Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

DSPM and Data Discovery: Finding and Classifying Sensitive Data at Scale

Proprietary data is the definitive differentiator in the age of AI. Models can be replicated, infrastructure can be rented, and tools can be replaced. What cannot be easily reproduced is institutional knowledge, customer insight, and strategic intent found in enterprise data. This data must be continuously identified, deeply understood, and actively protected as it changes state, location, and context.

SafeBreach's Evolution into an AI-First Development Team: Part 2

In this second installment of a series on the transformation of SafeBreach’s development organization, VP of Development Yossi Attas details a structured operational workflow that integrates Jira, BitBucket, and Claude Code to turn AI usage from ad-hoc prompting into a rigorous engineering methodology.

Governing Agentic AI: A Practical Framework for the Enterprise

In my previous piece, "The Agentic AI Governance Blind Spot," I laid out what I believe is one of the most critical gaps in the AI governance landscape today: the three most cited frameworks in AI governance, NIST AI RMF, ISO 42001, and the EU AI Act, don’t contain a single mention of agentic AI. Not one reference to autonomous agents, multi-agent systems, or AI that takes actions with real-world consequences. The response to that piece confirmed what I suspected.

Agentic AI Security: MITRE ATT&CK Coverage Analysis in Minutes

LimaCharlie's Agentic SecOps Workspace (ASW) enables true agentic security operations. With us, AI doesn't just advise but actively operates within your security environment. We do this by integrating everything, including AI, on our cloud platform via API. Our approach delivers superior AI security automation capabilities at a fraction of the cost, allowing security teams to scale operations without growing headcount.

What are AI skill-gaps new defenders can leverage? #cybersecurity #ai #podcast

AI skill gaps are a real conversation right now, and Chris Cochran, Field CISO and VP of AI Security at SANS Institute, breaks it down into three practical buckets for defenders who want to stay ahead. Start by figuring out what you can offload to AI: summarization, enrichment, repetitive tasks. Save the deterministic decisions for humans. Then learn how to secure AI itself: Finally, understand governance. Not just the technical side, but what your company is actually trying to do with AI. Security practitioners who can enable the business, not just protect it, become irreplaceable.

Live Webinar- Securing Multi-AI Deployments MCP; Agentic AI & Inter-AI Security

live webinar with Aaron Turner, IANS Faculty, who presents findings from his recent IANS research, 7 Steps to Securing Multi-AI Deployments, and explain how security teams can apply proven principles to modern AI systems.

Securing Human and Non-Human Identities from AI Security Risks

As organizations rely more on Artificial Intelligence (AI) to power critical operations, the infrastructure supporting AI development and deployment is becoming a high-value target for cybercriminals. From model training and data pipelines to cloud workloads and APIs, AI operations rely on access to privileged credentials and critical systems.

Agentic AI Security: Onboard Multi-Cloud Environments with AI

LimaCharlie's Agentic SecOps Workspace (ASW) lets agentic AI security solutions operate directly inside your environment. Everything in LimaCharlie’s SecOps Cloud Platform connects via API. For us, AI isn't a bolt-on layer. It's woven into the same fabric as your detections, sensors, and integrations. Limacharlie’s approach makes it easy for users to bring-your-own-LLM into security operations.