What should security and GRC teams do before approving a GenAI workflow?

Why This Matters for Security Teams

A GenAI workflow should not be approved as a simple productivity feature. It can introduce new data flows, third-party services, hidden retention paths, and unpredictable output behaviour that must be governed before use. Security and GRC teams need to know what the workflow can ingest, what it can return, and which controls are actually enforceable. Guidance from the NIST AI 600-1 GenAI Profile reinforces the need to evaluate generative AI risks through lifecycle governance, not only after deployment.

The common mistake is to focus on the model itself while ignoring the workflow around it. Approval should cover the business purpose, the allowed datasets, the identity boundaries, and the review process for unsafe or inaccurate outputs. If the workflow can reach sensitive repositories, ticketing systems, code, or customer data, then the approval decision is really an access and risk decision as much as an AI decision. Current guidance also suggests aligning the workflow with established control baselines such as ISO/IEC 27002:2022 Information Security Controls, especially for data handling, supplier oversight, and logging.

In practice, many security teams encounter GenAI risk only after the workflow has already been wired into sensitive systems without a clear owner, audit trail, or containment model.

How It Works in Practice

Approval works best when security and GRC teams treat the GenAI workflow like a governed service with defined inputs, outputs, and accountability. That means documenting the workflow architecture, the hosting location, the model or provider involved, the external APIs used, and the identity model that authorises access. It also means establishing what the workflow is allowed to see and do, including whether it can retrieve internal knowledge, call tools, create records, or act on behalf of a person.

A practical review usually covers:

• Data classification for prompts, retrieved content, logs, and generated outputs.
• Owner assignment for the workflow, the model, and each external dependency.
• Identity and access rules for users, service accounts, and agent-like execution paths.
• Monitoring thresholds for unsafe output, abnormal usage, and policy violations.
• Escalation paths for incidents, drift, misuse, and model or supplier changes.

Where GenAI touches identity or non-human access, teams should ask whether a human user, service account, or automated agent is actually making the request, because the approval evidence changes depending on who or what is executing. That becomes especially important when a workflow can copy sensitive content into prompts or expose information through retrieval-augmented generation. The NIST AI RMF is useful here because it frames AI governance as a continuous risk process rather than a one-time sign-off, and the GenAI Profile helps teams translate that into operational checks. These controls tend to break down when the workflow is embedded in a fast-moving development pipeline and no single team can stop release for missing documentation.

Common Variations and Edge Cases

Tighter approval often increases review time and slows experimentation, so organisations must balance innovation velocity against the cost of unmanaged AI exposure. Best practice is evolving for agentic and retrieval-heavy workflows, and there is no universal standard for every environment yet. The right approval depth depends on whether the workflow is public-facing, internal-only, or connected to regulated data, production systems, or privileged identities.

One edge case is a low-risk prototype that later becomes a production dependency. In that situation, the original approval often becomes stale because the data scope, user base, and tool access have changed. Another edge case is a vendor-hosted service where the model owner cannot fully explain retrieval, retention, or subprocessor behavior. In those cases, GRC teams should require contract terms, logging expectations, and clear exit conditions before approval.

For organisations operating under strong control frameworks, current guidance suggests mapping the workflow to ISO/IEC 27002:2022 Information Security Controls while using the NIST AI governance profile to test whether the approval remains valid after changes. Where a workflow can surface secrets, customer records, or regulated content, security teams should also consider whether additional human review is needed before outputs are trusted or forwarded. The hardest failures usually appear when a “temporary” GenAI pilot is promoted into production without revisiting ownership, access, and monitoring.

Related resources from NHI Mgmt Group

• How should security teams prepare data access governance before enabling GenAI tools?
• How should security teams validate AI output before it affects access or workflow decisions?
• What should IAM teams verify before approving a sovereign security platform?
• What should security teams measure before approving multi-model routing?