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AI adoption is accelerating across the enterprise, but governance isn’t keeping pace—leaving security teams without a clear view of what AI is running, how it’s being used, and where it introduces exposure. In this Demo Drill Down, we showcase AI Inventory in Falcon Exposure Management, delivering a centralized view of AI across hosts—from local LLMs and MCP servers to IDE extensions, packages, and applications.
There’s never a good time to lose a production database, but losing one to your own AI coding agent on a Friday afternoon has to rank near the bottom of the list. That’s the backdrop to the PocketOS incident, and it’s the clearest case yet for why AI agent security and intent-based access control belong at the top of every cloud security roadmap this year.
As more organizations move past experimentation and start planning real AI agent deployments, the same set of concerns keeps surfacing in our conversations with security teams. Whether the worry is a shadow agent that shows up uninvited or a sanctioned agent going rogue, the questions tend to cluster around control: These are the right questions to be asking, and they share a common answer that’s more concrete than most people expect. AI agents are only as dangerous as the privileges they can reach.
As autonomous and semi-autonomous AI systems take on more responsibility within the enterprise, they shift from being “features” of software to becoming true internal actors. They make decisions, take actions, call tools, orchestrate workflows, and influence other AI agents. With this evolution, we must confront an uncomfortable truth: the metrics and response patterns we built for deterministic software no longer work.
The external auditor’s evidence request lands Tuesday morning. A security architect at a Tier 1 bank pulls up her AI-SPM dashboard for the SOC2 Type 2 review. Eighty-three AI agents running across the bank’s clusters. For each one, the dashboard shows the current configuration and the current behavioral baseline. The data is accurate, comprehensive, and point-in-time.
It is 11:47 p.m. and the on-call security engineer is staring at two dashboards. On the left, LangSmith — the ML team’s debugging stack — showing the agent’s prompts, model responses, tool calls, and tokens consumed. On the right, the runtime detection console showing eBPF-captured syscalls, network connections, and process trees from the same Pod. Both are populated.
Cyber risk is not one size fits all In LATAM, organizations face evolving threats, uneven security maturity, and AI-driven challenges. Paul Harris breaks it down on The 443 Podcast.
In this video, we introduce Elastic Security and how it helps modern teams manage security operations more efficiently. It brings together detection, investigation, and response into one unified platform using AI, automation, and integrated security tools. Additional Resources.
Short answer: Because attackers exploit fragmentation faster than governments can respond This shift toward collective cyber defense is a cornerstone of the new federal vision. The March 2026 National Cyber Strategy for America explicitly calls for a "new level of relationship between the public and private sectors" and demands "unprecedented coordination across government" to protect the American people.