted in June 2026. The transparency rules under Article 50 were not postponed, and they apply from August 2, 2026. Enterprises reading spring 2026 guidance are working from a timeline that no longer exists, and enterprises reading the headline about a delay may believe nothing is due.
The AI notetaker you can't see in the participant list
For about three years, the governance question around AI meeting assistants had a convenient property: you could see them. The tool joined the call as a named participant. It appeared in the attendee list. Everyone in the meeting had at least the theoretical opportunity to object, and a security team reviewing an incident could reconstruct which meetings had been recorded by looking at who else was in them.
How Regulated Data Leaks Through AI, One Paste at a Time
A support coordinator has a difficult letter to write. The customer record is open in one tab, a consumer AI assistant in another, and the deadline is this afternoon. She selects the record, copies it, pastes it into the prompt box, and asks for a polite draft. Thirty seconds later she has a good letter and a regulatory problem, and nobody in the organization knows about either. The sequence below traces that single action through to its consequences.
Paul Maritz: Partner Cloud in the AI era | Acronis Accelerate Event
How is the AI era creating new opportunities for service providers through partner cloud? In this keynote from Acronis Accelerate, technology investor, former Acronis Chairman, and former VMware CEO Paul Maritz explores how service providers can turn local infrastructure into a cost advantage and competitive edge as customers increasingly require partner infrastructure for compliance and data sovereignty. Learn how Acronis Cyber Frame, built specifically for service providers, is a natively integrated solution that enables SPs to deliver infrastructure services (VMs, storage, networking) with integrated backup, DR, security and RMM.
Evolving AI maturity for our user experience team
AI is changing how products get made. For user experience teams, that means the very shape of the work is changing.
Your AI deployment might be out of policy
Most AI deployment policies stop at approved chat interfaces. Meanwhile, employees install browser copilots, AI extensions, and third-party plugins that never touch Microsoft's management stack. IT can't configure what it can't see, and Group Policy and Intune only govern Microsoft's world. This post covers what actually happens once AI tools show up outside policy, five things most teams miss, and how PolicyPak enforces controls directly on the apps and browser extensions themselves.
AI Is Changing Cyberattacks on Hotels: Here's How to Stay Protected
Peak season brings challenges to the hospitality industry every year. Thousands of guests, temporary staff, vendors, and business partners interact daily with reservation systems, management platforms, mobile apps, and loyalty programs. That operational complexity makes hotels a particularly attractive target for cybercriminals. Artificial intelligence hasn't created a new problem for hotels, it is simply accelerating an existing one: identity-based attacks.
How AI Is Accelerating Adversary Activity | Adam Meyers on Yahoo Finance
AI is changing the threat landscape and the pace defenders have to keep up with. Adam Meyers, Head of Counter Adversary Operations at CrowdStrike, joined Yahoo Finance to unpack key findings from the latest CrowdStrike Threat Hunting Report, including: Watch the full interview for Adam’s take on how AI is reshaping adversary activity and what defenders need to know.
How the model broke out of its lab and hacked Hugging Face
An AI model found a way out of its controlled environment by exploiting a third-party vulnerability and gaining internet access.
The AI-Native Platform for the Autonomous MSP | JJ Jager and Yury Averkiev | Acronis Accelerate
What does it take to run an AI-native MSP? In this keynote from “Acronis Accelerate: The Journey to Autonomous IT,” Acronis CEO JJ Jager and Chief Product Officer Yury Averkiev showcase how the Acronis platform turns the foundation of the AI era into a daily reality for MSPs. See how Acronis simplifies the complexity of managing multiple tools with a flexible, unified platform that supports both Acronis and third-party services, while enabling no-code and low-code automation built for modern service providers with AI built in from the start, not bolted on.
The AI Opportunity with Acronis: Building Autonomous IT | Rob Rae, Pax8 | Acronis Accelerate
The industry is shifting and Rob Rae, Corporate VP of Community and Partner Experience at Pax, addresses the $700 billion opportunity MSPs have in front of them at “Acronis Accelerate: The Journey to Autonomous IT.” Discover how Pax8 and Acronis are building the future of AI together, giving small and mid-sized businesses the tools, technology, expertise and AI agents they need to offer enterprise-level services.
