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Before expanding artificial-intelligence use, require a documented use-case assessment, accountable ownership, classified-data rules, phishing-resistant access controls, supplier and software-lifecycle checks, security testing, continuous monitoring, and a rehearsed response and recovery plan. Apply the controls proportionately to the data, integrations, autonomy, and consequences involved; no single framework or product is a universal checklist.
Start with a risk decision, not a tool purchase
AI should enter the same enterprise security and risk program used for other technology, with additional controls for model behavior, training and retrieval data, prompts, outputs, agents, and third-party integrations. Treat each proposed use as a separate system: a public writing assistant, an internal retrieval chatbot, an AI coding service, and an autonomous workflow have different failure modes and access requirements.
Use a written approval record before a pilot or production connection. Reassess it whenever the model, provider, data sources, users, integrations, or level of automation changes. NIST’s voluntary AI Risk Management Framework (AI RMF) organizes this work into Govern, Map, Measure, and Manage. Its companion Playbook supplies suggested actions, not mandatory rules. NIST’s AI RMF Generative AI Profile was published July 26, 2024; the NIST page was updated April 8, 2026 and notes ongoing revision work. It is cross-sector guidance, not a law or certification.
Minimum information to record for every use case
- Purpose and owner: the business task, accountable executive, technical owner, and security contact.
- People and process: affected employees, customers, patients, citizens, or other groups; decisions the system may influence; and required human review.
- Data: classification, source, retention, residency, regulated categories, intellectual-property status, and whether data enters prompts, retrieval indexes, fine-tuning sets, logs, or outputs.
- System map: model and version, provider or hosting location, APIs, plugins, agents, identity provider, storage, data pipelines, and downstream systems.
- Access and autonomy: user populations, service accounts, network paths, write permissions, transaction limits, and whether the system can act without approval.
- Impact: plausible effects of incorrect, unavailable, manipulated, or disclosed results; reversibility; and notification obligations.
- Decision: approved, approved with conditions, limited pilot, deferred, or rejected, including an exception owner and expiry date.
Use a framework as a map, not a universal checklist
NIST Cybersecurity Framework (CSF) 2.0, published February 26, 2024, provides high-level cybersecurity outcomes rather than a prescribed implementation. It can organize governance, identification, protection, detection, response, and recovery around an AI service, while the AI RMF adds lifecycle-specific risk analysis. Neither tells every organization exactly which control, vendor contract, or technology to buy.
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Sector rules, privacy law, records obligations, employment requirements, export controls, and contractual duties still require separate legal and compliance review. NIST’s landing page describes revision work and an April 2026 concept note for a critical-infrastructure profile, so record the edition and date used in your policy.
Assign decision rights
- A business owner accepts the residual risk and confirms the use is necessary.
- Security defines technical safeguards, testing, logging, and incident thresholds.
- Privacy, legal, records, and compliance teams decide what data and decisions are permitted.
- Procurement and third-party risk teams verify provider commitments and remedies.
- Operations owns monitoring, on-call escalation, rollback, and service recovery.
Document who can pause or disable an AI system. An approval that lacks a named shutdown authority is incomplete.
Protect information before it reaches a model
Classify information before it is entered into prompts, uploaded for retrieval, used in training or fine-tuning, written to logs, or passed into an output workflow. A practical policy should state which categories are prohibited, which require an approved service and contractual protection, and which may be used only in a controlled internal environment.
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Define handling rules
- Block secrets, credentials, authentication tokens, payment data, health information, unreleased financial results, and confidential client material from unapproved external tools.
- Specify which internal repositories may be indexed and which users may retrieve each collection.
- Limit export, copy, download, and sharing of generated results; label outputs that contain sensitive or regulated information.
- Set retention and deletion periods for prompts, files, embeddings, conversations, telemetry, backups, and provider-side records.
- Require a review of provider access, training use, retention, geographic processing, subprocessors, breach notice, and deletion terms before sensitive data is enabled.
