Shadow AI is a tool problem: employees using AI tools that IT hasn’t approved. The AI agent identity gap is an architecture problem: agents operating with credentials, accessing core business systems, and taking actions while the organization has no inventory, policy, or monitoring for them.
Shadow AI is a people and process problem: employees use tools that aren’t approved because the approval process is slow, the approved alternatives are inadequate, or the policy isn’t enforced. AI governance typically starts with discovery, policy, and enforcement: find the tools, set the rules, and enforce them.
AI agent identities create an additional problem. Agents are automated systems with credentials, access permissions, and the ability to take actions at machine speed across the systems they can access.
An agent that inherits excessive permissions from the user that deployed it or continues operating after the use case it was created for has ended can leave credentials and access in place indefinitely. CSA research found that 51% of organizations report no clear ownership of AI identities.
Only 16% of organizations effectively govern AI system access to core business platforms, according to the 2026 CISO AI Risk Report.
Agent governance starts with several practical controls:
That approach aligns with the May 2026 joint guidance from CISA, NSA, and international cybersecurity partners, which recommends strong identity management, limited privileges, continuous monitoring, and maintaining a trusted registry for agentic AI systems.
The starting point is discovery. You can’t set policies for agents you haven’t inventoried or revoke credentials for agents you don’t know exist. And that remains a widespread gap. Only 21% of organizations maintain a real-time registry or inventory of their agents, according to CSA.
Larridin’s AI Adoption capability surfaces AI tools and agents operating across the enterprise, including sanctioned and unsanctioned use. That gives CISOs an inventory and usage context they can use as the starting point for broader identity and access governance.
An AI agent identity is the credential or set of credentials an autonomous AI system uses to authenticate itself to other systems and take actions. It can include API keys, OAuth tokens, service account credentials, or other access mechanisms.
Unlike human identities, AI agent identities may lack a clear owner and may not go through the same provisioning, review, and decommissioning processes as employee accounts.
It starts with an inventory of which agents exist and what they can access, followed by scoped permissions, continuous monitoring, and a process for revoking credentials when an agent is no longer needed.
Larridin addresses the discovery prerequisite by helping organizations identify AI tools and agents in use. Identity and access management systems remain responsible for provisioning, enforcing, and revoking credentials and permissions.
The governance principles are similar, but AI agents can be more dynamic. They can interact with multiple systems, interpret instructions, and take a series of actions during a single task.
That makes clear ownership, tightly scoped permissions, monitoring, and lifecycle management especially important as agent deployments scale.
There’s no single regulation that creates a universal AI agent identity standard. Security guidance is becoming more specific, however.
In May 2026, CISA and its partners recommended strong identity management, limited privileges, continuous monitoring, and clear oversight for agentic AI systems. Organizations should also assess the privacy, cybersecurity, and AI regulations that apply to their specific systems and use cases.
Larridin helps CISOs discover sanctioned and unsanctioned AI tools and agents across the enterprise, providing the inventory and usage context needed to begin governing AI agent identities.
Book a discovery call to see your enterprise AI inventory.