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To build an AI security policy for your organization, define which AI uses are covered, assign accountable owners and approval rights, inventory systems and their data flows, and set risk-based requirements for approval, security testing, monitoring, incident response, and retirement. The policy should apply not only to systems your organization develops, but also to employee use and AI embedded in purchased products.
NIST’s AI Risk Management Framework (AI RMF) is a useful voluntary structure for organizing this work, not a universal legal requirement. NIST’s AI RMF overview has indicated that revision is underway and referenced a 2026 critical-infrastructure profile concept note; check the current NIST framework status before relying on version-specific guidance. The policy itself must also be tailored to the laws, contracts, sector requirements, and risks that apply to your organization.
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What should an AI security policy include?
An AI security policy turns an organization’s risk tolerance into repeatable decisions: what may be used, who can approve it, which safeguards are required, and what happens when something changes or goes wrong. It should establish lifecycle controls, not just a list of prohibited prompts or a one-time review before launch.
At minimum, include:
- Purpose, scope, and definitions: Identify covered uses, including employee-facing tools, internally built systems, third-party services, and AI features embedded in other products.
- Ownership and decision rights: Name an executive sponsor and operational owners; specify who can request, assess, approve, deploy, pause, and retire a system.
- An AI inventory: Record systems, use cases, providers, data, integrations, accountable owners, and lifecycle status.
- Risk tiers and approval gates: Set review depth and minimum assurance criteria according to context and potential impact.
- Acceptable-use and data-handling rules: Say which tools and accounts are approved, what data may be submitted, and how access, retention, logging, sharing, and human review work.
- Security requirements: Require proportionate threat modeling, secure integrations, least-privilege access, testing, and protections for confidentiality, integrity, and availability.
- Vendor and dependency review: Assess provider practices, data terms, service changes, incident communication, and material components such as APIs, plugins, and fine-tuned models.
- Monitoring and incident response: Establish reporting routes, containment and recovery responsibilities, recordkeeping, and lessons-learned reviews.
- Review and retirement: Define review intervals and change triggers, plus how to disable a system and handle its access, data, and artifacts when it is no longer approved.
The policy should also state what evidence teams must retain: inventory entries, risk assessments, threat models, evaluation results, approvals, vendor reviews, monitoring records, and incident or near-miss reports. Evidence makes the rules auditable and helps decision-makers revisit a use case when its context changes.
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How should we define scope and accountability?
Start by making the policy broad enough to cover AI wherever it enters the organization, while assigning clear responsibility for each use. A rule limited to tools that employees intentionally sign up for will miss systems purchased by departments, AI capabilities enabled inside existing software, and components introduced by vendors.
Set the boundary
State whether the policy covers:
- AI tools employees, contractors, or consultants use for work, whether accessed through an organization-managed account or another route.
- Systems the organization develops, fine-tunes, hosts, or integrates into its products and operations.
- AI features included in purchased software, cloud services, devices, and services delivered by third parties.
- Supporting components such as APIs, plugins, retrieval sources, libraries, and model artifacts where they affect the system’s security or behavior.
Define any exclusions narrowly and explain who can approve them. A low-impact experiment may warrant a lighter process than a system that accesses sensitive records or can take actions, but an exemption should not silently remove a system from visibility. NIST’s Generative AI Profile, AI 600-1 (July 2024), recommends enumerating generative AI systems in an inventory and considering downstream impacts, third-party plugins, and value-chain risks.
Name owners and decision-makers
Assign an executive sponsor and operational points of contact across security, IT, legal or compliance, privacy, procurement, and the relevant business area. The use-case owner should be accountable for its purpose, users, and operating context; technical teams should own implementation and controls; security and privacy reviewers should assess relevant risks; procurement should coordinate provider due diligence; and legal or compliance should identify applicable obligations.
Write down who has authority to request, assess, approve, deploy, pause, and retire a system. A workable model may centralize higher-risk decisions while delegating lower-risk approvals under documented thresholds. Whatever model you choose, identify the escalation route when reviewers disagree or a system’s risk changes after approval.