CISA’s September 2024 Stay Safe Online When Using AI tip sheet gives plain-language advice not to share sensitive or confidential information with generative-AI tools. Organizations need a more detailed internal standard that names their data classes and approved services.
Maintain provenance and integrity
For systems built or operated internally, record where datasets, documents, prompts, and model artifacts came from. Restrict who can add or revise source material, require trusted infrastructure, and preserve version history and checksums where appropriate. The joint AI data-security guidance described in an NSA release on May 22, 2025 highlights data supply-chain risks, malicious modification, trusted revisions, and drift. Monitor for unexpected changes in source distributions, retrieval results, model behavior, or output quality; define a threshold that triggers investigation or rollback.
Require strong identity and least privilege
Apply identity controls to the AI interface, administration console, source repositories, model registry, orchestration layer, APIs, plugins, agents, and every connected business system. Require multi-factor authentication (MFA) for users and service administrators. Prioritize phishing-resistant MFA for administrators and people handling sensitive data. CISA’s business guidance lists physical security keys as its strongest option among the methods it describes, followed by authenticator applications, biometrics, and text or email codes. CISA also advises businesses to work with their IT team to select a method that fits their needs.
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Design permissions around actual actions
- Give users access only to the models, workspaces, and data collections required for their role.
- Separate development, test, and production credentials and environments.
- Use short-lived, scoped tokens for APIs; store secrets in an approved secrets manager rather than prompts or source code.
- Constrain agents and plugins to an allow-list of tools, destinations, commands, and transaction values.
- Require human approval for external messages, financial actions, record changes, code deployment, or other high-impact operations.
- Review entitlements regularly and remove access promptly when a person, service, project, or supplier no longer needs it.
When comparing MFA methods, evaluate phishing resistance, compatibility with the organization’s identity provider and devices, administration at scale, account-recovery controls, and accessibility. A physical FIDO2 security key can be appropriate where the identity platform supports it, but the key alone is not an AI security program.
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Security review must cover hosted providers, internally developed models, open-source components, APIs, data connectors, vector stores, plugins, and infrastructure. Ask for evidence rather than accepting a generic “secure AI” statement.
Questions for a provider or internal engineering team
- Who owns security for the model, application, infrastructure, connectors, and customer configuration?
- Are customer prompts, files, outputs, or telemetry used for training or product improvement? What retention, deletion, residency, and subprocessor terms apply?
- How are administrators and service accounts authenticated, authorized, and audited?
- How are models, packages, containers, datasets, and dependencies inventoried, signed, scanned, patched, and rolled back?
- How are vulnerabilities reported, triaged, fixed, and disclosed? What support period and end-of-life notice are provided?
- What logs are available, how quickly can the organization obtain them, and can they be exported to its monitoring system?
- What incident-notification time, cooperation, evidence preservation, and customer-recovery commitments are contractual?
- How can the service be disabled, data disconnected, or workloads migrated if the provider is unavailable or terms change?
CISA and the UK National Cyber Security Centre’s secure-AI-system-development guidance, announced November 26, 2023, spans design, model and system development, deployment, and operation. It is aimed primarily at providers but is useful to managers and risk owners evaluating supplier practices. NIST IR 8596, the Cyber AI Profile, was an initial preliminary draft published December 16, 2025, with further work announced during 2026; treat it as draft material, not a finalized requirement.
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Test before launch and after every material change
Write acceptance criteria from the use-case assessment. Testing should cover the actual data, permissions, integrations, and failure consequences rather than only a model’s answer quality.
- Verify data boundaries: attempt to submit prohibited data, retrieve another user’s records, extract hidden instructions, and make logs or outputs reveal secrets.
- Verify identity and authorization: test MFA enforcement, role changes, disabled accounts, service-token scope, tenant separation, and administrative actions.
- Exercise misuse and failure scenarios: include prompt injection, malicious documents, poisoned retrieval content, unsafe tool calls, model unavailability, incorrect outputs, and dependency compromise as appropriate to the system.
- Check integrations: confirm that API timeouts, retries, rate limits, input validation, output encoding, and transaction approvals prevent cascading or duplicate actions.
