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An enterprise AI security checklist should answer six questions: who owns each AI use, what systems and suppliers are involved, what data and harms are in scope, how information and access are protected, how performance is tested and monitored, and how incidents or lifecycle changes are handled. The NIST AI Risk Management Framework (AI RMF) is a useful voluntary guide for organizing that work; it is not a legal certification or a replacement for applicable security, privacy, or enterprise-risk requirements.
What the checklist is for
Use the checklist as a repeatable review before an AI system is approved, when it changes materially, and during operation. It can cover internally built models, third-party AI services, and AI features embedded in software already in use. Tailor the questions to the system, its users, the data involved, and the consequences of failure; the list below is an operational synthesis, not a checklist prescribed verbatim by NIST.
AI risk work should fit into existing security, privacy, procurement, vendor-risk, and enterprise-risk processes. NIST’s AI RMF FAQ explains that “Each framework focuses on a specific set of challenges in managing risks.” An AI framework does not by itself establish compliance with laws, contracts, or sector-specific requirements.
How the NIST AI RMF can organize the review
NIST released AI RMF 1.0 on January 26, 2023. It is voluntary guidance for incorporating trustworthiness considerations into the design, development, use, and evaluation of AI systems. NIST says the framework is being revised, so check its current status when adopting it.
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| Function | How it helps an enterprise review |
|---|---|
| Govern | Establish accountability, policies, oversight, and the processes used to manage AI risk. |
| Map | Describe the system’s purpose, context, users, data, dependencies, and possible effects. |
| Measure | Assess and test risks and trustworthiness in the system’s actual context. |
| Manage | Prioritize risks, choose responses, and track them through the system lifecycle. |
NIST’s companion Playbook suggests actions for these functions; organizations can select those relevant to their use case. The framework is an organizing aid, not a universal scoring method or a substitute for an organization’s own controls.
For generative AI, NIST AI 600-1, released July 26, 2024, is a companion profile that addresses risks specific to generative AI and offers risk-management actions aligned with organizational goals. It supplements the broader AI RMF; it does not replace organization-wide governance.
Enterprise AI security checklist
1. Assign governance and accountability
- Name an accountable executive sponsor and owners for security, privacy, legal or compliance, procurement, engineering, and the business use.
- Define who can approve a use, what evidence reviewers need, and who can accept or escalate residual risk.
- Document the system’s approved purpose, user groups, prohibited uses, and any conditions on use.
- Set an escalation route and a review cadence appropriate to the system’s risk and rate of change.
2. Inventory systems, suppliers, and dependencies
- Record internally built AI, vendor services, AI-enabled features in existing products, models, data sources, integrations, and material dependencies.
- For each entry, capture an owner, purpose, affected users, deployment context, data types, supplier, and lifecycle state, such as proposed, in testing, deployed, suspended, or retired.
- Update the inventory when a model, supplier, integration, data source, or use changes materially.
NIST IR 8596, dated December 2025, discusses AI-related supplier-service inventories and other cybersecurity considerations. The document identifies itself as an initial preliminary draft, not a final standard or binding requirement.
3. Map the use, data, and potential harm
- Describe intended and prohibited uses, who operates the system, who is affected by its outputs, and where decisions or actions depend on those outputs.
- Map inputs, outputs, data sources, connected systems, human review points, and other dependencies.
- Consider plausible failure and misuse cases, including unreliable or harmful output, unauthorized use, security compromise, privacy impact, and business disruption.
- Assess security, privacy, reliability, safety, fairness, and business impact in context. Record assumptions and uncertainties rather than treating one score as a complete assessment.
4. Protect data and access across the workflow
- Identify what information users may submit, what the system can retrieve through connected sources, and what may appear in generated outputs.
- Minimize sensitive information sent to a model or service, and set permissions for users, administrators, integrations, and connected data.
- Establish and verify data-handling expectations, including retention, deletion, logging, and how submitted information and outputs are handled.
- Review whether prompts, outputs, logs, integrations, or shared access could expose confidential or personal information.
- Have privacy and legal owners map the design and use to applicable laws, regulations, and contracts. These prompts are not a complete legal control set.
5. Test before deployment and monitor in operation
- Test expected use as well as misuse, failure, and security scenarios before release and after material changes.
- Evaluate the system in its real operating context, including relevant data, integrations, users, and human oversight.
- Monitor outputs, security events, privacy incidents, performance shifts, and whether controls remain effective.
- Define who reviews alerts and what conditions trigger escalation, restricted use, suspension, or rollback.
NIST’s AI Resource Center provides technical resources related to testing, evaluation, verification, and validation (TEVV). These activities should reflect the system and its consequences rather than rely on a generic test result.
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6. Prepare for incidents and lifecycle change
- Assign response owners and procedures for data exposure, compromised components, harmful or unreliable outputs, supplier changes, and unauthorized use.
- Determine how to preserve relevant evidence, notify the right internal teams and external parties where required, and remediate the issue.
- Reassess the system when its purpose, model, data, supplier, integrations, user population, or operating conditions change.
- Decide whether to continue, modify, restrict, suspend, or retire the use when risks or controls change.
Questions to ask about vendors and supply chains
A vendor review should connect the supplier’s answers to the specific service and data flows being approved. Ask for information that lets your owners evaluate risk and plan for change, rather than treating a generic assurance document as proof that every use is safe.
- Which models, service components, subprocessors, and material dependencies support the feature?
- What data does the service receive, generate, retain, or make available to other components, and what are the applicable handling and deletion terms?
- What security and privacy evidence is available, and what does it cover?
- How will the supplier communicate material model, service, or subprocessor changes?
- How are incidents reported and coordinated, and what happens to data and access when service ends?
- What controls can the customer configure, and what responsibilities remain with the customer?
Record the answers, unresolved questions, accountable owner, and approval conditions in the risk decision. Revisit them when the service or its use changes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Tailor the checklist to your organization
Start by assigning owners and inventorying AI uses, including embedded features that may not have entered through a dedicated AI procurement process. Apply the six review areas to each use in proportion to its context and potential impact. Map the resulting decisions and controls into existing risk processes, confirm applicable legal and contractual obligations with qualified owners, and set a review cadence that responds to meaningful changes as well as ongoing operation.
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