Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

1Password

Security advisory for AI-assisted browsing interactions with the 1Password browser extension

Jan 30, 2026 By Andrew Hall and Drew Sen In 1Password
This advisory describes an ecosystem-level risk that emerges when AI agents are able to autonomously read and act on untrusted content while operating with user-level permissions in a web browser.
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gitguardian

Agentic AI and NonHuman Identities Demand a Paradigm Shift In Security: Lessons from NHIcon 2026

Jan 30, 2026 By Dwayne McDaniel In GitGuardian
In the race to innovate, software has repeatedly reinvented how we define identity, trust, and access. In the 1990's, the web made every server a perimeter. In the 2010's, the cloud made every identity a workload. Here in 2026, agentic AI makes every action autonomous.
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protecto

Agentic Data Classification: A New Architecture for Modern Data Protection

Jan 30, 2026 By Amar Kanagaraj In Protecto
In the evolving landscape of data protection and compliance, data classification is the bedrock of safe AI workflows. Yet legacy approaches rely on singular models that are fixed, rigid, and limited in context. Our agentic data classification approach reshapes this paradigm by not relying on any single model. Instead, we orchestrate a dynamic, intelligent layer that automatically selects the right model for the job.
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Featured Post
AI for Security Infrastructure: Rebalancing Cybersecurity for the Decade Ahead

AI for Security Infrastructure: Rebalancing Cybersecurity for the Decade Ahead

Jan 29, 2026 By Garrett Hamilton, CEO and Co-Founder In Reach Security
For more than a decade, cybersecurity has been shaped by a single doctrine: assume breach. Facing high-volume, relentless, and diverse attacks, the security industry has been forced into a reactive stance, playing a constant game of whack-a-mole in a nonstop damage-limitation exercise. This has driven major investment in detection, response, and recovery, and created a world in which organizations are better at reacting to incidents than at preventing them in the first place.
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salt security

Measuring Agentic AI Posture: A New Metric for CISOs

Jan 29, 2026 By Eric Schwake In Salt Security
In cybersecurity, we live by our metrics. We measure Mean Time to Respond (MTTR), Dwell Time, and Patch Cadence. These numbers indicate to the Board how quickly we respond when issues arise. But in the era of Agentic AI, reaction speed is no longer enough. When an AI Agent or an MCP server is compromised, data exfiltration happens in milliseconds rather than days. If you are waiting for an incident to measure your success, you have already lost.
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nightfall

Beyond Pattern Matching: How AI-Native File Classification Solves Modern DLP Challenges

Jan 29, 2026 By Chris Martinez In Nightfall
Legacy DLP operates on a fundamental constraint: it identifies sensitive data by matching patterns. Credit card numbers follow the Luhn algorithm. Social Security numbers conform to a nine-digit format. API keys match specific string patterns. This approach works for structured data, but it fails to address a critical reality: Your most sensitive assets aren't numbers. They're documents.
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Forward Networks

Introducing Forward AI

Jan 29, 2026 By Forward Networks In Forward Networks
As enterprises move toward agentic operations, speed without data accuracy becomes a liability. At Forward Networks, we recognized this challenge and set out to deliver a solution: speed backed by mathematical accuracy. In networking, acting on incomplete or approximate data is not an inconvenience, it is a cause of outages, security exposure, and operational risk.
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AI is Actively LEAKING Your Data (And You Don't Know It) #apisecurity #airisks #dataprotection #ai

Jan 29, 2026 By Wallarm In Wallarm
AI agents don't think. They pattern-match. Critical to understand: Generative AI (ChatGPT, Claude, etc.) does NOT reason like humans. It: The API Security problem: When you give an AI agent access to an API, it will: AI agents can't reason. They recreate patterns based on weights. You need to be very careful: data in, data out. Practical example: text User: "Show me the account balance for user" AI agent → calls GET /api/account/123 API → returns { balance: 5000, name: "John", SSN: "123-45-6789" } AI agent → outputs EVERYTHING to user (including SSN!)
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