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To build an AI security policy, define which AI uses it covers, assign owners and approval authority, inventory the systems and data involved, and set risk-based requirements for approval, security review, monitoring, incident response, and retirement. The policy should govern more than employee use of chat tools: include AI your organization builds, buys, and receives as part of other software. NIST’s AI Risk Management Framework (AI RMF) is a voluntary organizing framework—not a universal legal requirement—and its Generative AI Profile offers practical guidance for putting those decisions into operation.
What should an AI security policy include?
An effective policy turns the organization’s risk tolerance into decisions people can follow and controls teams can demonstrate. It should establish who is accountable, what systems and uses are in scope, how risk is assessed, what is permitted, and what happens when a system changes or causes harm.
At a minimum, include these policy components:
- Purpose, scope, and definitions: Specify whether the policy applies to employee and contractor use, internally developed systems, procured products, and AI features embedded in software. Clarify what the organization means by “AI system” and “generative AI” for policy purposes.
- Roles and decision rights: Name an executive sponsor and operational owners. Define who may request, assess, approve, deploy, pause, and retire a system.
- Inventory and context: Require teams to record systems, intended uses, owners, providers, data, integrations, and important dependencies—including embedded AI.
- Risk tiers and approval gates: Set evaluation and approval requirements according to a use case’s context and potential impact, before deployment and during operation.
- Acceptable use and data handling: State which uses and tools are allowed, what information can be entered, and how access, retention, logging, sharing, and human review work.
- Security and supplier controls: Require proportionate threat modeling, secure integration, access controls, testing, vendor review, and management of dependencies.
- Operations and accountability: Set monitoring, reporting, response, recovery, retirement, documentation, and policy-review expectations.
The policy itself should establish requirements and authority; procedures, standards, and checklists can provide the implementation detail. That separation makes it easier to update a testing checklist or review workflow without silently changing the organization’s risk rules.
How do you decide the policy’s scope and ownership?
Start by naming the systems, people, and decisions the policy governs, then assign accountable owners across the organization. A policy limited to employees using public chat tools can miss AI embedded in a purchased service or a model that an internal team connects to operational data.
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Set scope across the organization
Decide explicitly whether the policy covers:
- AI tools employees, contractors, and consultants use for work, including tools accessed through personal or organizational accounts.
- Systems the organization develops, fine-tunes, hosts, or integrates.
- AI features in products and services the organization purchases or already uses.
- Open-source components, third-party APIs, plugins, and models incorporated into a larger system.
For any excluded category, state the reason and who can authorize an exception. An exemption should not become a way for a team to avoid review merely because a capability arrived inside an existing product.
Assign roles and decision rights
Appoint an executive sponsor who can resolve cross-functional disputes and ensure the policy has authority. Name operational owners from security, IT, legal or compliance, privacy, procurement, and the relevant business teams. The same person may hold more than one role in a smaller organization, but the responsibilities should remain explicit.
Define who can request a system, assess it, accept residual risk, authorize deployment, operate it, pause it, and retire it. Decide when approval can be delegated under set thresholds and when a higher-level or cross-functional decision is required. NIST’s Generative AI Profile recommends organizational roles and communication procedures; clear decision rights turn that principle into an actionable approval path.
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Require an inventory entry before a system is approved for organizational use, and include AI embedded in purchased software as well as systems built in-house. The inventory should give reviewers enough context to judge what the AI does, what it can access, and who is responsible for it.
Capture, at a minimum:
- System or service name, purpose, business owner, intended users, and lifecycle status.
- Model and provider, hosting arrangement, and relevant APIs, plugins, libraries, fine-tuned models, or other components.
- Input and output data types, including whether sensitive information may be involved.
- Connections to organizational systems, data stores, tools, or external services, and the access each connection receives.
- Whether outputs are reviewed by a person, used to make consequential decisions, or allowed to trigger actions.
- Teams responsible for security, operation, monitoring, and incident escalation.
