AI agent identity management

What is AI agent identity management?

AI agent identity management is the set of technologies, policies, and practices used to create, authenticate, authorize, and govern the digital identities of autonomous AI systems—also known as AI agents. These agents, which include bots, copilots, and autonomous workflows, increasingly perform sensitive tasks across enterprise systems, often acting on behalf of human users or other agents.

Unlike traditional identity management, which focuses on long-lived human accounts or static machine identities, AI agent identity management is designed for dynamic, ephemeral actors that operate at machine speed. It addresses the unique needs of agents by enabling:

• Just-in-Time (JIT) identity provisioning to avoid pre-provisioning static accounts

• Delegated authority through standards like OAuth On-Behalf-Of (OBO)

• Task- and context-aware authorization using policy-as-code and dynamic attributes

• Runtime authentication using PKCE, SPIFFE/SVID, or DPoP for proof-of-possession

• Comprehensive auditing and observability to ensure accountability and compliance

As enterprises adopt agentic AI at scale, identity management for these agents becomes critical for enforcing Zero Trust, reducing credential sprawl, preventing privilege escalation, and maintaining operational and regulatory oversight.

Agent identity management platforms like Strata’s Maverics provide the orchestration layer needed to unify human, machine, and agent identities across clouds, runtimes, and identity providers

Real-world examples of AI agent identity management

1. Customer support copilot
An AI-powered assistant helps resolve support tickets. When it needs to issue a refund:

• The agent authenticates using OAuth PKCE.

• It acts on behalf of a human support rep using an OAuth On-Behalf-Of (OBO) token.

• Every action it takes is logged with clear delegation chains showing who authorized what.

2. Automated infrastructure remediation bot
A DevOps agent monitors cloud infrastructure for security drift. When it detects a misconfigured firewall:

• It is provisioned Just-in-Time with a scoped identity using SPIFFE/SVID.

• The agent is authorized only to make changes in approved network zones.

• It logs its actions via OpenTelemetry for security audit and compliance traceability.

• The identity is automatically revoked after the task is complete to prevent lingering access.

3. Financial trading assistant
A goal-driven AI agent executes trades for a portfolio manager:

• It authenticates using PKCE and DPoP (to prevent token replay).

• A liveness check (e.g., biometric) is required before executing trades over a certain threshold — enforcing a “human in the loop.”

• Maverics binds the agent’s actions to the original delegating human, with runtime authorization based on current market risk conditions.

4. Age-verification micro-agent
An identity-verifying agent is used to check age eligibility for purchasing concert tickets:

• The agent is provisioned on demand and returns a zero-knowledge proof (ZKP) result (yes/no) without exposing the user’s birthdate.

• It operates under a fine-grained, short-lived identity, and is logged out automatically after fulfilling its purpose.

Key differences in identity management for AI agents

Identity management for AI agents differs from traditional identity management in several fundamental ways. These differences stem from the nature of agents themselves: they are autonomous, ephemeral, and act at machine speed — often on behalf of other users or systems. Below is a breakdown of how AI agent identity management diverges from traditional human or static machine identity models:

1. Ephemeral lifespans vs. long-lived accounts

• Human/machine users: Typically have accounts that exist for months or years.

• AI agents: May exist for seconds or minutes, spun up dynamically in response to a task or event.

• Implication: Requires Just-in-Time (JIT) provisioning and automatic expiration of credentials.

2. Delegated authority vs. direct action

• Humans: Act on their own behalf with direct credentials.

• Agents: Often act on behalf of a human user or another system, requiring delegation chains and traceability.

• Implication: Requires OAuth On-Behalf-Of (OBO), token exchange, and delegation-aware authorization.

3. Runtime identity vs. pre-provisioned identities

• Traditional systems: Identities are manually created in advance and tied to infrastructure (e.g., service accounts).

• Agents: Need dynamic, purpose-bound identities issued at runtime, with task-specific scopes.

• Implication: Identity must be provisioned and governed at runtime, not only at admin time.

4. Fine-grained, contextual access vs. role-based access

• RBAC/ABAC models: Sufficient for static users and roles.

• Agents: Need policies that evaluate context — task type, delegation source, risk level — not just role.

• Implication: Requires policy-as-code (e.g., OPA, IDQL) and dynamic attribute-based authorization.

5. Cross-domain execution vs. single-domain access

• Humans: Usually operate in one cloud or system at a time.

• Agents: Routinely span multiple clouds, APIs, and identity domains in a single workflow.

• Implication: Identity must federate across trust zones and be portable — requiring token exchange and a unified identity fabric.

6. Auditability and observability gaps

• Traditional users: Actions tied to sessions and user IDs.

• Agents: Without proper identity, their actions are often unauditable, lacking attribution or visibility.

• Implication: Requires centralized logging, signed execution graphs, and policy decision telemetry.

Identity for AI agents isn’t just a new use case — it’s a fundamentally different class of identity that demands a purpose-built identity architecture.