Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Conversations at RSA 2026 circled back to the same topic: identity is the foundation of AI agent security. While it’s understandable, it’s the wrong way to look at things. Identity tells you who showed up. It says nothing about whether what they did made sense.

Two significant software supply chain cybersecurity attacks, seven days apart, with one hundred and eighty million weekly downloads between them. The chaos from development teams to the boardroom is real. And the pace is only going to get faster. Much, much faster…

Josh Rector is the Compliance Director, Public Sector at Ace of Cloud, a security and compliance consulting firm, certified CMMC Third-Party Assessor Organization (C3PAO), and Registered Provider Organization (RPO). With more than a decade of experience in cybersecurity compliance, he has worked both sides of the assessment table, leading internal and external assessments, serving as ISSO for systems at federal agencies, and guiding cloud service providers through the FedRAMP authorization process.

In 2025, organizations spent billions on security, deploying EDR/XDR, SASE, firewalls, identity platforms, email security, web security, and more. And yet, breaches persist. The reason often is not a zero-day, an advanced persistent threat, or a cutting-edge exploit. It is far more mundane. Misconfigurations across identity, endpoint, network, and email/web security controls remain among the top root causes of incidents.

Note: The core architecture for this pattern was introduced by Isaac Hawley from Tigera. If you are building an AI agent that relies on tool calling, complex routing, or the Model Context Protocol (MCP), you’re not just building a chatbot anymore. You are building an autonomous system with access to your internal APIs. With that power comes a massive security and governance headache, and AI agent security testing is where most teams hit a wall.

Organisations deploying agents face a challenge: the predominant AI frameworks most organisations rely on do not explicitly address agentic AI. This is true for the big three: ISO 42001, NIST's AI Risk Management Framework (RMF), and the EU AI Act.