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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsAn enterprise AI security program should inventory every model and AI-enabled workflow, classify and control data at each handoff, enforce least-privilege access for people and machines, and continuously monitor security, quality, drift and abuse. Use the NIST AI Risk Management Framework functions—Govern, Map, Measure and Manage—as the operating structure, then use implementation controls from OWASP AISVS and Cloud Security Alliance guidance. The checklist below turns those principles into accountable controls, evidence and review triggers.
1. Establish governance before deployment
AI security is a lifecycle discipline, not a one-time model approval. Accountability must remain clear from design through retirement, including for systems assembled from a hosted model, retrieval database, agent, plug-ins and internal data.
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| Control area | What to establish | Evidence to retain | Review trigger |
|---|---|---|---|
| Accountability | Name a business owner, system owner, security owner, privacy owner, and model or data steward. | Signed responsibility matrix and escalation contacts. | Role change, architecture change or incident. |
| Inventory | Record models, applications, agents, prompts, retrieval stores, datasets, tools, endpoints, vendors and retirement state in one living register. | Inventory entry with version, environment, owner and dependencies. | New use case, model, data source, tool or provider. |
| Intended use | Document permitted purposes, prohibited uses, affected people, human-oversight requirements and decommissioning criteria. | Approved use statement and misuse cases. | Material change in users, data, autonomy or outcome. |
| Risk management | Assign a risk tier using impact, data sensitivity, autonomy, affected populations and regulatory exposure. | Risk assessment, approval decision and compensating controls. | Model update, fine-tuning, new tool, incident or changed operating context. |
| Operating cadence | Set control owners, review frequency, alert severity, response times and evidence-retention periods. | Calendar, service-level objectives and completed review records. | Missed review, control failure or material threat change. |
NIST AI RMF 1.0 (2023) treats governance as a cross-cutting function. Its Govern 1.5 outcome calls for planned ongoing monitoring and periodic review, with organizational roles and responsibilities clearly defined. The framework is voluntary; tailor it to your sector, jurisdiction, contracts and risk tolerance rather than treating it as a legal-compliance certificate.
2. Build an inventory that can support a risk decision
An inventory is the foundation for every later check. A spreadsheet that lists only production model names is insufficient because sensitive data and permissions often sit in the surrounding application.
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Minimum record for each AI system
- Business purpose, user groups, accountable owners and support contact.
- Model name, provider, version or deployment identifier, hosting region and update mechanism.
- Application surfaces, APIs, agents, plug-ins, vector stores, datasets, prompts and external tools.
- Data classes handled at ingestion, retrieval, prompt, output, logging, telemetry, training and fine-tuning stages.
- Autonomy level, actions the system can request, human approval points and prohibited actions.
- Dependencies, software packages, model artifacts, vendors, subprocessors and network paths.
- Risk tier, approved environments, review date, known limitations and retirement state.
Risk-tier questions
Classify impact before choosing controls. Ask what happens if an answer is wrong, confidential information is disclosed, an agent acts without approval, or the service becomes unavailable. Increase scrutiny when the system affects employment, credit, health, safety, legal rights, financial transactions, critical operations or vulnerable people. Record the reasoning, not just the tier label; auditors and incident responders need to understand which assumptions drove the decision.
Map the complete data and control flow
Draw the path from collection to deletion: source systems, preprocessing, prompt construction, retrieval, model invocation, tool calls, output delivery, logs, traces, analytics and backups. Mark trust boundaries and identify where a provider, tenant, region or subcontractor receives data. The map should show which identity authorizes every transition and where a human can stop an action.
3. Protect data wherever an AI system touches it
Apply the same protection to prompts and retrieval context that you apply to databases. A prompt can contain a customer record, an access token or a confidential contract even when the model is not being trained on it.
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- Label personal, confidential, regulated, proprietary and security-sensitive fields at the source.
- Send only the fields and context needed for the declared purpose; remove direct identifiers and secrets, or replace them with controlled tokens.
- Block unapproved retention or provider training. Make the setting verifiable through contract terms and technical configuration.
- Apply purpose limits to prompts, retrieved passages, generated outputs, traces and analytics exports.
Secure retrieval-augmented systems
Keep tenant indexes and document collections separate unless a deliberate sharing policy permits otherwise. Enforce authorization at query time, not only when documents are ingested: a user who cannot open a source document must not receive its embedded passage through search. Carry source permissions and sensitivity labels into chunks and metadata, and test filters with cross-tenant and revoked-access cases.
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Control training and fine-tuning data
Record provenance, license, consent or other legal basis where applicable, transformations, labeling decisions and model-use restrictions. Maintain a manifest of files and versions so a prohibited record can be located and removed. Test for memorization and training-data leakage before release, and keep fine-tuning sets separate from unrestricted operational logs.
Protect transport, storage and deletion
Encrypt data in transit and at rest, and protect encryption keys separately from the services that use them. Store provider credentials and service secrets in a managed secret system with rotation rather than in prompts, source code or notebooks. Define retention and deletion rules for prompts, outputs, embeddings, fine-tuning sets, traces, logs and backups; verify deletion with provider records and restore tests instead of assuming that deleting an application row removes every copy.