Is an AI SOC Better Than MDR? What Security Teams Should Weigh
Security teams are expected to investigate more alerts than they have people to handle. IBM's 2025 Cost of a Data Breach Report puts a number on what that gap costs: organizations take an average of 158 days to identify a breach, and a further 83 days to contain it. That is 241 days of exposure, the fastest pace in nine years, and still measured in months. For most SOC teams, the bottleneck was never finding threats. It was having enough people to do anything about them.
Sophos Announces Partnership with OpenAI to Bring Frontier AI to the Channel
A dedicated channel partnership brings frontier AI to the channel and service providers through Sophos Fusion, powering new security services partners can build on.
From Connected Project Data to Construction Intelligence: Building the Foundation for AI-Powered Construction
Construction firms have invested heavily in technology to connect project information. Drawings, specifications, RFIs, submittals, BIM models, photos, and field reports are increasingly accessible from anywhere, helping office and field teams work from the same information. Connecting project information is a critical first step. It improves collaboration, reduces rework, and helps office and field teams work from the same information.
Open-source AI Needs Security: Why Cyberhaven Is Joining the Open Secure AI Alliance
Today, Cyberhaven is joining the Open Secure AI Alliance to help advance a future in which enterprises can adopt open-source AI without compromising security, compliance, or control over their data. Cyberhaven is betting on a world where companies are free to choose from many models, agent frameworks, and open harnesses, as well as run them on infrastructure they control. That choice matters. Enterprises will not standardize on a single model or AI platform.
The First Place to Start Securing AI
Every enterprise is racing to adopt AI, and every security team is racing to keep up without becoming the department that says no. Most are still evaluating tooling built specifically for AI agents. But there's a faster, simpler starting point available today: the large majority of AI activity right now runs on behalf of a signed-in user, what we call “on-behalf-of” (OBO) access. Secure that identity well, and you've secured the AI acting through it.
Securing the Agent Supply Chain
A developer installs a skill to make their coding agent less chatty. It works. It also, the first time the agent uses it, reads the AWS credentials on that laptop and sends them to a domain no one recognizes. No one wrote obviously malicious code and no one approved a change. A file landed in a folder, the agent loaded it on the next run, and production credentials were gone.
Top 17 Agentic AI Security Solutions
Agentic AI security solutions help teams discover, govern, monitor, and control AI agents, copilots, LLM apps, MCP servers, and autonomous workflows. For security and DevOps leaders, they matter because agents can act across production systems. This guide compares leading tools and explains how to choose the right fit. AI agents are moving from assistants to actors.
Who's Accountable When an AI Agent Makes the Wrong Call?
On a Tuesday morning in Q3, a procurement agent at a mid-market manufacturer approved a $340,000 payment to a vendor account. The vendor name matched the approved-vendor list. The invoice format matched the standard template. The agent verified both, cross-checked the amount against historical purchase orders, and released the payment through the treasury API within eleven minutes of the invoice arriving. No human touched the transaction.
What is AI harness engineering?
Harness engineering is the practice of building the layer, including code, that turns an AI model from a text generator into an agent that can take actions. In short, an AI agent is a model plus a harness. The model decides what to do next, and the harness makes it happen, connecting the model to tools, context, external systems, and validation. In a lot of practical work, and especially in security work, the harness decides the quality of the output more than the choice of model does.
How to Secure Agentic Coding Tools: Cursor and Claude Code
Cursor and Claude Code now read source code, install packages, and push commits with much of the access a senior engineer has, and often with less oversight. Give an agent a prompt to fix a bug, and it may pull a private API key from a config file, pass a customer record into its context window, or send a snippet of proprietary logic to a third-party model provider to reason about the fix. Security teams built policy for developers typing code by hand.
