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Before approving an AI tool for workplace use, complete a documented risk review that defines the use case, maps data and access paths, tests credible attack and failure scenarios, assigns controls and owners, and records an approval decision with operating conditions. The review should cover both ordinary application security and AI-specific threats; a product’s “AI” label is not a useful risk classification by itself.
Use the NIST Cybersecurity Framework (CSF) 2.0 to organize organization-wide cybersecurity outcomes and the NIST AI Risk Management Framework (AI RMF) to keep AI risks governed across design, deployment, use, and evaluation. These frameworks structure judgment; they do not certify a deployment or set one universal pass/fail score.
What the assessment must decide
Your review should end with a decision that someone with appropriate authority can defend later. It must state what the system does, where it sits in the business process, what information and permissions it receives, who could be harmed by an error or compromise, which controls are in place, and what residual risk the organization accepts.
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Define the system broadly. A workplace AI deployment can include a hosted model, an internal application, identity and access services, retrieval indexes, uploaded files, plugins, APIs, logs, feedback channels, telemetry, and suppliers. The model is only one component of the security boundary.
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Risk changes with the use case, data sensitivity, integrations, autonomy, scale, and consequences. A drafting assistant that sees public material is a different security decision from an agent that can read confidential records and send external messages. Record that distinction rather than assigning one risk label to every AI tool.
Use the NIST frameworks together
NIST CSF 2.0 is an outcome-oriented taxonomy for organizations of different sizes, sectors, and maturity levels. Its six functions provide a way to communicate accountability and security work. The AI RMF is a voluntary companion for managing AI-specific trustworthiness and risk throughout the system life cycle.
| Framework | Structure | How to use it in a deployment review |
|---|---|---|
| NIST Cybersecurity Framework 2.0 | Govern, Identify, Protect, Detect, Respond, Recover | Set governance and risk appetite, identify assets and dependencies, select protections, monitor for events, plan response, and verify recovery. |
| NIST AI Risk Management Framework 1.0 and its Playbook | Govern, Map, Measure, Manage | Define AI accountability, map context and affected parties, measure performance and risks, and manage or escalate residual risk across the life cycle. |
CSF 2.0 deliberately leaves implementation choices to the organization: The CSF does not prescribe how outcomes should be achieved.
Use the frameworks to organize evidence and decisions, then tailor controls to your threat model, legal context, architecture, and business impact.
Eight steps for a pre-deployment assessment
1. Define the use case and the decision to be made
Write a plain-language description of the proposed capability and the business process it enters. State:
- What the AI is expected to generate, classify, recommend, retrieve, or execute.
- Who will use it, who may be affected, and who is accountable for outcomes.
- Whether outputs influence employment, financial, safety, customer, security, or other consequential decisions.
- Whether the system can take actions directly or only provide information for a human to review.
- Expected volume, user population, geographic scope, and service hours.
- Allowed uses, prohibited uses, and escalation points when a request falls outside the approved purpose.
Identify the decision authority before testing begins. A security team can provide evidence, but business and risk owners must decide whether the remaining exposure fits the organization’s risk appetite.
2. Draw the system boundary and every data flow
Create a diagram and an inventory that follow information in both directions. Include the model or service, application code, hosting location, accounts, identity provider, plugins and tools, APIs, retrieval systems, data stores, monitoring services, and upstream or downstream suppliers.
Trace prompts, uploaded files, retrieved passages, generated outputs, user feedback, telemetry, support access, backups, and deletion requests. For each flow, record where data is processed, how long it is retained, who can see it, and whether it may be used for service improvement or training under the applicable terms.
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Tag confidential, personal, regulated, security-sensitive, and publicly available data separately. Decide whether each category is allowed, requires redaction, or is prohibited. Include copies in indexes, caches, logs, exports, and vendor support systems; a restriction applied only to the prompt field is not a complete data-control strategy.
3. Review ordinary cybersecurity exposure
Many AI risks overlap with familiar software and deployment risks. Assess confidentiality, integrity, and availability for the data and service, then inspect the controls that protect them:
- Authentication, authorization, role separation, and least-privilege permissions.
- Secrets storage, key rotation, tenant isolation, and secure configuration.
- Network exposure, API gateways, rate limits, and egress restrictions.
- Application and software dependencies, patching, build provenance, and change control.
- Logging that captures security-relevant events without unnecessarily duplicating sensitive content.
- Backup, restoration, business continuity, and incident-response procedures.
- Supplier access, subcontractors, support channels, and contractually stated security and data-use terms.