How do we inventory AI systems and uses?
Maintain one inventory that captures both the system and the use case. A model name alone is not enough: the same service may be low risk for internal drafting and materially riskier when connected to confidential records or allowed to trigger operational actions.
For each entry, record at least:
- System or service name, business purpose, and accountable business owner.
- Provider, model or service version when known, and whether the system is developed internally, purchased, or embedded in another product.
- Users, deployment environment, hosting arrangement, access method, and lifecycle status.
- Input and output data types, including sensitive or confidential information, and applicable retention or logging arrangements.
- Integrations, connected data sources, APIs, plugins, and permissions, including whether outputs can initiate actions or influence consequential decisions.
- Risk tier, assessment date, approval status, required controls, and the teams responsible for monitoring.
Set a process for discovery as well as registration. Require teams to identify AI capabilities during procurement, software changes, and project intake; ask service owners to disclose embedded features; and provide a simple route for staff to report tools they encounter. Define any inventory exemption criteria explicitly, record the rationale, and assign an owner to revisit it. Update entries when a provider, model, data source, integration, or intended use changes.
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How should we classify risk and approve AI use?
Use tiers that reflect the system’s context and potential impact, rather than applying one approval rule to every use. The tiers below are an illustrative internal design, not official NIST categories; define thresholds that fit your organization’s risk tolerance and obligations.
| Illustrative tier | Example context | Typical decision gate | Assurance emphasis |
|---|---|---|---|
| Lower | Limited-purpose drafting or summarization with non-sensitive material and no operational system access. | Registered use and approval under a documented standard, with clear user and data rules. | Confirm approved tool and account, data boundaries, human review expectations, and basic monitoring. |
| Moderate | Use involving internal information, meaningful integrations, or outputs that guide business work. | Documented risk review by the business owner and relevant security, privacy, or IT reviewers before deployment. | Assess data flows, permissions, provider terms, threat scenarios, and test results; define ongoing review. |
| Higher | Sensitive data, consequential decisions affecting people, external-facing outputs, or actions connected to important systems. | Explicit go/no-go authorization by designated decision-makers before launch and after material changes. | Deeper threat modeling and evaluation; consider independent review or red-teaming proportionate to risk; define tight monitoring and response procedures. |
For every tier, set a minimum approval record: intended purpose, owner, affected users or groups, data and integrations, identified risks, selected controls, unresolved issues, approver, and conditions of use. Define what constitutes a no-go, what mitigations can make a use acceptable, and who may accept residual risk. An approval should have a scope: permission for one purpose and configuration does not automatically approve a new use, connection, or data category.
Consider input and output sensitivity, system connectivity, potential for malicious use, new vulnerabilities, effects on people, and how dependable the system is in its intended context. Assess relevant capabilities and the robustness of safeguards before launch and during operation. NIST’s AI RMF can help organize this work, but the organization must set its own thresholds and decision authority.
How do we govern employee use of generative AI?
Give employees usable rules for approved tools, permitted data, and required review instead of relying on vague instructions to “use AI responsibly.” Make the rules apply to employees and relevant third-party personnel, and distinguish low-risk drafting from use involving sensitive information, connected systems, or operational decisions.
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Specify:
- Which services and organization-managed accounts are approved for work, and how staff can request review of a new tool.
- What categories of information may or may not be entered, including rules for confidential, personal, regulated, or customer data.
- How access is granted and removed, what sharing and logging are permitted, and what retention expectations apply.
- When a human must verify generated material before it is shared, used in a decision, or acted upon.
- Which uses are prohibited or require prior approval, including use of generated code or content in sensitive workflows if applicable to the organization.
- How to report unexpected outputs, possible exposure, unsafe behavior, or an unapproved integration.
Do not treat an interface as the only control. A text-generation tool used for internal drafting has a different exposure from an application that retrieves confidential records, invokes plugins, writes code, or can take actions. The policy should require review of the actual configuration and permissions, not just the product category.
