Frameworks from firms like KPMG and AWS emphasize that AI agents must be treated as entities with identities and permissions. A strong IAM foundation is a critical control layer to prevent agents from accessing or unintentionally leaking sensitive information, reflecting a broader shift to treat agents like any other privileged user in an IT ecosystem.

Enterprises should model AI agent identity in two layers. A "Stable Agent Principle" acts like a permanent user account for governance, while a "Temporal Runtime Identity" acts like a temporary session for specific actions. This prevents overwhelming identity systems while ensuring full auditability and accountability for every agent action.

For CISOs adopting agentic AI, the most practical first step is to frame it as an insider risk problem. This involves assigning agents persistent identities (like Slack or email accounts) and applying rigorous access control and privilege management, similar to onboarding a human employee.

Simply giving an agent a user account is dangerous. An agent creator is liable for its actions, and the agent has no right to privacy. This requires a new identity and access management (IAM) paradigm, distinct from human user accounts, to manage liability and oversight.

Current identity standards like OIDC are insufficient for AI agents. The future requires a "three-legged stool" identity combining a service account (the agent's identity), owner role claims, and "on-behalf-of" claims inherited from the user.

A key barrier to enterprise AI adoption is security and control. AWS's Bedrock Managed Agents provides each agent with its own dedicated compute environment and unique identity. This allows security teams to create specific governance policies for each agent, balancing enablement with necessary guardrails.

Teleport's decision to build a single identity layer for humans, machines, and workloads prepared them for the AI wave. This architecture became critical for containing non-deterministic AI agents, as enforcing security policies requires reasoning about all identity types simultaneously.

Traditional audit logs and screenshots are inadequate for AI agents. To ensure accountability, every agent needs a distinct, machine-readable identity, like a Decentralized Identifier (DID). All agent actions should be cryptographically signed and recorded in a tamper-evident ledger to create a trustworthy audit trail.

The rise of autonomous software agents like Cognition's "Devin" introduces a new, critical security layer: agent identity. Organizations must decide if agents have their own unique identities or inherit them from the deploying user. This is fundamental for creating auditable logs and securing their actions.