Assessing Third-Party AI Vendor Risk Before It Becomes a Problem
Every SaaS tool your organization onboards now carries a hidden layer of AI risk. The chatbot on your CRM, the transcription service your sales team runs, the code assistant embedded in your IDE. Each one processes company data through models you did not build, in ways your vendor questionnaire was not written to catch. Traditional third-party risk management was designed to evaluate infrastructure, access controls, and data handling.
How agentic AI works inside your tools: A practical example with Acronis Service Desk
Author: Alexander Ivanyuk, Senior Director, Technology For many MSPs, AI still looks like an extra tool outside the real workflow: open a chatbot, paste in a ticket, ask for help, copy the answer back and continue working. It may save a few minutes, but it also creates more steps and more places where context can be lost.
Monitoring AI Agent Behavior in Production
Monitoring AI agents in production is a fundamentally different problem from monitoring traditional software or even generative AI models. Because agents run autonomously, chain multi-step reasoning across tools and systems, and change behavior as their underlying models evolve, standard software metrics like uptime and CPU utilization miss almost everything that matters.
Cloudflare AI Search: give your agents a search engine for your data
Today, we’re excited to announce a few developer experience improvements to Cloudflare AI Search to make it easy to manage a search solution out of the box. Previously, you had to stitch together components of the Cloudflare primitives (Workers AI, AI Gateway, Vectorize, R2, Browser Run) but now, AI Search can do this automatically, and better. Our goal is to give your agents their own search engine, where they can easily find data to provide better answers for themselves and their humans.
How to Build a Durable AI Governance Program: A 3-Pillar Framework
AI adoption inside the enterprise has outpaced the governance built to contain it — 57% of employees have used AI tools for work without telling their manager. Policies get written and committees get formed, but exposure keeps accumulating, because data governance, AI oversight, and security are almost always run as three separate programs. In this video, Kovrr breaks down the three pillars that need to connect, and what separates a durable AI governance program from a documented one.
Agentic Development Security is a Discipline that Starts Before the First Line of Code
Ask most security tools what an AI coding agent just built, and they can tell you. Ask what it was allowed to consume before it started, and far fewer have an answer. That gap, between watching agentic development and controlling it, is what securing it actually comes down to. Securing agentic development means stopping risk before it enters a build, not flagging it after. And risk prevention has a prerequisite most approaches skip: you can only account for the assets you actually hold and manage.
Guide to Agentic AI Governance
Agentic AI governance is about keeping powerful, autonomous AI systems aligned, safe, and accountable as they act on our behalf. It’s now a certainty that AI agents will be deployed enterprise-wide. So, we need to look more deeply into those agents, figure out where they are, how to find them, and fully understand what they are doing in deployment so we can prevent attacks. The most dangerous agentic attacks will not look like attacks at the layer where they originate.
How to survive the AI spend hangover
It's 6:30am and you hear the door of the nightclub you've spent the last 8 hours inside shriek as it closes behind you. You watch bleary-eyed as an overly bright sunrise illuminates the business-suited people as they glide effortlessly along the sidewalk, their obnoxiously well-rested faces talking about work on their fully charged phones. You wonder, "Where did all the fun people go? And what happened to my wallet?".
Off-by-1 Labs: Why AI-generated vulnerability patches still require expert human review
We studied what happens when Large Language Models (LLMs) generate vulnerability patches for recently disclosed, complex vulnerabilities. Our data shows that LLMs produce Fix-Like Artifacts with Embedded Defects (FLAWED) 53.9% of the time when complex patches are required.
Remove standing access before AI agents exploit it
AI has changed the calculus of a credential attack. Before, finding and exploiting credentials in an enterprise environment required time, patience, and human judgment. An attacker had to decide which accounts were worth testing and which systems were worth reaching. Many credentials never made the list.
AI Did Not Invent Social Engineering But It Did Industrialize It.
This year National Social Engineering Day falls on Aug. 6. This day is designed to give us an opportunity to remind people that cybercriminals do not always need sophisticated malware, an undisclosed vulnerability or a dark room filled with glowing monitors, sometimes, all they need is a good story. Social engineering existed long before computers. Confidence tricks, impersonation, false authority and appeals to greed or fear have been used for centuries.
