Existing security controls weren’t designed for AI agents.

Static credentials and standing privileges aren’t sufficient for an emerging model where organizations need to rapidly authorize, limit, and revoke permissions from autonomous agents, sometimes more than once within a single workflow.

Agentic AI requires organizations to carefully consider how to govern agentic identity, agent-to-agent communication, secrets management, privileged access, and workforce identity.

Agentic identity

The first challenge is to establish a reliable identity for agents themselves.

The “how” here is still being hotly debated. Some organizations treat AI agents as another form of non-human identity, similar to service accounts or machine identities. Others argue that agents should be their own category, distinct from both human users and machine accounts.

In any case, agents need something like a “certificate” to give them an identity that can be recognized and governed across environments. This is especially important because, in most enterprises, agents will operate across multiple environments, including cloud platforms, on-premises systems, and SaaS applications. 

Agent-to-agent communication

Securing agentic AI requires organizations to limit not only which resources AI agents can access, but also which other access-enabled agents they can communicate with. This is often currently handled with Model Context Protocol (MCP) gateways, although this approach is largely giving way to the use of agentic mesh.

An agentic mesh is a distributed architecture where multiple specialized AI agents can discover one another, coordinate, and collaborate on tasks without a central controller. This approach lets organizations overlay intent-based communication rules via certificates, but also allows permissions to be revoked on demand.

Agentic secrets

Traditionally, secrets like passwords and API keys are managed via requests through IT service management platforms. But this mechanism doesn’t work for AI agents, which operate too quickly and across too many systems to rely on static credentials.

Instead, secrets should be generated dynamically, used for a specific purpose, and then retired when the task is complete. This approach can be compared to modern hotel key cards. Unlike the physical room keys of the past, a key card is issued for a specific stay, but after that, it becomes worthless to both legitimate users and malicious actors.

Privileged access

AI agents may start with the same permissions as a given human user, drawing on relevant business systems and data for context. However, as workflows get handed off from agent to agent, this privilege should not be passed along throughout the process. Rather, privileges should be whittled down at each stage until only a thin layer remains to authorize a specific execution step.

Workforce identity

Organizations already manage the identities of human workers, of course, but often these identities are handled differently across separate management platforms and sign-on tools. To support agentic AI, organizations must find ways to break through this fragmentation, ensure that worker identities are current, and translate workforce permissions correctly into agentic workflows.

A lifecycle approach to identity

These five areas should not be addressed in isolation. Rather, organizations should apply governance and observability across the identity lifecycle, ensuring that every agentic action can ultimately be traced back to approved access and permission levels.

The outcomes of this effort—including dynamic access, the principle of least privilege, strong identity, and clear auditability—are goals that many organizations have long been pursuing. The rise of agentic AI makes them more urgent than ever.

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