Test data-specific attacks
- Prompt injection that attempts to override instructions or expose hidden context.
- Retrieval poisoning that inserts misleading or malicious content into an index.
- Data exfiltration through generated text, tool arguments, error messages or logs.
- Sensitive-data disclosure caused by over-broad context, memorization or insecure output handling.
- Training-data leakage and membership or reconstruction attempts appropriate to the use case.
4. Apply least privilege to users, services, agents and tools
AI access control has more subjects than a conventional application. A workforce user, background job, agent, model endpoint, vector database and plug-in each need an identity and an explicit authorization decision.
Identity foundations
- Federate workforce identity and require phishing-resistant multi-factor authentication for privileged administration where feasible.
- Use workload identity for services and agents. Prefer short-lived credentials over embedded keys, and rotate secrets on a defined schedule.
- Separate development, test and production environments, accounts, data and model endpoints.
- Use centralized policy enforcement so authorization is consistent across the application, retrieval layer, tools and provider APIs.
Permission design
Grant the smallest operation and data scope required: read-only retrieval instead of write access, a single approved system instead of an unrestricted network, and a bounded transaction amount instead of an open-ended financial capability. Make tool permissions explicit in code and policy. Validate the user, agent, tool, target resource, parameters and purpose before each call; do not treat a model-generated plan as authorization.
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Approval gates and separation of duties
Require human approval before financial transfers, legal commitments, safety-critical changes, production modifications, irreversible deletion or actions affecting a person’s rights. Keep the person who develops a prompt or model from being the sole approver of its production release when risk warrants separation. Log the requested action, approver, decision, timestamp, parameters and resulting tool call.
Access review and evidence
Review entitlements on a schedule and after a role change, model change, vendor change or architecture change. Capture successful and denied authorizations, privilege changes, administrative actions, token issuance, tool calls and policy overrides. Alert on dormant privileged identities, repeated denials, unusual geography or time, and sequences that differ from the system’s normal workflow.
5. Secure the model, application and supply chain
Threat-model the full application
Include prompt injection, insecure output handling, model theft, poisoned data, supply-chain compromise, denial of service and unsafe agency in the threat model. For each threat, specify a preventive control, a detection signal, a response owner and a recovery step. High-risk use cases should have abuse cases and adversarial tests that reflect their actual tools, data and users.
Constrain inputs, outputs and execution
- Validate input size, type, encoding and source; reject unexpected fields before they reach the model.
- Validate output schemas and treat generated text as untrusted data. Escape or sanitize content before rendering, executing or inserting it into queries.
- Allow-list tools and destinations, constrain arguments and sandbox any code execution.
- Rate-limit requests and set budget controls for tokens, compute, external calls and transaction value.
- Pin and verify dependencies, containers, packages and model artifacts; scan them through the build and deployment pipeline.
Prepare rollback and recovery
Maintain versioned prompts, policies, model artifacts, configuration and retrieval indexes so the responsible team can reproduce a failure. Test rollback, kill-switch, backup and recovery procedures under realistic permissions. A kill switch that only the model team can operate is not sufficient if an incident requires security or operations to stop tools immediately.
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6. Monitor production for security, quality and control failure
Monitoring must show both what the model produces and whether the surrounding controls still work. A dashboard of latency and uptime cannot detect leakage, prompt abuse or a silent loss of authorization filtering.
Operational and quality signals
- Availability, latency, throughput, queue depth, token or compute cost and provider errors.
- Model and input-data drift, retrieval coverage, schema-validation failures and changes in user feedback.
- Task quality, factual or business error rates, hallucination indicators and human override frequency.
- Safety-policy violations, refusal failures and outcomes for defined high-risk scenarios.
Security telemetry
- Prompts or retrieved content matching sensitive-data detectors or secret patterns.
- Prompt-injection indicators, repeated jailbreak attempts and abnormal tool sequences.
- Anomalous identities, token use, geography, volume, access denials and privilege changes.
- Unexpected dependency, model, prompt, policy or retrieval-index changes.
- Cross-tenant access attempts, unusual export activity and disabled or bypassed controls.
Turn signals into an operating process
For every metric, define a baseline, threshold, alert route, severity, response time, evidence-retention period and escalation owner. Thresholds should reflect the system’s normal workload and risk tier; a sudden increase in denied tool calls may be urgent for an autonomous agent but routine during a controlled red-team exercise. Send actionable events to the security monitoring and ticketing systems, while preserving enough prompt, output, trace, model-version and policy context to investigate under applicable privacy rules.
Re-evaluate after change
Repeat risk and control testing after model updates, fine-tuning, prompt or policy changes, new tools, data-source changes, provider changes, incidents or material shifts in user behavior. Report control effectiveness—not merely alert counts—to governance owners. NIST’s Govern and Manage functions and Cloud Security Alliance guidance both emphasize continuous monitoring and periodic review.