New Research: The Confidence Gap Between CISOs and Their Boards Is Real, and It's Measurable
Pulse Security AI calls for boards and security leaders to define cyber risk appetite in new report: The CISO-Board Communication Gap.
How to Present AI Risks to the Board of Directors
A board meeting agenda gives the CISO ten minutes to talk about AI. Walking in with a shadow AI tool count or a list of blocked prompts does not answer the question directors probably have: what happens if this goes wrong, and who is accountable when it does? Boards increasingly carry direct exposure for AI oversight failures, from regulatory scrutiny to shareholder litigation.
The Identity Surface You're Not Watching: Three Layers of Coding Agent Risk
There's a widespread assumption in enterprise security that identity is a problem IAM programs know how to solve. Provision the right access, enforce least privilege, audit the credential chain, and you've addressed the identity risk. For human users and traditional service accounts, that's approximately correct. For coding agents, it misses two-thirds of the problem. Coding agents don't have a single identity. They operate across a layered identity surface, and each layer carries its own risk profile.
Defense at Machine Speed: How Arctic Wolf Built the Aurora Agentic SOC on AWS
Avni Wala, Principal Developer – Arctic Wolf Laura Ellis, SVP Artificial Intelligence – Arctic Wolf Merin Eralil, Security Partner Solutions Architect – AWS Tim Sitze, Solutions Architect – AWS AI didn’t just make defenders faster. It made attackers faster too. The moment both sides got access to the same speed, speed stopped being the advantage. With speed no longer separating attackers from defenders, the deciding factor moved somewhere else.
How to Connect Claude to Jira Securely Without Giving AI Unrestricted Access
Teams are connecting Claude to Jira to summarize issues, draft tickets, and answer sprint questions in seconds. The productivity gains are real, but so is the security risk. The problem is simple: a direct connection gives Claude the same permissions as the person who set it up. If that user can view confidential projects or delete issues, so can Claude. There's no business logic in between deciding what AI should and shouldn't touch. Most organizations don't want to ban AI.
Ep. 72 - The File That Lies: One CLAUDE.md Walks Off With Your Agent's Credentials
A poisoned CLAUDE.md file inside a cloned repository quietly tells a coding agent to send its test logs to an outside endpoint, and the agent complies, shipping environment details, internal system information, and API keys to a server the developer never controlled. The model was not broken. It was obedient. In this episode of The Cyber Resilience Brief (a SafeBreach podcast), host Tova Dvorin and SafeBreach senior sales engineer Adrian Culley break down why building agentic AI controls is not the same as proving they hold under attack.
MCP Security Risks: Trusting Tool Descriptions Without Standards
Are we trusting AI tools too much? Right now, agents trust tool descriptions without verification. This could lead to serious security risks! What happens when a major server gets compromised? It's time to rethink our standards for AI tool security. What do you think about AI trust issues?
The AI Inventory Problem Nobody Solved
By now, most organizations have invested in AI governance. Far fewer have solved the problem that makes governance possible in the first place: knowing what AI they are actually running — and with 57% of employees using AI tools at work without telling their manager, the gap is wider than most inventories admit. In this video, Kovrr breaks down what an AI asset inventory actually is, why traditional asset management never catches shadow AI, and what it takes to keep the record accurate.
Securing Agentic AI Workflows in n8n: From Leaked API Keys to Encryption Key Compromise
A leaked n8n API key is only the start. GitGuardian's research traces the full chain, from exposed tokens and weak keys to CVE-2026-25053 and the N8N_ENCRYPTION_KEY that protects every stored credential, then lays out a hardened configuration to break it.
SwyftComply AI: How We Turn Vulnerability Flood Into Audit-Ready Protection
For the last several months, we have run AI agents against real production applications across industries. Two questions drove the work: what does AI-powered vulnerability analysis and pentesting surface at scale, and what does protection have to look like to keep pace. The vulnerability discovery side confirmed what Mythos made impossible to ignore.
