Runtime Governance

Defining a RACI Model for AI Agent Runtime Control Ownership

An AI agent runtime control RACI model assigns explicit ownership across discrete runtime functions rather than treating the agent as a single governed entity. At minimum it should separate identity issuance, permission scoping, tool-call authorization, monitoring and audit logging, and revocation, aligning with the policy decision and enforcement separation described in NIST's Zero Trust Architecture guidance and the least-privilege requirements in NIST SP 800-53. Security typically holds accountability for policy definition and audit oversight, platform engineering is responsible for enforcement infrastructure, AI product teams are consulted on functional scope, and compliance is informed of exceptions and incidents.

Explore Runtime Governance Talk to an Expert

Five Runtime Control Points at a Glance

Agent governance is easier to reason about when it is broken into discrete, independently ownable functions rather than one broad "agent governance" category.

Identity Issuance

Establishing an agent's runtime identity, separate from what it is permitted to do.

Permission Scoping

Defining the minimum tool and data access an agent needs, per least-privilege principles.

Tool-Call Authorization

Real-time approval decisions made at the moment an agent invokes a tool or external system.

Monitoring & Audit Logging

Ongoing visibility into agent actions, distinct from the approval decision itself.

Revocation

Removing or reducing agent permissions after grant, treated as its own control point.

Questions to Resolve Before Assigning Ownership

Use the following questions to surface gaps in the current ownership model before mapping roles.

Who is accountable for defining runtime permission scopes versus who is merely consulted on those definitions?
Which team owns tool-call authorization when the tool server is operated by a third party, as in MCP-style architectures?
How is agent identity issuance separated from permission scoping in the current architecture, and who owns each?
What is the documented escalation path when an agent attempts an unauthorized or denied runtime action?
Who owns revocation of agent permissions, and is that distinct from initial permission grant ownership?

As enterprises deploy autonomous agents that invoke tools and access systems at runtime, responsibility for defining, approving, and enforcing those actions is often distributed across security, platform engineering, AI product, and compliance teams without explicit boundaries. NIST's AI Risk Management Framework addresses this gap at an organizational level: its Govern function calls for documented accountability structures for AI risk oversight, but it does not specify how that accountability should be divided across the discrete technical functions that make up an agent's runtime behavior. In practice, this leaves ambiguity over who owns a given control at the moment it matters most: when an agent requests access to a new tool, when a permission grant needs periodic review, or when an unexpected action needs to be traced back to a specific decision point. A RACI model addresses this by mapping ownership to individual runtime functions rather than to the agent program as a whole.

Discrete Runtime Functions That Require Distinct Ownership

Treating "agent governance" as a single ownership category tends to obscure the fact that different runtime functions carry