IAM for Agentic AI

AI agents are autonomous or semi-autonomous software entities that leverage artificial intelligence techniques to perceive their environment, make decisions, take actions and ultimately achieve specific goals with limited human supervision once initiated. LLM-based AI agents combine software orchestration and language-based or multimodal foundation models to implement this type of behavior.

Traditional IAM systems will face challenges when managing the identities of AI agents due to their unique characteristics and the evolving nature of their interactions. The divergent approaches between AI tool providers and the IAM industry further complicate this landscape, potentially leading to fragmented security, inconsistent policies and challenges in achieving comprehensive auditability and accountability.

• Governance and administration - establish clear ownership and responsibility for agent actions, such as:
  • Assigning ownership
  • Defining policies
  • Human oversight
  • Governance framework
  • Reviews and audits
     
• Identity issuance - Uniquely identifying and registering AI agents within IAM systems is foundational for managing their access and ensuring accountability. This can be achieved via the following:
  • Agents as a distinct constituency
  • Registration and attestation workflows
  • Leveraging existing protocols
  • Unique identifiers and metadata
  • Supporting multiple credential types
  • Autodiscovery of agents
     
• Authentication - Robust authentication mechanisms should preferably be provided by the enterprise identity provider. This can be achieved via the following:
  
  • Dynamic and just-in-time (JIT) access
  • Strong authentication
  • Mutual authentication for multiagent collaboration
     
• Authorization - AI agent interactions require dynamic and fine-grained authorization mechanisms to implement zero-trust and least-privilege principles. This can be achieved via the following:
  • Delegation of authority
  • Fine-grained access control
  • Context-aware access controls
  • Resource-level access control
  • Human-in-the-loop authorization flows
  • Rich authorization requests (RAR)
     
• Federation - The collaboration of AI agents across security domains requires the establishment of trust and workload federation to enable token exchange:
  • Trust agreement
  • Workload federation
  • Token exchange
     
• Monitoring - Comprehensive logging and auditing of AI agent actions are essential for security monitoring, incident response, compliance and maintaining accountability. Effective monitoring involves:
  • Detailed logging
  • Tracking delegated actions
  • Token activity monitoring
  • Auditing delegation credentials
  • Enforcing segregation of duties (SoD)
     
• Observability - Comprehensive discovery, visibility and observability of AI agent configuration and actions are essential for maintaining security posture and responding to threats:
  • AI agent discovery
  • Enhanced metadata capture
  • Agent-specific behavioral baselining
  • Behavioral monitoring and anomaly detection
  • Threat detection and response

• Adopt a workload identity approach
• Put humans first to help establish trust and self-govern their AI agents
• Establish robust governance and accountability frameworks
• Implement comprehensive discovery, visibility and observability
• Enforce the principle of least privilege
• Leverage and extend existing IAM protocols
• Implement strong and dynamic authentication
• Implement secure multiagent collaboration
• Adopt fine-grained and context-aware authorization
• Investigate natural language to structured permission translation

Need more guidance on AI agents? We have a whole spotlight track discussing the latest insights on IAM and AI at Gartner Identity & Access Management Summit 2025, happening December 8 – 10, in Grapevine, TX.