What security controls should an AI review require?
Require a threat model and security review proportionate to the system’s access, data, and impact. The review should examine the complete system—model, application, data sources, connected tools, permissions, and provider dependencies—because a weakness may arise in an integration or workflow rather than in the model alone.
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Address generative-AI attack paths
NIST AI 600-1 identifies direct and indirect prompt injection, data poisoning, adversarial examples, membership inference, and model extraction among risks to consider. Direct prompt injection involves malicious content supplied to the system; indirect prompt injection can arrive through material an integrated application retrieves. A policy should require teams to identify which paths are relevant and how they will test and mitigate them.
Do not assume that a single filter, instruction in a system prompt, or user warning resolves these risks. Set layered requirements appropriate to the use case, such as limiting permissions, separating sensitive data, validating inputs and outputs, constraining tool calls, and requiring human confirmation before consequential actions. These are control objectives to evaluate in the design, not guarantees that an attack can be eliminated.
Protect security properties across components
Require safeguards for:
- Confidentiality: Control which data reaches the model, provider, logs, retrieval sources, and connected tools; review access and retention arrangements.
- Integrity: Consider whether inputs, training or retrieval data, model artifacts, generated code, and outputs could be manipulated or corrupted.
- Availability: Plan for service disruption, dependency failure, abuse, or degraded behavior, and identify how critical work continues if the AI system is unavailable.
Use least-privilege permissions, secure integration patterns, and separation of sensitive data where appropriate. Document the trust boundaries, the actions the system can take, the human checkpoints, and the fallback path. The controls should account for relevant model and system components rather than assuming the provider is the only party responsible for security.
Test before launch and during operation
Set pre-deployment and recurring evaluation requirements. The risk tier should determine test depth, whether specialist or independent review is needed, and which attack scenarios must be exercised. Record test conditions, findings, mitigations, residual risk, and approval. Re-evaluate after changes to the model, provider, data, permissions, integrations, or use case, and use operational incidents and near misses to improve tests.
How should we review AI vendors and dependencies?
Review a provider before procurement or a significant change, and revisit the review when the service or its role changes. NIST’s Generative AI Profile recommends updating acquisition and vendor due diligence to account for privacy, security, intellectual property, embedded AI, monitoring, and incident or vulnerability information.
Ask the business and procurement teams to document what is known about:
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- Data handling, access, retention, deletion, and the provider’s terms for submitted data and outputs.
- Security practices and available information about vulnerabilities, incidents, and notification or escalation channels.
- Service or model changes, including how the organization will learn about changes that could affect behavior, data handling, or risk.
- Dependencies such as APIs, plugins, libraries, fine-tuned models, and other providers in the service chain.
- Privacy, intellectual-property, and contractual concerns relevant to the proposed use.
- Monitoring responsibilities and how the organization can obtain information needed to investigate an issue.
Document unanswered questions and decide whether they are acceptable, require additional safeguards, or prevent approval. Where appropriate, put security, incident communication, change notification, and data-handling expectations into contracts or escalation procedures. A vendor review is not a substitute for the organization’s own assessment of its configuration, users, and intended purpose.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do we monitor AI and respond to incidents?
Assign a team to receive reports and establish a response path for unexpected outputs, data exposure, unsafe integrations, security events, or service failures. Monitoring should match risk and should include relevant third-party services, not just the organization’s application layer.
Define a response sequence that enables teams to:
- Receive and record reports, including errors, near misses, and negative impacts, with enough context to identify the system, use, data, and time involved.
- Assess severity and notify the designated security, privacy, legal, business, or provider contacts according to the incident type.
- Contain the issue, which may include limiting access, disabling an integration or action, pausing the affected use, or suspending the system.
- Investigate relevant logs, data flows, model or service changes, and connected components while preserving appropriate records.
- Recover safely, validate that controls work, and obtain the required approval before resuming affected use.
- Conduct an after-action review, record lessons, and update the risk assessment, policy, tests, or controls as needed.
