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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesBefore expanding AI use, require a documented risk assessment for every use case, strict data and privacy rules, phishing-resistant identity controls, supplier and software-lifecycle assurance, security testing, continuous monitoring, and a rehearsed response and recovery process. Treat AI as part of the existing security program, but add controls for model behavior, training and retrieval data, prompts, agents, APIs, and other integrations.
No single checklist is mandatory for every organization. The appropriate baseline depends on your sector, jurisdiction, data sensitivity, deployment model, level of automation, and the consequences of failure.
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Set a risk-based gate before any expansion
Expansion should be an approval decision, not an informal technology rollout. An accountable executive or risk owner should be able to answer what the AI system does, what information it can access, which people and services can operate it, and what happens if it is wrong, compromised, unavailable, or misused.
Use the following minimum gate for each proposed tool, internally built system, hosted service, API, or AI feature embedded in another product:
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- A named business owner and security owner.
- A written description of the task, users, affected processes, and degree of automation.
- A data inventory covering prompts, uploaded files, retrieval sources, training or fine-tuning data, logs, and outputs.
- A map of models, providers, APIs, plugins, agents, identities, networks, and downstream systems.
- An impact assessment covering confidentiality, integrity, availability, privacy, safety, financial loss, legal exposure, and reputational harm.
- Documented acceptance criteria, testing results, monitoring requirements, incident contacts, and rollback or disablement authority.
Reassess when the model, data, provider, permissions, connected tools, user population, or business purpose changes. A low-risk drafting assistant and an agent that can alter production systems should not pass through the same approval path.
NIST’s voluntary AI Risk Management Framework (AI RMF) organizes this work into Govern, Map, Measure, and Manage. Its Playbook supplies suggested actions rather than a certification or legal checklist. NIST’s Cybersecurity Framework (CSF) 2.0 provides broader cybersecurity outcomes and likewise does not prescribe one implementation.
Governance and use-case approval controls
Assign decision rights
Define who can approve a new use case, accept residual risk, grant an exception, suspend the system, and notify regulators, customers, or affected staff. Include security, privacy, legal, procurement, IT operations, and the process owner for higher-impact systems. Keep an exception register with an owner, expiry date, compensating controls, and review date.
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Maintain an authoritative inventory
The inventory should include sanctioned and discovered uses. Record the model or service name and version, hosting location, provider, data residency, contract owner, integrations, service accounts, administrators, end users, and retention settings. Include “shadow AI” found through expense records, browser telemetry, proxy logs, or user attestations; an unlisted service cannot be governed.
Classify impact and autonomy
Use a tiering scheme that drives controls. For example, a system that produces non-sensitive internal drafts may need standard access and logging, while one that handles health, financial, identity, or regulated records needs restricted data paths, stronger assurance, human review, and an approved provider contract. Any system that can send messages, execute code, change records, approve transactions, or control physical processes deserves the highest scrutiny.
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Protect data and privacy throughout the AI lifecycle
Classify before data enters a prompt or dataset
Mark information before it is submitted to an external tool, placed in a retrieval index, used for training or fine-tuning, written to logs, or included in an output workflow. Define allowed, restricted, and prohibited categories. A plain-language rule from CISA’s September 2024 “Stay Safe Online When Using AI” tip sheet is not to share sensitive or confidential information with generative AI tools. Organizations need a more specific internal policy that identifies which data classes may be used with which approved services.
Control provider handling
Procurement and security review should establish whether prompts and files are retained, used to improve models, accessible to provider personnel, transferred across borders, or available to subprocessors. Require contractual commitments for deletion, access logging, breach notification, support access, retention periods, and return or destruction of data at termination. Verify that “private” or “enterprise” settings are enabled and technically enforced rather than relying on a sales description.
Secure retrieval and output paths
Apply source-system permissions to retrieval. A language model must not allow a user to search documents that the user could not open directly. Prevent sensitive outputs from being copied to personal storage, email, ticketing systems, or analytics platforms without an approved destination. Add validation and human review where generated content can trigger a decision, publish externally, or update a record.