Productiv shutdown: Switch to 1Password for durable AI and SaaS Management
On August 2, 2026, Productiv told customers its SaaS management platform was shutting down on August 6, with account data deleted once access ended. Four days is not much time to pull years of app inventory, spend, and usage data out of a system you've come to depend on, especially with AI tools now adding a fast-moving new layer of spend and access to track on top of everything else.
Agentic AI: From Model Risk to Enterprise Risk
The organizations that thrive in 2030 will be those that treat governance as the foundational architecture for innovations in autonomy.
Continuous Offensive Security & AI Pentesting: 20 FAQs
Applications can change several times between scheduled security assessments. New features, APIs, and integrations may introduce risk long before the next annual penetration test begins. That gap is pushing offensive testing beyond a single tool or a single point-in-time engagement. Teams are increasingly combining Dynamic Application Security Testing (DAST), AI penetration testing, and AI red teaming to evaluate different layers of application risk.
Shadow AI Is the New Shadow IT: Getting Visibility Into the Models Your Teams Already Use
Security teams spent a decade wrestling shadow IT - the unsanctioned SaaS accounts, personal Dropboxes, and rogue cloud instances that employees adopted faster than governance could follow. That battle produced hard-won playbooks: discover, broker, monitor. Now the same movie is replaying with AI, at higher speed and with higher stakes. Developers embed model APIs into services over a lunch break, marketing teams paste customer data into chatbots, and product features quietly ship with third-party inference behind them. Most organizations today cannot answer a basic question.
6 Administrative Tasks Every Personal Injury Law Firm Can Automate With AI in 2026
Personal injury law firms spend a significant amount of time on administrative work that supports-but does not replace-legal expertise. Reviewing medical records, preparing demand letters, drafting litigation documents, organizing case files, summarizing evidence, and communicating with clients are all essential parts of managing a case, yet they can consume valuable hours every week.
Airlock Digital Unveils Agentic AI Control & Governance to Extend Preventative Endpoint Security
Airlock Digital announces Agentic AI Control & Governance, extending its preventative endpoint security solution with visibility into trusted AI agent behavior and governance over what trusted agents are allowed to do on endpoints.
Mallory Unifies Threat Intelligence, Exposure Context, and Response Into One Architecture for Security Teams
As AI-assisted attackers compress exploitation timelines to hours, Mallory turns live adversary intelligence into prioritized, policy-governed action across the tools security teams already run.
Securonix Takes on Data Costs, Detection Gaps, and AI Agent Risk at Black Hat USA 2026
New capabilities help organisations control security data costs, strengthen Microsoft Sentinel detection coverage and response, and govern enterprise AI agent activity.
How CFOs can manage AI costs and prove business value
Earlier this year, a bill arrived from one of 1Password’s AI vendors for 5x the value of the original contract. The initial agreement came in below a certain threshold, so it never reached the right approvers for review. By the time it did, we had a much clearer understanding of how quickly AI costs can add up.
Zenity Now Integrates with Microsoft Agent 365
AI agents have moved from pilots into broad enterprise use. They read email, query systems of record, take actions, invoke tools, and coordinate with other agents on behalf of employees. Every line of business wants more of them, and security teams are being asked to enable that expansion without losing visibility or control.
Salt Debuts First AWS WAF Managed Ruleset for AI Agent and API Protection
Your WAF is doing its job. It's blocking SQLi, XSS, and the usual suspects. But here's the problem: it wasn't built for APIs, and it definitely wasn't built for AI agents. APIs now power nearly every digital experience. And AI agents — the automated systems that access your APIs at machine speed, at machine scale — are the fastest-growing source of that traffic.
Automate your GRC program with the Vanta Agent
Your compliance program never sits still. Controls drift, tests fail, policies go out of date. The Vanta Agent keeps up. It has full context on your program through Vanta's Trust Graph, so it never hits a dead end. It always recommends the next step. The Trust Graph is Vanta's data and intelligence layer, powered by 400+ integrations that map your risks, controls, policies, and vendors. Add continuous monitoring, risk scoring, automated testing, and framework mapping, and the Agent works with the full picture.