Decide in advance who can pause a system when harm or exposure is suspected and who authorizes restoration. Keep incident records and route recurring errors or near misses into governance reviews; a system that has not caused a confirmed incident may still reveal a control gap through repeated unexpected behavior.
How should we review, update, and retire the policy and systems?
Set a policy review cadence and event-driven triggers. Review after a material model or vendor change, a new integration, a security incident, a significant shift in use, or a change in applicable requirements. Assign an owner to coordinate revisions and communicate what changed to affected teams.
For each approved system, define the conditions that require reassessment and the process for safe deactivation. Retirement should specify who disables the service or capability, removes credentials and user access, handles stored data and model or system artifacts, closes connected integrations, and updates the inventory. NIST’s profile includes safe deactivation and decommissioning within governance; retirement is therefore a planned control, not an informal end to support.
How do the NIST AI RMF and NIST RMF fit together?
The AI RMF and the NIST Risk Management Framework (RMF) offer complementary ways to organize governance. The AI RMF groups work into Govern, Map, Measure, and Manage; the NIST RMF overview describes a seven-step lifecycle: Prepare, Categorize, Select, Implement, Assess, Authorize, and Monitor.
| Framework view | What it helps organize | How to use it in policy work |
|---|---|---|
| NIST AI RMF | AI-specific governance, context mapping, measurement, and risk management. | Use it to structure responsibilities and questions about AI use cases, impacts, evaluation, and ongoing risk. |
| NIST RMF | A system security and privacy risk-management lifecycle from preparation through monitoring. | Use it to connect system categorization and selected controls with implementation, assessment, authorization, and continuous monitoring. |
Organizations can use both views, tailoring them to their needs and applicable constraints; neither replaces a determination of which laws, contracts, or sector controls apply. NIST states in its AI RMF FAQs that “The AI RMF is intended for voluntary use.” Treat it as an organizing framework rather than a universal mandate, and verify current NIST materials before citing specific framework revisions or profiles.
How do we know whether the policy is working?
Assess whether the organization can show that the policy changes decisions and operations, not merely whether employees have acknowledged it. A practical evidence review checks that:
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- Risk tiers, approval thresholds, and exception decisions are documented and consistently applied.
- Higher-risk systems have proportionate threat models, evaluations, and recorded approvals before deployment.
- Vendor reviews, change triggers, monitoring responsibilities, and incident routes are assigned.
- Reports, incidents, errors, and near misses are recorded, reviewed, and used to revise controls.
- Retired systems have access removed and their data, integrations, and artifacts handled under defined procedures.
Use gaps in this evidence to improve the policy and implementation plan. For example, an inventory that omits AI features embedded in purchased software suggests a procurement or service-owner discovery gap; approvals without follow-up evaluations suggest the lifecycle process ends too early. The objective is a traceable chain from use-case purpose to risk decision, controls, operational monitoring, and eventual retirement.
Frequently Asked Questions
Does an AI security policy replace our existing information-security policy?
Usually it should extend and connect existing security, privacy, procurement, and incident processes rather than create a conflicting parallel system. Map AI-specific decisions—such as model changes, prompt-injection testing, and embedded AI discovery—to the teams and processes already responsible for those functions.
Who should sign off on an AI security policy?
The executive sponsor or other authority responsible for organizational risk should approve the policy, with input from security, privacy, legal or compliance, IT, procurement, and affected business leaders. The policy should separately name who may authorize individual systems; policy approval alone is not deployment approval.
How often should an organization review its AI security policy?
Choose a cadence your organization can sustain and define event-driven reviews for material system, vendor, integration, use, incident, or requirement changes. The policy should name the owner who initiates a review and the authority that approves revisions.
What is the difference between AI security and AI ethics governance?
AI security focuses on protecting systems, data, and operations from compromise, misuse, and failures; broader AI governance may also address issues such as fairness, transparency, and accountability. Their concerns can overlap, so organizations should coordinate reviews while making ownership and approval responsibilities explicit.
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