Questions a CIO Should Ask Before Approving an In-House AI Control Plane Build

Scope and requirements: what exactly is being built?

Most weak proposals fail at the first question: what is in scope? Ask the team to name the AI system types and components the layer covers. NIST's work on control overlays for AI systems treats components such as training and test data, model weights, and configuration settings as distinct, and a proposal should be equally specific about what it touches.

Ask how the highest-risk use context was identified. NIST's AI Risk Management Framework says the risks judged highest in a given context of use call for the most urgent prioritization and the most thorough risk management. A sound answer ranks use contexts and sequences the build accordingly. A warning sign is a plan to cover everything at once, or a scope defined only by the tools the team already uses.

Ask which existing controls the design depends on. NIST notes that AI security is closely intertwined with the security of the underlying IT infrastructure, and its overlay approach assumes access control, account management, authentication, configuration management, and incident response already exist. The proposal should state what it inherits and what gaps it adds. Finally, ask whether a risk and impact assessment was completed before the build-versus-buy decision, and when it will be revisited. A sound answer has a date and a decision point attached.

Runtime policy enforcement: in the path or after the fact?

The most consequential design question is where the enforcement point sits. A control that sits in the execution path can act before a model response, agent action, or tool call takes effect. A monitoring layer can only report afterward. Both have uses, but only the first enforces policy. Ask the team to draw the request path and mark where a decision is made.

Then ask which actions the layer can take: allow, redact, block, rate limit, and escalate for human approval. For each one, ask what triggers it, what the user or agent experiences, and what is recorded. For escalation, ask whether the action is held until a person decides, and how approvers are identified and recorded. A warning sign is escalation that sends a notification while the action proceeds.

Ask what happens when the enforcement point fails or cannot reach its policy store. Failing open and failing closed are both defensible, but the choice should be explicit, set per use context, and approved by the business owner. Ask also how policy is versioned and tested before release. OWASP's guidance to apply least privilege and defense-in-depth is a useful test: a single enforcement point with broad permissions is a concern in itself.

Evidence requirements: evidence is not logging

Activity logs show that something happened. Evidence shows that a control was in force, made a decision, and acted on it. NIST AI RMF MAP 4.2 asks that internal risk controls be regularly monitored and that controls be applied and documented. That language supports asking the build team to show both the control and its documentation.

Ask the team to walk through one blocked or redacted interaction and produce the record. A sound record identifies the policy version in force, the input context, the decision, the action taken, any human approver, and a timestamp. It is retained for a defined period, protected against alteration, and retrievable by an auditor without engineering help. A warning sign is raw logs that someone must interpret and reconstruct.

Ask how evidence maps to the frameworks that apply to your organization, such as NIST AI RMF and, where relevant, the EU AI Act. The mapping should be control by control, with an owner who updates it as guidance changes. NIST's overlays are still at concept-paper stage, so expect that work to continue. Also ask how effectiveness is tested. MEASURE 1.2 calls for regular assessment and updating of controls, so a proposal without a test method for each control is incomplete.

Coverage, reliability, and long-term ownership

Coverage is where internal estimates drift. Ask how many models, agents, tools, MCP servers, and coding assistants are covered on day one, and what it takes to add the next one. A sound answer gives a repeatable onboarding process and a named owner. A warning sign is a design tied to one provider's API or one agent framework, since the AI estate will change during the life of the build.

Set operational requirements yourself rather than accepting the team's defaults. Define the availability target for a component that sits in the execution path, the latency budget it may add, and how both will be measured under production load. Ask whether it can run self-managed in your cloud, how data residency is enforced, and how retention is configured for each class of evidence. Each answer should come with a test, not an assurance.

Finally, ask about people. Who owns the layer after launch, how many engineers are assigned, who is on call, and who assesses control effectiveness on an ongoing basis? The framework guidance points to recurring assessment, not a one-time build. A proposal that budgets for construction but not for years of maintenance, evidence upkeep, and policy updates has understated its cost.

One reference point for production runtime control

If you want a reference for what production runtime control involves, Trussed AI is one. It enforces policy in the execution path across models, agents, tools, MCP servers, AI sites, and coding assistants, with allow, redact, block, rate limit, and escalate for human approval, applied to individual interactions and tool calls. Enforcement produces continuous audit-ready evidence mapped to HIPAA, NAIC, NYDFS, the EU AI Act, and NIST. It supports self-managed deployment in the customer cloud, with configurable retention and data residency.

Its approved proof points are 18+ months of continuous production in a regulated enterprise, 99.95% uptime on live production traffic, 100+ models under one policy plane, and 30+ GB of audit evidence generated per day. Use these as a yardstick when you ask an internal team what its own design will have to sustain.