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07/06/2026 8:51 pm
TL;DR: AI agents need the same authentication, authorization, and audit foundations as human users, while purpose-built AI security platforms mainly add monitoring and guardrails, according to WorkOS. The article contrasts those approaches for enterprise deployment, and the core issue is that agent security breaks when identity, permissions, and revocation are treated as separate layers rather than one governed system.
NHIMG editorial — based on content published by WorkOS: Noma Security vs WorkOS, a comparison of platforms for securing AI agents and autonomous systems
By the numbers:
- Noma Security says its platform integrates with 80+ AI services and tools to create a unified dashboard for tracking AI usage patterns.
- WorkOS states that its platform maintains 99.99% uptime SLAs for enterprise customers.
Questions worth separating out
Q: How should security teams govern AI agents that act on behalf of users?
A: Security teams should bind each agent to a governed identity, scope its permissions to the requesting principal, and enforce authorization at the application boundary.
Q: Why do AI agents complicate traditional IAM models?
A: AI agents complicate IAM because they can execute actions dynamically across tools and data sources, which makes static permission assumptions less reliable.
Q: What breaks when AI agent access is not tied to lifecycle events?
A: Access becomes orphaned when directory changes, offboarding, or role updates do not revoke agent permissions derived from those identities.
Practitioner guidance
- Anchor agent actions to a governed principal Tie every AI agent request to a stable user or service identity, and make authorization decisions at the resource boundary rather than in a separate monitoring layer.
- Map delegated tool paths before production rollout Document every MCP-connected tool, the identity used to reach it, and the maximum downstream scope each path can reach.
- Couple agent revocation to directory and lifecycle events Ensure that employee offboarding, role changes, and token rotation revoke any agent access derived from that identity without waiting for manual cleanup.
What's in the full article
WorkOS's full comparison covers the operational detail this post intentionally leaves for the source:
- Step-by-step implementation detail for enterprise SSO, Directory Sync, and fine-grained authorization in production systems.
- Platform-specific handling of user-to-agent delegation, including how permissions are inherited and revoked.
- Audit logging and compliance features that help teams reconstruct agent activity after an incident.
- Deployment trade-offs for teams deciding whether to extend existing identity controls or add a specialised AI security layer.
👉 Read WorkOS's comparison of Noma Security and agentic identity foundations →
Agentic security platforms vs identity foundations: what teams miss?
Explore further
08/06/2026 8:33 am
Agentic security still collapses into identity governance. The article is right to separate monitoring from access control, because watching an agent is not the same as governing it. AI systems acting in production need authenticated principals, scoped permissions, and revocation that follows the actor rather than the alert stream. The implication is that teams that treat agent security as a standalone category will duplicate controls and still miss the real authorization boundary.
A few things that frame the scale:
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to AI Agents: The New Attack Surface report.
- In the same research, 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
A question worth separating out:
Q: Should organisations use separate controls for AI agents and human users?
A: Most organisations should not build separate trust models unless the agent truly needs a distinct identity boundary. The better pattern is to extend established IAM, authorization, and audit controls so they apply consistently to humans, service identities, and agents. Separate layers can create policy drift, duplicate administration, and inconsistent enforcement.
👉 Read our full editorial: Agentic security platforms still depend on identity foundations