Record the system’s context, not just its product name. A drafting assistant with no sensitive integrations presents a different review question from an AI feature that retrieves confidential records or can initiate an operational action. NIST’s profile calls for an AI inventory and attention to downstream impacts, third-party plugins, and value-chain risks.
Set criteria for any inventory exemptions and assign someone to review them. Maintain the record as systems change; an inventory that captures only initial procurement can become inaccurate when a team adds an integration, changes the use, or adopts a new model or provider.
How should risk tiers and approval gates work?
Classify use cases by their context and potential impact rather than applying one approval rule to every AI tool. Use that classification to determine the minimum review, assurance, and authorization a system needs before launch and while it is in operation.
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Factors for a risk assessment include:
- Sensitivity of the information supplied to the system and returned in its outputs.
- Connections to other systems, the scope of access, and whether outputs can trigger actions.
- Potential consequences for people, the organization, or its operations if outputs are wrong or manipulated.
- Potential for malicious use, newly introduced vulnerabilities, or misuse of the system’s capabilities.
- How dependable the system is for the specific context in which it will be used.
Translate those factors into tiers that fit your organization; the NIST material does not prescribe a universal set of tier names or thresholds. For each tier, specify a minimum evaluation, required reviewers, who can authorize use, and what ongoing checks apply. Define a go/no-go decision and how the organization records any accepted residual risk.
Make approval specific to the proposed use, configuration, data, and integrations. A material change—such as adding a sensitive data source, changing the provider, or allowing the AI to take action—can alter the risk enough to require a new assessment. NIST recommends evaluating relevant capabilities and the robustness of safety measures before launch and on an ongoing basis.
How do you govern employee use of generative AI?
Write rules that tell people which tools and accounts are approved, what data they may enter, and when a human must check the output. Address employees and third-party personnel, and distinguish low-impact drafting from use that connects AI to sensitive information or operational actions.
An acceptable-use section should state:
- Which tools, accounts, and configurations are approved for work, and how to request review of a new tool.
- What information may be entered into each approved tool, including any restrictions on sensitive or confidential data.
- How access is granted and removed, and what logging, retention, and sharing rules apply.
- When generated content needs human review before it is relied on, shared externally, or used to make a consequential decision.
- Which uses are prohibited or require additional approval, and how workers can report uncertainty or an unexpected result.
Address proprietary and open-source tools, as well as third-party personnel, rather than assuming the same safeguards apply to every interface. NIST’s profile recommends acceptable-use policies for interfaces and human-AI configurations. The organization should make the practical rules available to people when they select or use a tool, not leave them buried in technical review documents.
What security risks should technical reviews address?
Require technical reviewers to examine the threats relevant to each use case, including how data, model components, integrations, and system availability could be affected. Generative AI can be manipulated through prompt injection, and a single instruction or filter should not be treated as a complete defense.
Threat-model the full system
Review how the model interacts with users, retrieved material, tools, plugins, APIs, and organizational data. NIST’s Generative AI Profile describes direct prompt injection, where malicious content is supplied directly, and indirect prompt injection, where malicious content is placed in material an integrated application retrieves. Either may influence system behavior in ways the system’s operator did not intend.
Also consider data poisoning and attacks such as adversarial examples, membership inference, and model extraction. Which threats are relevant depends on the system, data, deployment, and access paths; the policy should require an assessment rather than assume every system has the same exposure.
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Set proportionate technical requirements
Require safeguards appropriate to the system’s risk, such as least-privilege access, separation of sensitive data, secure integration, and testing of relevant attack paths. Review protections for the availability of the service and the integrity and confidentiality of relevant data, code, models, and other artifacts. Consider where components are hosted and what happens if an external service, plugin, or dependency changes or becomes unavailable.
