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03/06/2026 11:01 am
TL;DR: AI governance policies only reduce risk when they are enforced through identities, access, transactions, and evidence across real business systems, according to SafePaaS. Without a federated operating model, enterprises get policy at the top and fragmented controls below, leaving no reliable proof that guardrails are working in practice.
NHIMG editorial — based on content published by SafePaaS: Federated governance is becoming the enforcement layer for AI governance
Questions worth separating out
Q: How should security teams govern AI identities across business systems?
A: Security teams should govern AI identities through the same access and evidence model used for people and other non-human identities.
Q: Why do AI governance policies fail without federated enforcement?
A: They fail because policy documents do not control runtime access on their own.
Q: What breaks when segregation of duties is not applied to AI actions?
A: AI can accumulate incompatible privileges across steps in a business process even when no single permission looks risky on its own.
Practitioner guidance
- Define a shared control model for AI identities Place AI agents, machine identities, and service accounts inside the same identity and access governance framework used for workforce access, so policy application is consistent across actors.
- Embed PBAC in AI-initiated workflows Require policy-based access control checks before AI systems can create suppliers, approve requests, or trigger financial activity in ERP, SaaS, or cloud platforms.
- Extend segregation of duties to autonomous processes Model SoD across the full transaction path, including request, routing, approval, and downstream system actions, so AI cannot accumulate toxic privilege combinations.
With 69% of security leaders saying identity management must fundamentally shift to address agentic AI systems, the governance gap is already visible in programme priorities, not just architecture diagrams?
👉 Read SafePaaS's analysis of federated governance for AI identities and access →
Explore further
03/06/2026 11:03 am
Federated governance is the missing enforcement layer for AI governance. AI policy without identity and transaction enforcement remains an aspiration, not a control system. The article is right to separate policy design from operational execution, because enterprises do not govern risk through statements alone. They govern it through identities, access decisions, approvals, and evidence trails that hold up under challenge.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How do audit teams prove that AI-related controls are working?
A: They need a traceable chain from policy to approval to access change to transaction outcome. If that chain is missing, the organisation can describe governance but not demonstrate it. Strong evidence capture lets audit teams test whether AI activity stayed inside approved limits and whether exceptions were handled consistently.
👉 Read our full editorial: Federated governance is becoming the enforcement layer for AI governance
