AI Use Policy

An AI use policy defines which AI tools, data types, and business activities are allowed inside an organisation. It turns broad governance principles into practical boundaries for employees, contractors, and teams, especially where output review, restricted inputs, and escalation are needed.

Expanded Definition

An AI use policy is the operating rule set that tells people and teams what AI tools may be used, for which tasks, and under what safeguards. In practice, it translates governance intent into day-to-day boundaries for acceptable prompts, approved data sources, human review, and escalation when outputs affect customers, systems, or regulated decisions.

Definitions vary across vendors and internal governance teams, but the policy is usually broader than a model whitelist. It should address public chat tools, embedded AI features in business software, internal assistants, and agentic workflows that can take actions or access data. It also needs to account for sensitive inputs such as secrets, personal data, source code, and confidential business information.

For security teams, the key distinction is between “permission to use AI” and “permission to use AI in a specific way.” A strong policy separates low-risk experimentation from production use, and it should align with governance models such as the NIST Cybersecurity Framework 2.0 so that AI use is tied to risk management, oversight, and accountability. The most common misapplication is treating an AI use policy as a one-page acceptable-use notice, which occurs when organisations fail to specify data restrictions, approval paths, and review requirements for real business workflows.

Examples and Use Cases

Implementing an AI use policy rigorously often introduces friction in routine work, requiring organisations to weigh productivity gains against tighter review, data handling, and approval steps.

• A marketing team may be allowed to use approved generative AI tools for drafting public content, but prohibited from entering customer lists, campaign performance data, or unreleased product details.
• A software engineering team may use AI assistants for code suggestions, while requiring human review before any generated code is merged into production repositories.
• A finance or legal team may permit AI for summarisation of non-confidential documents, but block use for decisions involving sensitive records, contractual clauses, or regulated disclosures.
• An operations group may be allowed to use an internal AI assistant that connects to enterprise data, provided the assistant cannot execute actions without explicit approval and logging.
• Security teams may require that any AI tool handling identities, tokens, or secrets be reviewed through the same control process used for other high-risk applications, with reference to guidance such as NIST CSF governance practices.

These use cases show that policy design is not just about banning tools. It is about defining where review is mandatory, where data must be excluded, and where an approved tool can operate only under monitored conditions.

Why It Matters for Security Teams

An AI use policy matters because it is often the first control that stops unsafe adoption before it becomes an incident. Without clear boundaries, staff may paste secrets into consumer chatbots, rely on unreviewed outputs for customer-facing decisions, or connect internal systems to AI tools that were never approved for those data classes. That creates exposure across confidentiality, integrity, compliance, and third-party risk.

For security teams, the policy also becomes a bridge between governance and enforcement. It informs identity and access decisions, data loss prevention rules, logging requirements, and approval workflows for new tools. Where agentic AI is involved, the policy should be explicit about tool access, action limits, and human sign-off before execution. Where business units use AI informally, the policy can help distinguish sanctioned usage from shadow AI.

Organisations typically encounter policy gaps only after a sensitive prompt, unsafe output, or unauthorized AI integration has already happened, at which point the AI use policy becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• How should organisations enforce AI policy compliance across employee and agent use?
• What should teams do when AI use is already happening before policy is ready?
• What NHI types do Agentic AI systems typically use?
• When should organizations use MCPs for AI identity management?