Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The race to adopt AI agents has created a massive, unmonitored blind spot in the enterprise software supply chain. At the heart of this revolution is the Model Context Protocol (MCP) – an open connectivity standard designed to move AI models (LLMs) out of their passive “chat box” and give them direct active access to your company’s internal systems.

This week, we released our 2026 State of Agentic AI Risk Report, a global survey of 250 senior cybersecurity leaders examining how enterprises are approaching agentic AI as it moves closer to production. The findings point to a clear reality. While AI agents are advancing quickly, security leaders are deliberately slowing adoption. In fact, 98% of respondents say security and data concerns have already slowed deployments, added scrutiny, or reduced the scope of agentic AI initiatives.

Researchers at Google’s Threat Intelligence Group (GTIG) warn that nation-state threat actors have adopted Gemini and other AI tools as essential components of their operations. The threat actors are using tools to conduct research and reconnaissance, target victims, and rapidly create phishing lures.

AI is not just another workload category. It is the first category of workloads that decides what to do at runtime. And that changes everything about how security must work in the cloud. For years, cloud security evolved around deterministic systems. You deploy code. That code follows defined logic paths. If something unexpected happens, such as a new process, an unusual outbound connection, or privilege escalation, you investigate and respond.

In this article, we will explore the hot topic of securing AI-generated code and demonstrate a technical approach to shifting security left for cloud AI agents by using Model Context Protocol (MCP) tools.

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.

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.

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.

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.