Protect provenance and integrity
For teams operating models, document where training, evaluation, and retrieval data came from, who approved each revision, and how the original can be restored. Use trusted infrastructure and controlled pipelines. The joint AI data-security guidance described by the National Security Agency (NSA), CISA, the FBI, and partner agencies in a May 22, 2025 release highlights data supply-chain risk, malicious modification, trusted revisions, and drift. These controls help distinguish a genuine model or data change from tampering.
Require strong identity and access controls
Protect every access path
Require multi-factor authentication (MFA) for AI user accounts, administration consoles, source repositories, data stores, model registries, cloud accounts, and connected infrastructure. Prioritize phishing-resistant MFA for administrators and users handling sensitive data. CISA’s business guidance places physical security keys at the strongest end of the MFA options it describes, followed by authenticator applications, biometrics, and text or email codes. Select a method that fits your identity provider, managed devices, accessibility needs, recovery process, and procurement rules; a security key alone is not an AI security program.
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Apply least privilege to humans and machines
Separate ordinary use, prompt or workflow administration, model deployment, and security administration. Give service accounts, APIs, plugins, agents, and retrieval connectors only the permissions required for a specific task. Use short-lived credentials where possible, prohibit shared administrator accounts, and require approval for privilege elevation.
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Review and revoke access
Run periodic access reviews tied to the risk tier. Remove permissions when staff change roles, suppliers end work, a project closes, or a connector is no longer needed. Log authentication, authorization failures, administrative changes, tool calls, data exports, and high-risk actions in a system that security personnel can investigate.
Make suppliers, models, APIs, and dependencies part of the security review
Ask for evidence before procurement
Request the provider’s security ownership model, architecture, data-flow description, authentication and authorization controls, vulnerability-management process, dependency inventory, update policy, logging capabilities, incident-notification commitments, backup and recovery arrangements, and subcontractor list. Establish how quickly the provider will disclose a security incident or material model change and what assistance it will provide during investigation.
Secure the complete software lifecycle
Review source code, infrastructure-as-code, model artifacts, containers, libraries, prompt templates, evaluation data, and deployment pipelines. Require code review, signed or otherwise verifiable artifacts, secrets management, vulnerability scanning, reproducible build practices where feasible, and separation between development, testing, and production. Include APIs and plugins: an otherwise secure model can become a data-exfiltration path through an over-privileged extension.
Use current guidance with the right status
CISA and the UK National Cyber Security Centre’s “Guidelines for Secure AI System Development” cover secure design, model and system development, deployment, and operation. They are aimed primarily at providers but are useful to managers and risk owners evaluating suppliers. NIST’s Cyber AI Profile, Internal Report 8596, was published as an initial preliminary draft on December 16, 2025; further work was announced during 2026. Treat it as draft material, not a settled requirement.
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Test before launch and after material change
Define acceptance criteria first
Write measurable conditions before testing: permitted data classes, maximum output or action scope, required human approvals, response-time and availability expectations, logging coverage, and unacceptable failure modes. The owner should sign the acceptance record, with security and privacy approval appropriate to the impact tier.
Test the system and its integrations
- Verify that users and service accounts cannot exceed their assigned permissions.
- Attempt to retrieve, infer, export, or expose data outside the approved scope.
- Test prompt and instruction handling, including malicious or conflicting instructions supplied through documents, web pages, messages, or tool results.
- Exercise connected APIs, plugins, code execution, file handling, and outbound communication under denied and degraded conditions.
- Check that logs capture the identity, input or reference, model and tool versions, action, result, and approval state needed for investigation.
- Run misuse, outage, rollback, and corrupted-data scenarios appropriate to the system’s consequences.
Repeat testing after a model replacement, major prompt or retrieval change, new connector, permission expansion, provider-term change, or significant drift in data or outputs. Keep test evidence with the inventory record.