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For generative systems, define pre-deployment and recurring evaluation requirements. The depth of testing should reflect the risk tier and can include independent review or red-teaming where warranted. Record the evaluation scope, results, significant limitations, and who accepted any remaining risk. Do not treat a successful test as proof that a system is safe in every future context; changes in use, components, or threat conditions may require reassessment.
What should vendor and dependency reviews cover?
Review providers and components before procurement or significant changes, and make the review specific to the system’s data flows and intended use. A supplier’s general security claims do not answer every question about how an AI service handles organizational data or how the organization will learn about a material change.
Ask reviewers to examine:
- Data handling, access, retention, and any use of organizational inputs or outputs.
- Security practices and the information the provider supplies about incidents or vulnerabilities.
- How the provider communicates model, service, or dependency changes that could affect the approved use.
- Relevant privacy, intellectual-property, and embedded-AI considerations.
- Dependencies such as APIs, libraries, plugins, and fine-tuned models, including which party is responsible for monitoring them.
- Contract terms and escalation routes needed to support the organization’s review, incident response, and ongoing oversight.
Record the reviewed provider, components, material terms, and the conditions of approval in the inventory or linked records. NIST’s profile recommends updating AI acquisition and vendor due diligence to account for privacy, security, intellectual property, embedded AI, monitoring, and incident or vulnerability information.
How should monitoring and incident response work?
Give users a clear way to report unexpected outputs, possible data exposure, unsafe integrations, and service failures. Assign a team to receive reports and define how it will contain, escalate, investigate, recover from, and document an AI-related incident.
Adapt the organization’s incident process to account for AI systems and their dependencies. The response plan should identify:
- Who receives an initial report and who decides whether to restrict, pause, or disable a system.
- How affected data, accounts, integrations, or model components can be contained or isolated.
- How the organization communicates with internal stakeholders and relevant providers.
- How service or operations can be recovered safely, and what conditions must be met before resuming use.
- What records are kept, including errors, near misses, impacts, decisions, and lessons learned.
Monitor system behavior and third-party services in proportion to their risk, and make a route for users to report issues part of normal operations. NIST recommends assigning incident communication responsibilities, planning for response and recovery, monitoring on an ongoing basis, and tracking errors and near misses. Use after-action reviews to identify whether the issue calls for a control change, a new assessment, or a policy update.
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How do you review, update, and retire approved systems?
Set a review cadence and event-based triggers so that approval remains connected to how the system is actually used. A periodic review alone may not be enough when a significant model, provider, integration, or use change happens between scheduled reviews.
Define reassessment triggers such as:
- A major model, provider, or service change.
- A new integration, plugin, data source, or capability to take action.
- A material change in the purpose, users, or consequences of the system.
- A security incident, serious error, near miss, or negative impact.
- New requirements that apply to the organization or its deployment.
For retirement, specify how the organization disables the system, removes access and integrations, and handles associated data and artifacts. Record who authorizes decommissioning and how the inventory is updated. NIST’s profile includes safe deactivation and decommissioning as part of governance.
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NIST’s AI RMF provides an AI-focused way to organize governance through four functions: Govern, Map, Measure, and Manage. NIST’s Risk Management Framework (RMF) describes a seven-step lifecycle for information-security risk management: Prepare, Categorize, Select, Implement, Assess, Authorize, and Monitor.
These frameworks can be used as complementary views. The AI RMF helps an organization frame AI-specific risks, context, and responsibilities; the RMF lifecycle helps connect risk decisions to system controls, assessment, authorization, and monitoring. Both need to be tailored to organizational needs and applicable constraints. The AI RMF is voluntary; it should not be presented as a legal requirement for every organization. NIST’s AI RMF overview has described a revision as underway and referenced a 2026 critical-infrastructure profile concept note, so check NIST’s current status before relying on a particular version or revision schedule.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you choose an approval model?
