Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Enterprise artificial intelligence adoption is scaling at a pace that manual inventory methods simply cannot match. This rapid proliferation has created a severe visibility chasm for security and risk teams: it is fundamentally impossible to govern, secure, or quantify what you do not know exists. ‍ To bridge this gap, organizations are shifting away from point-in-time compliance audits and adopting continuous discovery.

When your AI agent calls an MCP tool, that tool has no idea who actually triggered the request. It sees the agent, not you. This post explains why that matters and how to fix it with Teleport JWTs. In part two of this post, we will explain how to extend this to AWS to carry your identity through Amazon Bedrock AgentCore all the way into CloudTrail.

As organizations aggressively shift from static Large Language Model (LLM) chatbots to fully dynamic, autonomous AI agents (e.g. systems designed to plan workflows, call APIs, write runtime code, and modify enterprise databases), traditional compliance and governance frameworks are hitting a breaking point. A landmark press release from Gartner highlights a critical systemic risk: treating AI agent governance as a monolithic, one-size-fits-all policy guarantees project failure.

There’s a category of security risk that most organizations aren’t ready for. It doesn’t live in your code repository, your CI pipeline, or your developer laptops. It lives in your runtime, in the autonomous AI agents already running in your environment, extending their own capabilities, and making decisions that no human explicitly approved. This is the challenge JFrog set out to address with our integration with NanoCo AI and their open-source agent framework, NanoClaw.

Recently, I had the pleasure of presenting an AI governance-focused webinar with my colleague Neil Jones at Egnyte. In the session, we discussed many ways to improve AI governance, and you can watch and share the complete session replay here. During the session, we discussed the importance of respo nsible AI usage policies. However, my experience is that many organisations struggle to create policies aligned with their business requirements and the technological solutions that they use.

Security teams are drowning in findings, not because scanners miss things, but because nothing confirms which ones an attacker could actually reach. Seemplicity AI Analysts run the investigation themselves, checking runtime configuration, network reachability, and exploit conditions for each finding, and re-rank your backlog by confirmed exploitability. What rises to the top is backed by evidence. What drops down has been checked and reasoned out.