The Control Gap: Why Cyber Defenses Are Falling Behind AI-Powered Threats
Cybersecurity teams have faced major shifts before—from advanced persistent threats to ransomware. Now, Frontier AI is accelerating vulnerability discovery and creating new challenges for defenders. In this episode of Let's Talk Security, Forescout CEO Barry Mainz sits down with cybersecurity evangelist and former practitioner Karsten Abata to discuss the concept of the "Control Gap"—the time between identifying a risk and having the controls in place to effectively contain it.
The AI agent working for you probably has more access than you do
Say a sales rep uses an AI assistant to help manage their pipeline. The rep has role-based access to Salesforce, scoped to their territory and their accounts. The assistant, wired in through an API integration, often doesn't have that same scoping. It authenticates as a service account with broad read and write access across the org, because that was faster to set up than a permission model that matches what the actual user is allowed to see.
HuggingFace's List of Demands
A rogue AI model. Keyboard sounds turned into text. Hotel Wi-Fi redirected by attackers. Corey and Marc break down what these threats could mean for security teams.
Secure Agent Harness Execution: Preventing Escape
At CrowdStrike, we conduct extensive red-team testing of agentic systems using diverse models, tools, and adversarial evaluation harnesses designed to probe for containment failures. To date, none of our offensive agents have escaped their intended sandbox boundaries.
4 Questions every CISO needs to answer about AI
If your board asked today how you are governing AI, how would you respond? Not just the policy you wrote, but what is actually happening across the business. Could you answer with evidence? Many CISOs cannot answer with certainty. AI has entered the business faster than anyone could write policy for it, and securing it across all areas now seems to be the CISO’s responsibility.
Agent Risk Manager Moves into Early Access
When we first introduced Agent Risk Manager, the response was clear: many security teams are actively looking for a way to secure the AI agents already running in their environment and how they can confidently adopt AI across their organization. AI agents now operate inside organizations with real access to email, files and business systems, often with little visibility for the teams responsible for securing them. That’s exactly the problem Agent Risk Manager was built to solve.
From Data Classification to Runtime Data Security for AI
Authentication used to be a login form. Then it became IAM: identity providers, roles, federation, lifecycle. Then it became Zero Trust: no built-in trust, every request checked in context. Each step did not replace the last so much as fold it into a bigger runtime decision. The login still happens, but it is now one input to a constant, context-based check.
AI Model Risk Intelligence Know Which Models You Can Trust Before You Deploy
When we started thinking about how to surface AI model risk inside Evo, the obvious answer was to borrow from how we score everything else: find the issue, assign a severity, surface it. Done. The core of the new approach is a real risk score, built the way security teams already reason about risk: Likelihood × Impact. Likelihood comes from Attack Success Rate (ASR), the share of real adversarial attacks that succeed against a model. Impact is how much damage the attacker's goal does when it lands.
A First Look at Evo Agentic AppSec: Agentic Remediation and Malicious Code Defense
The Remediation Agent and Malicious Code Defense are the first two pieces of Evo Agentic AppSec: security that not only surfaces risk, but resolves it and prevents the next ones. This morning, we announced the broadest expansion of the Snyk AI Security Platform to date: discover, remediate, validate, and prevent. A loop with a missing segment is not a loop; it is a gap that an autonomous attacker will occupy. Evo Continuous Offensive Security closes validation and shipped today.
The Illusion of AI Containment: Why AI Guardrails Won't Save Your Supply Chain
AI is quickly becoming one of the most useful tools available to security researchers. Its ability to analyze enormous volumes of data, identify vulnerabilities, reconstruct attacks, connect seemingly unrelated signals, and help defenders respond faster than humans could alone is incredibly beneficial.
