Before deploying AI, a mature security program needs a clear use case and accountable owners, an inventory of the systems and data involved, supplier and access controls, evidence from testing the actual deployment, and an operating plan for monitoring, incidents, and change. Treat AI as a governed system change—not as a product that is secure merely because a vendor says it is.
There is no universal readiness certificate in the reviewed guidance. NIST describes its AI Risk Management Framework as voluntary, and its Generative AI Profile offers suggested actions rather than a pass-or-fail certification test. The right approval threshold depends on the organization, the use case, and the impact of failure.
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What should the review cover?
Review the complete application and the way people will use it, not just the model in isolation. An AI feature may depend on a provider’s model, your fine-tuning or retrieval setup, data stores, APIs, tools or plugins, identity systems, user interfaces, and vendor-operated services. A weakness in any connected component can change the risk of the whole deployment.
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NIST’s AI Risk Management Framework is intended to support risk management across AI design, development, use, and evaluation. Its AI RMF 1.0 was released on January 26, 2023; NIST says the framework is voluntary and is being revised. NIST published the Generative AI Profile, NIST AI 600-1, on July 26, 2024. These are tools for organizing decisions, not proof that a specific system is secure.
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Security remains concerned with confidentiality, integrity, and availability, but AI components can introduce risks tied to model behavior, training and retrieved data, generated outputs, and how the application acts on those outputs. NIST’s AI security control overlays are described as in development, not as a finished mandatory standard. OWASP’s 2025 LLM Top 10 is a useful security taxonomy, not a regulatory requirement.
Who and what must be in scope?
Set the intended use and decision authority
Write down the intended use, intended users, affected systems and people, and the actions the AI may take. Name the business owner, security owner, release authority, and any privacy, procurement, or governance reviewers whose approval is needed. Define the organization’s risk tolerance and the conditions under which the deployment must be limited, delayed, or rejected.
Make clear who can approve exceptions and who is accountable after release. A model provider, system integrator, internal product team, and operating team may each control different parts of the system; responsibility should not disappear between them.
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Map components and trust boundaries
Document the path from user input to model response and any downstream action. Include the model and provider, hosting, fine-tuning, retrieval sources, data stores, APIs, tools, plugins, identity and authorization services, logging, and human review points. Mark where data or instructions cross a trust boundary, including content retrieved from sources that users do not control.
What should the AI inventory record?
Keep an inventory that lets security and governance teams identify what is deployed, how it is accessed, and what could change its risk. Record at least:
- System and use-case name, accountable owner, model and version, provider, and hosting arrangement.
- Access mode, connected tools and data sources, intended context, and permitted actions.
- Data provenance where known, plus whether prompts, retrieved content, outputs, logs, or feedback may contain personal, sensitive, proprietary, or licensed information.
- Human oversight roles, known issues, documented limitations, and the safeguards intended to address them.
- Retention, acceptable-use, update, reassessment, and decommissioning rules.
Review the full data lifecycle. Prompts, retrieval results, generated content, logs, and user feedback may each create a disclosure or retention risk. NIST’s Generative AI Profile identifies privacy impacts such as leakage, unauthorized disclosure, and de-anonymization; assess those risks in the context of the data and deployment, rather than assuming that removing a name makes information anonymous.
How should suppliers and AI supply chains be assessed?
Extend existing procurement and supplier-risk processes to cover embedded AI features, model libraries, APIs, fine-tuned models, tools, and both open-source and proprietary services. A supplier review should address the components that matter to the actual deployment, not just the primary vendor named on an invoice.
- Security and privacy: Review relevant controls, known incidents and vulnerabilities, data access, and monitoring or alerting practices.
- Data and intellectual property: Clarify how prompts, uploaded data, retrieved content, and outputs are retained, used for training, located, and accessed. Consider rights and licensing for data and model inputs.
- Change and incident visibility: Establish how the provider will communicate material changes, vulnerabilities, and incidents that could affect your system.
- Evaluation rights: Where appropriate, use contract terms that support evaluation of third-party processes and clarify cooperation during security reviews and incident response.
NIST’s Generative AI Profile recommends updating acquisition and procurement diligence for generative AI and considering contract clauses that enable evaluation of third-party processes. The specific terms depend on the service and the organization’s legal, privacy, and regulatory context.
How should identities, permissions, and autonomy be bounded?
Apply least privilege and layered defense to AI components, including the identities used by models, agents, integrations, and background services. Permissions should match the particular use case; an AI feature that summarizes documents generally should not inherit broad access to every document or system simply because that is convenient to configure.
For agentic systems, explicitly limit autonomy and potential blast radius. CISA and five partner agencies, in guidance announced May 1, 2026, recommend avoiding broad or unrestricted access—especially to sensitive information and critical systems—and emphasize strong identity management, oversight, layered defense, threat modeling, and continuous monitoring.
