Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Coding agents have become one of the fastest-adopted AI technologies in the enterprise. They help developers write code, debug applications, automate repetitive tasks, and ship software faster than ever before. They also introduce a security challenge unlike anything most organizations have faced. Unlike traditional AI assistants that generate content, coding agents take action.

A few years ago, I was sitting across from a security leader at a large enterprise. They had just deployed their first wave of AI agents. When I asked how they were thinking about the security of it, they paused for a moment and then said something I haven’t forgotten. I felt that. Not just as a researcher, but as someone who had been in enough of those rooms to know it was not one person’s gap. It was the whole industry’s gap.

Customer telemetry shows how AI agents behave in a limited set of production environments and what risks they carry. Vulnerability research surfaces how those environments can be attacked. Both sources are valuable, but neither shows actual attacker behavior or how quickly they operationalize a new vulnerability once it's public.

Anthropic’s Claude Tag represents a meaningful shift in how AI agents operate inside the enterprise. Unlike traditional AI assistants that act on behalf of an individual user, Claude Tag introduces a shared AI agent with its own identity, credentials, service accounts, and permissions. That shared agent lives inside a Slack channel, builds context over time, connects to enterprise systems, and performs work for everyone in the conversation.

For the past two decades, enterprise security has evolved around a relatively stable assumption: software executes instructions, people take actions, and security teams are responsible for understanding and governing the interaction between the two. The technologies have changed. Infrastructure moved to the cloud. Applications became distributed. Identities expanded beyond employees to include partners, contractors, and machines. Yet the underlying model remained remarkably consistent.

The regulatory landscape for agentic AI is moving faster than most compliance programs are tracking. CISOs who wait for final guidance before building their compliance posture will find themselves in catch-up mode at exactly the wrong moment and, in some cases, already behind.

The next government security challenge isn’t AI models, it’s AI agents. Zenity and Carahsoft are helping agencies prepare. Across government agencies, AI agents are already interacting with sensitive data, mission-critical workflows, and public services. Yet most organizations still lack visibility into where these agents are deployed, what they can access, and how they behave once operational. The result is a growing governance gap between AI adoption and AI security.

Many organizations still use the terms AI governance and AI security interchangeably. While they are closely related, they address fundamentally different challenges. Governance establishes accountability, defines acceptable use, manages risk, and helps organizations align AI adoption with business, legal, and regulatory requirements. Security focuses on understanding and controlling behavior.

We are excited to share that Zenity now integrates with Claude's Compliance API to bring Claude activity into the same AI security and governance platform enterprises already use to govern agents across the business. By combining Claude's Compliance API telemetry with Zenity's native agent security capabilities, security teams gain the visibility, posture controls, and real-time enforcement needed to secure Claude across the full agent lifecycle.

Identity tells us an agent is allowed to act, intent tells us why it is acting. In an agentic world, only one of those questions actually predicts whether your environment is about to break.