- Test human controls: ensure reviewers can understand, challenge, override, and document a result before a consequential decision is executed.
- Record evidence: retain test scope, findings, risk acceptance, remediation owners, retest results, and the approval decision.
Repeat testing after a model or provider change, new data source, permission expansion, connector addition, major prompt or policy change, or incident. Do not treat a vendor’s general assurance report as proof that your configuration is safe.
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AI risk changes during operation. Monitor model and data versions, retrieval sources, connected tools, permissions, provider terms, usage patterns, anomalous requests, sensitive-data exposure, and security events. Keep logs detailed enough to reconstruct who asked for what, which model and data were used, what tools ran, what output was returned, and which human approved an action. Protect logs from unauthorized alteration and set retention consistent with legal and operational needs.
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Define an AI incident playbook
- Triggers: suspected data disclosure, account takeover, malicious data change, unsafe autonomous action, model drift, provider breach, or unexplained integrity failure.
- Containment: revoke tokens, disable connectors or agents, isolate data sources, suspend high-risk workflows, and preserve evidence.
- Decision authority: name the person who can disable the service immediately and the person who accepts reactivation risk.
- Notification: identify security, privacy, legal, customers, regulators, suppliers, and executives who must be informed, with applicable deadlines determined for the organization’s jurisdiction and contracts.
- Recovery: restore trusted model and data versions, validate permissions, test outputs, and return to service in stages.
- Learning: document root cause, update controls and training, and reassess every affected use case.
The AI RMF treats risk management as continuous across the lifecycle. The May 2025 joint AI data-security guidance likewise identifies malicious data changes and drift as concerns, so monitoring cannot end at deployment.
Tailor the baseline to deployment and impact
There is no inherently secure choice between a hosted service and a self-hosted or internally developed system. Compare the operating model on the dimensions below, then apply stricter controls where exposure or consequences are greater.
| Decision axis | Questions that change the control set |
|---|---|
| Hosting and responsibility | Who operates infrastructure, patches dependencies, manages backups, and can access data or logs? |
| Data sensitivity and source | Is data public, internal, confidential, regulated, or proprietary, and can its provenance be verified? |
| Access and integration scope | Is the system isolated, connected to repositories, or able to write to production systems? |
| Autonomy and impact | Does it advise a person, or can it make decisions, send communications, move money, change code, or alter records? |
| Assurance and supplier duties | What independent evidence, contractual commitments, vulnerability process, and incident cooperation are available? |
| Monitoring and recovery | Can the organization detect misuse, obtain usable logs, roll back data and models, and operate without the service? |
A low-impact public-information assistant may need fewer controls than an AI system handling health records or executing production changes, but both still require an owner, approved data flows, access control, testing, monitoring, and a disablement path.
A practical approval gate for expansion
- Inventory: list every existing and proposed AI tool, embedded feature, API, model, connector, and agent.
- Classify: map data, users, integrations, autonomy, jurisdictions, and consequences.
- Set conditions: define allowed data, MFA, least-privilege roles, human approvals, logging, retention, supplier terms, and test requirements.
- Test and review: run use-case-specific security and misuse tests; obtain privacy, legal, procurement, and business-owner sign-off where applicable.
- Launch with limits: use a staged rollout, rate and transaction limits, alerts, and a documented rollback or disablement procedure.
- Reassess: review on a schedule and whenever the model, data, provider, permissions, integrations, or impact changes.
Expansion is justified when the organization can demonstrate those controls and operate them, not merely when a model performs well in a demonstration.
Quick Recap
What to avoid
- Allowing staff to paste sensitive information into an unapproved public service.
- Giving an agent broad production credentials because a pilot worked with test data.
- Relying on a framework name as proof of compliance or security.
- Accepting a provider’s marketing claims without data-handling, logging, incident, and exit details.
- Launching without an owner who can stop the system and without tested recovery.
- Assuming a model update, new connector, or changed provider terms is harmless; each can alter the risk assessment.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
