What should security teams do first when GenAI is outside traditional model risk management scope?

Why a governed control point comes first

When GenAI falls outside traditional model risk management, the first job is not to decide every policy exception. It is to create a control point that makes model and agent activity visible, attributable, and enforceable. Without that choke point, teams are approving unknown usage patterns, not governing them.

A governed gateway changes the security posture immediately because it converts shadow usage into managed traffic. It gives security, platform, and risk teams a place to observe prompts, responses, tool calls, destinations, and blocked events before they argue about scope, ownership, or acceptable use.

This is also where AI Agent Authorisation Guide becomes practical: the control point is what lets policy decisions be evaluated per request rather than as a static policy statement. For agentic traffic, that matters more than drafting broader exceptions because the agent’s authority can change with task, context, and tool use.

What the control point must establish

The minimum output is inventory and control, not perfection. Security teams need a reliable record of which models, agents, users, applications, and integrations are actually in use, what they are sending, and where requests are going. That visibility supports usage attribution, scoping, and later exception handling.

The same point should also enforce basic guardrails: filtering sensitive content, constraining outbound destinations, and blocking high-risk requests when the request pattern is clearly outside approved behaviour. If the platform cannot do all three, it should at least log consistently and fail in a way that preserves evidence for follow-up.

Permission-Aware RAG Guide fits here because retrieval and response pathways often become the first place where policy breaks down. If the application can retrieve data the user or agent should not see, the problem is already operational, not theoretical.

The control point also needs a clear boundary for approval. Route traffic through the governed path first, then decide which use cases deserve exception handling, tighter policy, or redesign. That sequence prevents teams from writing scope exceptions before they can prove what the system is doing.

How to use the telemetry without creating false comfort

Telemetry is useful only if it is detailed enough to support a decision. Log the identity of the calling workload or user, the model or agent, the request destination, the action requested, and whether the request was allowed, transformed, or blocked. That gives you enough context to separate experimentation from production use and to spot uncontrolled integrations.

At the same time, do not treat logging as the control itself. Logging without enforcement still leaves the programme documentary rather than operational. The point is to make policy testable against real traffic, then use the resulting evidence to decide what can be approved safely.

NIST AI 600-1 GenAI Profile is useful here because it reinforces governance, testing, and incident handling for generative AI systems. It supports the idea that teams should establish operational controls and evidence before they rely on policy language alone.

Where agents can call tools or invoke downstream systems, the first telemetry review should ask whether those actions are observable, bounded, and attributable. If not, the right response is to narrow the action set, not to widen policy language.

Practitioner Guidance

What to prioritise: Build one governed ingress path for GenAI traffic and require all known models and agents to pass through it before broader policy work begins. That gives you a single place to measure adoption, discover hidden use, and apply consistent guardrails.

What to verify: Confirm that the control point records enough context to support action, not just detection, including source, destination, request type, outcome, and the business owner or application owner where available. If you cannot trace a request back to a responsible system or team, you do not yet have workable governance.

Decision rule: If a GenAI use case cannot be routed, logged, and selectively blocked, treat it as ungoverned until the integration is redesigned. If it can be observed but not constrained, treat it as a temporary visibility gain, not an acceptable steady state.

Practitioner takeaway: The first control should make GenAI traffic governable in real time, because inventory and exceptions are only credible once the programme can see and shape actual usage.

Related resources from NHI Mgmt Group

• How should security teams scope a third-party risk management program?
• How should security teams implement AI third-party risk management in environments where employees adopt tools outside procurement?
• How should security teams implement model risk management for high-stakes AI decisions in production?
• How should security teams prepare for GenAI adoption without assuming model prompts are the only risk?