Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

Least privilege is foundational. It's been a core security principle for decades, and it's no less relevant in agentic AI environments. An agent shouldn't hold permissions beyond what its task requires, and remediating over-permissioned agents is one of the highest-value quick wins available to any agentic AI security program. But here's what the security industry has been slow to acknowledge: correctly implemented least privilege still isn't sufficient.

The Trump administration has spent much of its second term removing regulatory constraints on AI development. On June 2, it added one back voluntarily and carefully. Earlier this week, President Trump signed "Promoting Advanced Artificial Intelligence Innovation and Security" after months of internal debate, a last-minute pull of the signing in May, and a compressed final timeline. The result of this tumult is an order that strikes a deliberate balance.

At Zenity, we like to say we don't only exist on the bleeding edge; we are the bleeding edge. It's a defensible claim. Zenity Labs consists of multiple teams focused on various technical disciplines within the security industry, and while the Labs moniker sits loosely over the group, the work it produces tells a unified story around AI Agent security.

When the AI Agent Security Summit launched in San Francisco last October, agent-based threats had already escalated from a novel consideration to a predominant blocker for enterprise adoption. The security community was laser-focused on recognizing and minimizing the blast radius posed by agentic vulnerabilities, whether that meant indirect prompt injection, MCP poisoning, or hallucinations.

The security industry hasn’t been wrong about agentic AI risk. It’s been incomplete. There’s no shortage of single-signal solutions for the problem: tools that analyze prompts for malicious content, platforms that monitor data access patterns, capabilities that assess model behavior for signs of manipulation. Each captures something real. None is sufficient on its own.

Gartner named Zenity the Company to Beat in AI Agent Governance on April 17, 2026. That recognition, grounded in technical capabilities, customer implementations, ecosystem breadth, and business model, isn't a marketing award. To us, it's the analyst community confirming that purpose-built architecture for agentic AI is winning. The recognition didn't come in isolation. Gartner's own language captures the stakes.