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Monitor the things that can change
Track model and policy versions, training and retrieval data revisions, permissions, connected tools, provider status and terms, unusual usage, failed authentication, sensitive-data detections, output-quality signals, and security alerts. Establish thresholds for investigation rather than collecting logs that nobody reviews. Protect logs from alteration and restrict access because they may contain sensitive prompts or outputs.
Prepare an AI-specific incident branch
Incident procedures should identify who can disable a model, revoke a token, disconnect a retrieval source, block an export, revert to a trusted model or dataset, and preserve evidence. Define escalation for prompt injection, data leakage, compromised providers, malicious model or dataset changes, unsafe autonomous actions, and material drift. Coordinate AI incidents with the organization’s existing cyber-incident, privacy, legal, business-continuity, and crisis-communications plans.
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Maintain tested backups of configurations, approved prompts, retrieval indexes or source references, model artifacts where permitted, policies, and audit records. Establish a manual or alternate process for critical work if the AI service is unavailable. Document recovery time and data-loss objectives for higher-impact uses, and rehearse restoration and safe re-enablement rather than assuming that a provider’s uptime commitment covers your business process.
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Tailor controls to deployment and risk
Deployment model alone does not determine security. Compare options using the following axes:
| Decision axis | Questions to answer | Control implication |
|---|---|---|
| Hosted provider versus self-hosted or internally developed | Who operates infrastructure, patches components, manages keys, and investigates incidents? | Allocate responsibilities explicitly; require provider evidence where operations are outsourced and internal hardening where they are not. |
| Data sensitivity and source | Does the system process public, internal, confidential, regulated, or safety-critical information? | Restrict destinations, retention, residency, retrieval scope, and human review as sensitivity rises. |
| Access and integration scope | Can it read repositories, call APIs, execute code, send messages, or change records? | Use least privilege, network controls, approval gates, and detailed action logging. |
| Autonomy and potential impact | Does it advise a person, or can it act without confirmation? | Require stronger testing, dual control, rate limits, transaction limits, and an immediate disablement path for autonomous functions. |
| Assurance and supplier responsibility | What independent assessments, security documentation, incident commitments, and dependency disclosures are available? | Make evidence and contractual obligations conditions of approval, not optional reading. |
| Monitoring, response, and recovery | Can the organization detect misuse, obtain logs, revert changes, and operate manually? | Do not expand exposure until those capabilities are tested and assigned to named owners. |
A practical implementation sequence
- Discover: inventory approved, embedded, and shadow AI uses; identify data flows, identities, integrations, and owners.
- Tier: rate each use by data sensitivity, autonomy, access scope, affected population, and consequence of failure.
- Set conditions: apply data, MFA, least-privilege, supplier, testing, logging, and recovery requirements for the tier.
- Validate: complete security, privacy, legal, and procurement reviews; test acceptance criteria and record residual risk.
- Operate: monitor changes and alerts, review access, maintain evidence, and rehearse incident and recovery actions.
- Reapprove: repeat the assessment when the model, provider, data, permissions, integrations, or business purpose changes.
How the major guidance fits together
NIST’s AI RMF, including its Generative AI Profile published July 26, 2024 and page updated April 8, 2026, is voluntary, cross-sector, and use-case agnostic. NIST says revision work is underway and has described an April 2026 concept note for a critical-infrastructure profile. Use it to structure governance and lifecycle risk work, not as a law or certification.
NIST CSF 2.0, published February 26, 2024, supplies high-level cybersecurity outcomes that can organize identity, protection, detection, response, and recovery around AI systems. CISA’s MFA guidance, the CISA-UK NCSC secure-development guidance, and the NSA-led AI data-security guidance add practical control areas. CISA’s general-use tip sheet is useful for communicating safe behavior but is not a complete enterprise policy.
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NSA Cybersecurity Director Dave Luber summarized the reason for this discipline in an April 15, 2024 NSA release: “AI brings unprecedented opportunity, but also can present opportunities for malicious activity.” The appropriate response is controlled expansion: increase capability only as ownership, protection, assurance, monitoring, and recovery mature with it.
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