Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Falcon Exposure Management AI Inventory: Demo Drill Down

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.

Nine Seconds to Delete a Database: What the PocketOS Incident Teaches Us About AI Agent Privilege Management

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.

How Zero Standing Privileges Defuses the Shadow AI Agent Problem

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.

The Metric AI Security is Missing

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.

AI-SPM for Financial Services: Managing AI Risk Under SOC2, PCI-DSS, and MAS TRM

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.

Prompt and Tool Call Visibility: What Your AI Agents Are Actually Doing

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.

Whole-of-state cyber defense: How AI-driven security helps US states protect what matters most

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.

Datadog MCP Server, Experiments, Bits AI Security Analyst, and more | This Month in Datadog

April’s This Month in Datadog spotlights the Datadog MCP Server, which gives AI agents secure, real-time access to Datadog telemetry, and Datadog Experiments, which lets you design, launch, and analyze experiments to see the full impact of product changes on the user journey. Plus, we cover how to: Accelerate Cloud SIEM investigations with Bits AI Security Analyst Remediate vulnerabilities in your codebase with Bits AI Dev Agent for Code Security Explore Datadog with natural language using Bits Assistant.