Choose an approach that gives the right people decision authority without creating gaps between approval and operational responsibility. The best fit depends on the organization’s size, risk tolerance, and ability to provide consistent review.
| Decision area | Approach options | What to decide |
|---|---|---|
| Scope | Employee use only; or employee use plus developed, procured, and embedded AI | Which systems and personnel must be inventoried and governed |
| Risk basis | One uniform approval rule; or risk tiers tied to impact and context | How review depth and approval authority change with risk |
| Governance | Centralized approval; delegated approval under thresholds; or a hybrid | Who can decide, and which cases require escalation |
| Assurance | Self-attestation; internal security review; independent assessment; or a mix | What evidence is appropriate for each risk tier |
| Operational coverage | Launch approval alone; or lifecycle controls for changes, monitoring, incidents, and retirement | How approval stays valid as the system evolves |
| Evidence burden | Documentation requirements and review frequency set by the organization | What teams must retain and when records are revisited |
A hybrid model can delegate routine, lower-risk approvals while reserving higher-impact or unusual cases for cross-functional review. Whatever model you choose, state its thresholds and escalation route in the policy or a controlled procedure so that teams do not have to guess where a request belongs.
How do you put the policy into practice?
Build the policy around a repeatable path from request to retirement. The sequence below creates the core governance machinery without assuming every system needs an identical review.
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- Assign the sponsor and owners. Name the executive sponsor, operational roles, and people authorized to approve, pause, and retire systems.
- Set scope and definitions. Cover employee use, internal development, procurement, embedded AI, and third-party personnel as appropriate; state and govern any exceptions.
- Establish the inventory. Decide which information teams must record and require entries for systems before approval, including relevant components and integrations.
- Define risk tiers and gates. Choose context-based criteria, minimum evaluations, approval authority, and conditions that trigger reassessment.
- Write acceptable-use and data rules. Identify approved tools, permitted data, access and retention expectations, and when human review is required.
- Connect security and vendor reviews. Require threat modeling, proportionate testing, supplier due diligence, and assessment of dependencies before procurement or significant changes.
- Prepare operations and incident handling. Set monitoring, reporting, containment, escalation, recovery, and recordkeeping responsibilities.
- Set review and retirement procedures. Establish cadence and change triggers, then define how to disable systems, remove access, handle artifacts, and update the inventory.
- Keep evidence of decisions. Retain role assignments, inventory records, threat models, evaluation results, approvals, incident records, and periodic review outcomes.
Evidence makes governance auditable and helps teams apply the policy consistently. NIST’s profile emphasizes documented roles, inventories, evaluations, incident records, and ongoing review; the organization should decide how those records fit its existing security and compliance processes.
What legal and sector-specific limits should you check?
A general AI security policy does not determine which laws, regulations, contracts, or sector controls apply to a particular organization. Applicability depends on jurisdiction, industry, information handled, and deployment context, so have the relevant legal, privacy, compliance, and security owners assess those obligations before approving a use.
NIST’s AI RMF can help structure voluntary risk management, but using it does not by itself establish that an organization has met every applicable requirement. Document any additional obligations and connect them to the system’s approval, operational controls, and review process.
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Frequently Asked Questions
Is the NIST AI Risk Management Framework mandatory?
NIST describes the AI RMF as intended for voluntary use. Whether an organization has separate legal, contractual, or sector-specific obligations depends on its circumstances, so framework use should not be treated as a substitute for determining those requirements.
Does an AI security policy apply to AI features inside ordinary software?
It should address embedded AI if employees or systems can use that capability for organizational work. Ask software owners and procurement teams to identify those features and route them through the organization’s inventory and review process.
What records should we keep to show the policy is working?
Retain records that demonstrate how decisions were made and controls operated, such as role assignments, inventory entries, threat models, evaluation results, approvals, incident records, and periodic reviews. The organization can align retention and storage with its existing governance processes.
How often should an AI security policy be reviewed?
Set a recurring review cadence in the policy and supplement it with event-driven reviews. Significant changes to a model, provider, integration, use, or applicable requirements—and incidents or serious near misses—are reasons to reassess the relevant system and controls.
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