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Why an AI policy is only the starting point
A policy can state what is permitted, prohibited, or subject to review. But it cannot govern an AI capability that the organization has not identified, nor can a broad rule determine whether a particular use is appropriate without knowing its users, data, permissions, connected applications, and role in a business process.
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That gap can appear in obvious AI systems as well as in features embedded in software or workflows. The article’s “rules and rails” framing treats the policy as the rules and the working processes and controls as the rails that help keep actual use aligned with them. The practical point is to assess capabilities in their organizational context, not treat a model or application as an isolated object. [TechRadar Pro’s article]
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What ongoing governance looks like
NIST’s AI Risk Management Framework (AI RMF) treats governance as a continuing part of risk management, not a one-time approval. Its AI RMF Core says: “Attention to governance is a continual and intrinsic requirement for effective AI risk management over an AI system’s lifespan and the organization’s hierarchy.” The framework identifies practices organizations can use to put that principle into operation. [NIST AI RMF Core]
- Inventory AI systems and capabilities. Establish a way to identify what is in use, where it is used, and which organizational processes depend on it.
- Document requirements and risks. Identify relevant legal and regulatory requirements, then choose risk-based activities and controls suited to each use.
- Assign roles. Make clear who approves a use, who operates it, who monitors it, and who handles concerns or changes.
- Monitor and review. Plan periodic review so controls can be reconsidered as systems, access, data, or business purposes change.
- Keep useful records. NIST notes that “Documentation can enhance transparency, improve human review processes, and bolster accountability in AI system teams.” [NIST AI RMF Core]
These are governance practices, not a guarantee that every risk will be prevented. Their value is that they give an organization a repeatable way to find relevant uses, make decisions, and revisit those decisions.
Rank #2
Assess the capability in its business and access context
A useful review asks more than “Which AI product is this?” It considers how the capability is deployed and what authority it has. For each identified use, document the relevant context:
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- Purpose and process: What task does it support, and where does its output enter a business decision or workflow?
- People and ownership: Who uses it, who is accountable for the use, and who can approve or stop it?
- Data: What information can users provide or the system access, and what rules govern that information?
- Identity and permissions: Which user or service identities can invoke it, and what actions or resources can those identities reach?
- Connections: Which applications, files, services, or other tools are linked to the capability?
- Change conditions: What changes in purpose, permissions, data, or connected systems should trigger reassessment?
This context-based approach helps distinguish uses that may look similar at the product level but carry different exposure because they serve different processes or have different access. It also gives reviewers concrete facts to use when deciding what controls are proportionate.
Rank #3
Make the policy actionable with a repeatable process
- Set the rules. State acceptable and prohibited uses, approval expectations, and escalation routes in language employees and system owners can follow.
- Find uses and owners. Create an inventory process that captures AI capabilities, business purpose, users, responsible owners, and relevant connections.
- Review by risk and context. Assess data, permissions, business impact, and applicable requirements before approving or changing a use. Match review effort and controls to the risk rather than applying an identical process to every use.
- Implement controls and records. Put approved safeguards into the relevant workflow and document decisions, ownership, and review expectations so people can check how the use is governed.
- Monitor and revisit. Schedule periodic reviews and define events that prompt another look, such as a change in access, purpose, connected application, or applicable requirement.
NIST’s AI RMF is a voluntary framework that can help organizations structure this work; it is not a substitute for determining which laws apply. NIST says the framework is intended for voluntary use to incorporate trustworthiness considerations into AI design, development, use, and evaluation. It was released on January 26, 2023, and NIST says AI RMF 1.0 is being revised, so consult its live page for current status. [NIST AI Risk Management Framework]
Voluntary guidance and legal obligations are not the same
Framework guidance and legislation have different authority. The AI RMF offers voluntary guidance. The EU AI Act is legislation, with implementation, supervision, and enforcement involving the European Commission’s AI Office and national authorities. Its application and obligations depend on the system’s role and risk classification; it should not be described as imposing the same requirements on every organization or every AI use. Organizations need to assess whether and how the Act applies to their circumstances, rather than infer a universal duty from the existence of the law. [European Commission: Governance and enforcement of the AI Act] [EUR-Lex: Regulation (EU) 2024/1689]
| Question | Policy alone | Operational governance |
|---|---|---|
| What is covered? | Rules and expectations stated in a document | Identified capabilities, users, permissions, data, connections, and business context |
| Who is responsible? | May state general responsibilities | Defines owners and roles for approval, operation, monitoring, and escalation |
| When is it reviewed? | Can be treated as a one-time publication or approval | Includes monitoring, periodic review, and reassessment when context changes |
| What is its authority? | An organizational rule; its effect depends on how the organization implements it | Processes that make internal rules workable alongside applicable legal obligations |
The comparison is about function, not a choice between policy and operations: operational controls need rules to implement, while policy needs working processes to have practical effect.
Questions leaders should be able to answer
- Can we identify the AI capabilities used in important workflows, including embedded features?
- For each use, do we know its purpose, owner, users, data, permissions, and connections?
- Are approval and escalation responsibilities clear enough that a change or concern reaches the right people?
- Do monitoring and review happen on a planned basis, and do changes in context prompt reassessment?
- Have we distinguished voluntary guidance from legal requirements that apply to our specific role and use?
If these questions do not have clear answers, the next step is not simply to expand the policy. It is to establish the inventory, ownership, review, and monitoring processes needed to make the policy govern real use.
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