AI agents pose unique IAM challenges:
Scale & Autonomy: Organizations could see thousands of agents operating independently, requiring controls for on/offboarding and delegated authority.
Mixed Identities: Agents may act on behalf of a user or as autonomous entities with their own credentials.
Threat & Detection: Traditional “bot detection” may erroneously block legitimate AI agents, or fail to catch malicious ones.
Governance & Oversight: Sponsors or custodians must monitor an agent’s behavior, entitlements, and risk posture.
Consent & Delegation: Over-permissive delegation may expose excessive data; organizations should allow more fine-grained entitlements and require human oversight for sensitive tasks. Insufficiently specific consent or a lack of explicit boundaries on an agent’s permission may result in agents taking actions users did not believe they had authorized, leading to unhappy customers and attempts to roll back transactions initiated by authorized agents (e.g. purchase chargebacks).
Looking ahead, organizations should evaluate whether their IAM systems can do the following:
Provide Visibility
Computer Using Agents (CUA) detection
Enterprise agent discovery
Agent platform integrations
Agent service accounts
Onboard & Manage Agents
Unique agent identity type
Agent ownership
MCP tools and resources
Provisioning and registration
Policy management
Delegated entitlements
Authenticate & Authorize Agents
OAuth authorization
MCP gateway
Policy enforcement
Agent-specific protocols
Ensure Human Oversight
Explicit consent
Human-in-the-loop authorization
Audit trails
Request & approvals support
Governance & certification
JIT privileges
Policy constraints
Agent/human experiences
Protect Against Threats
Threat detection for AI-based threats
Automated threat response
Admin reviews
By ensuring these capabilities, organizations can lay the groundwork for safely and efficiently integrating AI agents into their environments.
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