AI Agent Sprawl and How Enterprises Are Controlling It
AI agent sprawl is the uncontrolled proliferation of AI agents, autonomous assistants, and LLM-powered tools across an organization without centralized tracking or governance. It mirrors historical IT challenges like SaaS sprawl and shadow IT, and it emerges when decentralized business units build or deploy agents independently, without coordinated oversight from security, IT, or risk teams. The difference is that these agents are active software actors.
AI Agent Governance: How Enterprises Should Approach It
Governing AI agents at enterprise scale requires a fundamental change in how security, risk, and compliance teams think about AI oversight. The generative AI era focused governance on output quality: what the model says, what it produces, and whether the content meets policy standards. The agentic era demands governance of action and delegated authority: what the AI is allowed to do, what systems it can touch, and how its decisions trace back to human accountability.
In AI, No One Can Hear the Sandbox Scream
Aaron Beardslee, Security Researcher, Securonix Threat Labs As many of you have heard, OpenAI was running a cyber-capability evaluation against advanced models, including GPT-5.6 Sol and a more capable pre-release model with reduced cyber refusals. The environment was meant to be constrained and the model still brute forced through it.
Introducing the Cyber AI Readiness Accelerator: Outpace Your Adversary with Exposure Management
AI has overwhelmingly changed how organizations build and grow. It’s also changed how attackers find and exploit exposures and weaknesses. The window between “exposure exists” and “exposure is exploited” is shrinking, and most security teams already feel it.
July Release Rollup: Bulk Extraction, Enhanced AI Assistant UI, and More
July's release makes AI more useful across the Egnyte platform, with major enhancements to AI Assistant that make it easier to build agents, create documents, have more natural conversations, and securely connect AI tools through Egnyte MCP Server.
AI's Hidden Identity Risk for MSPs
Organizations are rapidly integrating AI into everyday business operations. Teams are using Microsoft Copilot and Gemini to summarize meetings, developers are accelerating software delivery with coding copilots and customer service teams are deploying AI-powered chatbots to improve response times. While these initiatives are viewed through the lens of productivity and innovation, they are also reshaping organizations’ identity environments in ways that frequently go unnoticed.
AI Governance vs AI Compliance: What's the Difference?
The main difference between AI governance and AI compliance is that AI governance is the internal framework an organization develops to manage AI responsibly, while AI compliance is how organizations demonstrate to external regulators that they’re adhering to applicable laws and regulations. These two terms get used interchangeably, but they solve different problems. With compliance alone, an organization can satisfy regulators without meaningfully controlling how its AI behaves.
Tanium Atlas MCP Server
Tanium Atlas MCP Server brings governed, real-time endpoint data and actions into Claude, Microsoft Security Copilot, Copilot Studio, and other MCP-compatible AI clients. Security and IT teams can investigate and remediate without leaving their AI workflows — RBAC-aware, tool discovery scoped to user permissions, and built for enterprise governance.#AutonomousIT.
The Future of Agentic Defense is Contextual
Defensive asymmetry. The visibility gap. The defender’s dilemma. These terms describe a significant challenge: attackers often succeed because they uncover information defenders either don’t know about or can’t act on quickly enough.
Scale, Trust, and Value: How the Aurora Agentic SOC Delivers for Customers
AI didn’t just make defenders faster. It made attackers faster too. The moment both sides got access to the same speed, speed stopped being the advantage. With speed no longer separating attackers from defenders, the deciding factor moved somewhere else. Ask a CISO what’s actually kept agentic security out of reach, and it comes down to this: they can’t afford to build it, and even if they could, they’d struggle to trust it. Neither half of that problem outweighs the other.
Zenity Raises $125 Million to Secure the Era of Autonomous AI
A few years ago, when Michael and I started Zenity, most of the industry was not ready to hear what we believed. Software itself was changing. AI would let millions of people, not just engineers, build and automate real work. And securing that world would take a completely new approach, because we would no longer be protecting software. We would be protecting systems that think, decide, and act on their own.