- Use scoped identities and narrow permissions for each tool or action.
- Separate read access from write, administrative, or destructive permissions.
- Require human approval for consequential actions where appropriate.
- Define action boundaries and provide a way to disable or contain the agent.
- Test whether a compromised model, malicious input, or faulty output could reach a sensitive system or data set.
What threats should the team test for?
Threat-model the whole application, its trust boundaries, and the downstream effects of model output. Relevant cases include direct prompt injection, indirect prompt injection through retrieved or otherwise supplied content, data poisoning, sensitive-information disclosure, supply-chain compromise, model or data integrity failures, unauthorized access, and extraction. Consider how a failure in one component could trigger an unsafe action elsewhere.
NIST describes direct prompt injection as malicious input supplied directly to a model and indirect prompt injection as adversarial instructions placed in data likely to be retrieved. OWASP’s 2025 LLM Top 10 includes prompt injection, sensitive information disclosure, and supply-chain risks. Use these categories to inform scenarios; they do not replace a threat model for your own architecture.
What evidence should be required before release?
Test the configuration people will actually use, not only the model in a vendor demonstration. Use representative data and workflows, deployment-like conditions, and the intended integrations and permissions. Record the test scope, results, limitations, failure modes, and any assumptions that affect the conclusion.
- Validate capability claims empirically. Test vendor and internal claims against the intended use, data, workflow, and user population.
- Exercise security controls. Evaluate relevant attack scenarios, access boundaries, and whether safeguards continue to work when the model behaves unexpectedly. Use AI red-teaming where appropriate.
- Assess limitations. Document where the system does not generalize reliably, what it cannot safely do, and how failures could affect people, data, or connected services.
- Submit results to the release authority. Give the decision-maker the evidence, unresolved risks, proposed mitigations, and any conditions attached to release.
NIST recommends empirical validation of capability claims, pre-deployment testing, evaluation in conditions similar to deployment, documenting limits on generalization, and sharing results with release authorities. A successful test is evidence about the tested scope; it does not establish safety for untested versions, data, permissions, or uses.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should deployment options be compared?
When there is a choice of model, provider, or operating mode, compare each candidate against the same use case and risk tolerance. The table is a decision framework, not a claim that one option is always safer.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitches| Decision dimension | What to compare | Why it matters |
|---|---|---|
| Autonomy and blast radius | Suggestion-only use, human-approved actions, or autonomous execution; reachable data and systems; likely impact if compromised. | More authority or reach can increase the consequences of misuse or failure. |
| Data exposure | Prompt, retrieval, training, logging, and output handling; sensitive or regulated data; retention and reuse terms. | Different data paths can expose information in different ways. |
| Integration and supply chain | Provider and model, APIs, tools, plugins, retrieval sources, hosting, update visibility, and incident reporting. | Risk depends on connected components and the organization’s ability to see material changes. |
| Assurance evidence | Deployment-like test results, red-team findings, known limitations, monitoring, and recovery capability. | Evidence should match the configuration and use under consideration. |
| Governance fit | Accountable owners, risk tolerance, approval route, and fit with existing security and privacy processes. | A technically viable option still needs a workable decision and oversight model. |
What must be ready for ongoing operation?
Release is the start of operational security work, not the end of review. Assign owners to monitor behavior, access, outputs, security anomalies, supplier changes, and the effectiveness of safeguards. Define how findings are escalated and who can pause or roll back the service.
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Connect AI-related incidents to the organization’s existing incident-response, privacy, and breach-reporting processes. Establish containment, deactivation, recovery, rollback, and evidence-preservation steps before they are needed. Rehearse scenarios that involve a third-party provider as well as internal teams; NIST recommends clear incident-response ownership and rehearsal, while CISA and partner agencies call for continuous monitoring and regular security assessments for agentic services.
Reassess when the model version, data, integrations, permissions, or intended use changes. A change that appears minor to a supplier or product team can alter the threat model or invalidate earlier test evidence.
What does a defensible release decision look like?
A mature program should be able to show, in its own records, why the deployment is acceptable for a defined use and under what conditions. Before approval, the release authority should have:
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- An inventory and system map that identify relevant components, data flows, access, and known issues.
- Supplier and data-handling decisions, including unresolved contractual or privacy questions.
- A threat model and test results for the intended configuration, with limitations and residual risks recorded.
- Defined operating safeguards, monitoring responsibilities, incident procedures, and reassessment triggers.
This is a risk-based operating model drawn from official guidance, not a universal checklist that certifies a system as secure. The organization must set its own acceptance thresholds and address applicable legal, sector, and jurisdiction-specific obligations.
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