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7. Audit an AI vendor before approval
Third-party risk includes the model provider, hosting platform, retrieval service, observability vendor, plug-ins, datasets and every subcontractor that can process data or change the service.
| Evidence to request | Questions the review must answer | Approval condition |
|---|---|---|
| Data-flow and subprocessor description | Which regions, subprocessors, support channels and backups can receive prompts, outputs, embeddings or logs? | Processing locations and onward transfers fit privacy, residency and contract requirements. |
| Retention and deletion terms | Are prompts, outputs, traces and backups retained? Can deletion be verified, including after account closure? | Written retention limits, deletion process and evidence are available. |
| Training and use policy | Are customer inputs used for training, evaluation, abuse detection or human review, and can those uses be disabled? | Purpose and opt-out settings match the approved data classification. |
| Security and test evidence | How are access, isolation, vulnerability management, model artifacts and incident response tested? | Evidence is current, scoped to the service and reviewed by security. |
| Change and incident duties | How much notice is provided for model, subprocessor, region or API changes, and when will the vendor notify you of an incident? | Notice windows, contacts and customer responsibilities are contractual. |
| Exit plan | Can data, indexes, prompts, configurations and logs be exported, deleted and replaced without hidden production dependencies? | A tested migration and decommissioning path exists before production use. |
Do not infer security from a vendor’s marketing label. Record the service version, approved features, data classes, geographic scope, evidence date and owner who accepted residual risk.
8. Respond to incidents and retire systems safely
Incident playbooks
Prepare separate playbooks for data leakage, compromised credentials, unsafe outputs, model abuse, provider incidents and service compromise. Each should identify decision authority, containment steps, communications, evidence handling and recovery criteria.
- Contain the path: revoke credentials, disable a tool, quarantine a retrieval source, block an endpoint or activate the kill switch.
- Preserve prompts, outputs, traces, model and prompt versions, access records, configuration and relevant provider notices, subject to privacy and legal-hold rules.
- Assess affected data, users, tenants, decisions and downstream systems; involve privacy, legal, security and business owners.
- Roll back or replace the model, remove poisoned data, rotate secrets and test the corrected path before restoring service.
- Notify affected stakeholders according to contracts and applicable law, then document lessons and control changes.
Decommissioning checklist
- Stop traffic and revoke workforce, service, agent and vendor credentials.
- Remove API routes, scheduled jobs, webhooks, plug-ins, network rules and hidden production copies.
- Delete or archive prompts, outputs, indexes, fine-tuning data, traces and backups according to policy and legal holds.
- Preserve required audit evidence, ownership records and incident history.
- Verify through logs, discovery scans and owner sign-off that no active users or integrations remain.
9. Use NIST, OWASP and CSA together
| Framework | Release | Best use | What it adds to the checklist |
|---|---|---|---|
| NIST AI RMF 1.0 | 2023 | Enterprise governance and risk process. | Govern, Map, Measure and Manage functions; accountability, context, measurement and ongoing review. |
| NIST Generative AI Profile (NIST-AI-600-1) | 2024 | Generative-AI-specific risk interpretation. | Use-case context for data, model, content, misuse and lifecycle risks. |
| Cloud Security Alliance AI Core Security Responsibilities | 2024 | Cloud and shared-responsibility implementation. | Continuous monitoring, reporting mechanisms and responsibility boundaries between customer and provider. |
| OWASP AISVS 1.0 | 2026 | Testable application and engineering controls. | Shared verification checklist covering identity, access control, data, development, deployment and retirement. |
NIST describes its Playbook as neither a checklist nor a set of steps that must be followed in its entirety. Use the RMF to decide and govern, the Generative AI Profile to interpret generative risks, CSA material to clarify cloud responsibilities, and AISVS to turn requirements into tests and audit evidence. None of these documents alone establishes legal compliance.
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10. A practical rollout sequence
- Discover. Interview business and technology teams, inspect network and identity records, and populate the inventory with models, applications, agents, data stores, tools and vendors.
- Classify. Map data flows, assign risk using impact, sensitivity, autonomy, affected people and regulatory exposure, and document prohibited uses and human oversight.
- Lock down. Enforce identity federation, MFA for privileged users, workload identity, short-lived credentials, tenant-aware retrieval authorization, secret management and environment separation.
- Test. Run prompt-injection, exfiltration, poisoning, leakage, output-validation, dependency and abuse tests. Record expected results, failures, owners and retest dates.
- Instrument. Ship security, quality, drift, cost, availability and control-effectiveness signals to monitoring, with thresholds and response playbooks.
- Approve and operate. Obtain business, security and privacy sign-off appropriate to the risk tier; review access and controls on schedule and after every material change.
- Improve or retire. Feed incidents and monitoring results into the next risk review. When a system no longer has a justified purpose, execute the verified decommissioning plan.
Bottom line
The defensible enterprise checklist is a living control system: know every AI component and owner, follow sensitive data through prompts and logs, authorize each human and machine action, test the application and supply chain, and monitor whether security and quality controls continue to work. NIST provides the lifecycle structure; OWASP AISVS and CSA guidance make the work testable and explicit. Approval is only the beginning—the same evidence and accountability must remain in place until the last integration is removed.
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