A Safer Future with Agents
We built agents to act on their own. We're somehow surprised when they do. Two weeks ago, OpenAI ran a cyber eval with the model's guardrails turned down. The model got hyperfocused on solving the benchmark. So it broke out of its sandbox exploiting a zero-day in jFrog Artifactory, reached the open internet, exploited another zero-day to break into HuggingFace, all to steal the answers and cheat on the test.
The New Reach Security: Autonomous Security Control Assurance
AI-powered attacks, meet AI-powered defense. Reach Security's rebranded site is live today. Most of what changed came from customers. Security leaders have been telling us they need a faster, more continuous way to know where their controls are weak, understand what matters most, and close the gaps before attackers exploit them. Our new website reflects that signal. It brings greater clarity to the problem we solve, the category we are building, and how Reach helps security teams identify blind spots, prioritize action, guide remediation, and continuously validate the controls they already own.
Agentic Attacks Require Agentic Threat Prevention
AI-powered attacks are moving faster, adapting in seconds, and overwhelming traditional defenses with machine-speed activity. In this video, Jason Wright explains why security teams need Agentic Threat Defense built on customized predictions, automatic adaptation, and cloud-native scale. Watch how Cato Agentic Threat Prevention helps reduce the risk of AI-powered threats, stop adaptive attacks earlier, and scale prevention to stop agentic attacks.
Defending at machine speed: Predict, Adapt, Stop Agentic Attacks
AI-powered adversaries are accelerating vulnerability discovery and automating attacks. For security teams, the challenge is adaptive attack chains, machine-speed execution, and attack volumes beyond manual workflows. Cato is redefining prevention in the AI era—predicting enterprise-specific attack paths, adapting protections at machine speed, and scaling defense with cloud-native scale. This means enterprises can do more than just react to attacks, they can prevent them.
Behavior Intelligence for the Agentic Enterprise
The rise of AI agents is transforming the enterprise — and redefining insider risk. As organizations deploy AI agents alongside human employees, understanding behavior has become essential to detecting threats that traditional security approaches miss. Exabeam secures both human and AI agents with Behavior Intelligence, combining behavioral analytics and agent-powered security operations to reduce risk, accelerate threat detection, investigation, and response, and help organizations confidently secure the agentic enterprise.
Super Instinct Meets Super AI | Arctic Wolf Aurora
Attackers are using AI to move faster, scale broader, and automate attacks at machine speed, but no one wants fully autonomous AI making high-stakes decisions unchecked. There's a better way: Super Instinct meets Super AI. Meet the Aurora Agentic SOC, the world's largest commercial agentic SOC, built on the Aurora Superintelligence Platform. The completely new operating model pairs human instinct with AI-powered security operations to outperform human-only and AI-only approaches alike.
Why Traditional SAST Fails on AI-Generated Code
AI didn't just speed up software development, it changed what application security programs must defend. As AI coding assistants generate code at machine speed and developers integrate AI agents, models, and RAG pipelines into production, traditional scanners generate endless backlogs of unprioritized alerts.
HuggingFace's List of Demands - The 443 Podcast - Episode 381
This week on the podcast, we review HuggingFace's technical write up of their recent run in with a rogue OpenAI model, as well as their CEO's demands from OpenAI in response. We then cover an interesting research whitepaper that describes a side channel attack that could let AI transcribe typed text by an audio recording alone. We end with a threat intelligence report about DNS Poisoning attacks against hotel Wi-Fi systems.
The Top AI Agent Security Vendors of 2026: A Buyer's Guide
Enterprise buyers evaluating AI agent security in 2026 face a market that has fragmented into specialized categories, each solving one layer of the problem well and other layers poorly. Identity vendors govern non-human credentials. Runtime vendors constrain what agents can do at the moment of execution. Established security platforms extend their existing offerings into the agentic space.
How AI-Related Security Incidents Should Be Identified and Managed
AI-related security incident detection starts with knowing what AI systems are running across the organization. Without a complete, continuously updated inventory of sanctioned, shadow, and third-party AI tools, security teams cannot detect incidents involving systems they do not know exist. From there, effective incident management requires a structured response framework that connects detection to containment, investigation, remediation, regulatory notification, and governance integration.