Check these controls in the actual product configuration and deployment, not only in a vendor’s general description. A well-designed service cannot compensate for excessive permissions, exposed storage, weak identity controls, or an unmonitored integration.
4. Add AI-specific threat scenarios
Write scenarios that describe how an attacker, user, supplier, or failure could defeat the intended safeguards. Include at least the following areas:
- Prompt injection and instruction confusion: untrusted text in a document, web page, message, or retrieved passage attempts to override system instructions or redirect an agent.
- Evasion: crafted inputs cause a classifier or other model to miss malicious or prohibited content.
- Model extraction: repeated queries or exposed interfaces are used to reproduce model behavior or reveal sensitive implementation details.
- Data leakage and memorization: prompts, retrieval results, outputs, logs, or model behavior disclose confidential or personal information.
- Membership inference: an attacker tries to determine whether a person’s or organization’s data appeared in training or other model inputs.
- Poisoning: training, fine-tuning, retrieval, feedback, or reference sources are altered so that outputs become misleading or unsafe.
- Insecure output handling: generated text, code, markup, queries, or commands are passed to another system without validation or escaping.
- Tool and agent misuse: a model uses an allowed connector in an unintended way, takes an irreversible action, or chains permissions across systems.
- Abuse at scale and availability attacks: automated use consumes quotas, drives up cost, degrades service, or prevents legitimate work.
These scenarios matter because some machine-learning attack areas, including evasion, model extraction, membership inference, and availability, are not comprehensively covered by traditional security guidance.
5. Estimate likelihood, impact, and uncertainty
For every plausible scenario, document the threat actor or failure mode, the weakness that enables it, the affected asset or person, the business consequence, the likelihood estimate, and the impact estimate. Explain the evidence behind each judgment. If evidence is weak, record uncertainty instead of presenting a precise score that implies false accuracy.
Prioritize scenarios by potential harm and exposure. Consider direct losses, privacy and safety consequences, regulatory or contractual duties, operational interruption, downstream decisions made from bad output, and the difficulty of detecting or reversing the event.
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| Risk-register field | What to record | Useful evidence |
|---|---|---|
| Scenario | A specific attack or failure, such as prompt injection causing an unauthorized tool call. | Threat model, abuse case, architecture diagram, test case |
| Weakness and exposure | The permission, data flow, configuration, dependency, or process that makes the scenario possible. | Configuration review, access listing, code review, supplier documentation |
| Impact | Who or what could be harmed and the operational, financial, privacy, safety, or security consequence. | Business impact analysis, data classification, process owner assessment |
| Likelihood and uncertainty | A reasoned estimate with assumptions and confidence limits, rather than an unexplained number. | Test results, incident history, threat intelligence, expert judgment |
| Controls and residual risk | Preventive, detective, and recovery measures, followed by the exposure that remains. | Configuration evidence, test output, monitoring design, recovery exercise |
| Owner and due date | The person accountable for treatment and the date for completion or reassessment. | Risk-acceptance record, change ticket, governance minutes |
There is no universal quantitative threshold for approving workplace AI. A score is useful only when the organization defines its scale, assumptions, and decision authority.
6. Select controls and assign residual-risk owners
Map each scenario to controls that prevent the event, detect it, or limit recovery time and damage. Common measures include:
- Minimizing the data sent to the system and redacting identifiers or secrets.
- Restricting users, tools, network destinations, file types, and actions by role.
- Configuring tenant separation, retention, deletion, and service-improvement settings deliberately.
- Requiring human approval before consequential, external, financial, or irreversible actions.
- Validating, escaping, and policy-checking outputs before another system executes them.
- Isolating retrieval sources, approving changes to indexes, and checking source integrity.
- Applying rate limits, quotas, anomaly detection, and abuse controls.
- Maintaining security tests, monitoring, alerting, backups, rollback capability, and an incident playbook.
- Putting data-use, breach notification, support access, subcontractor, and change-notice commitments into contracts where appropriate.
For each control, name an owner, required evidence, implementation date, and verification method. Treat vendor features as claims to validate in your tenant and architecture; do not assume a setting is enabled or sufficient because it appears in product documentation.
7. Test before launch and set operating conditions
Test representative misuse and failure cases, not only normal prompts. At minimum, verify:
- Access boundaries between users, roles, tenants, tools, and data classes.
- Behavior when prompts or retrieved content contain conflicting or malicious instructions.
- Handling of sensitive inputs, outputs, logs, exports, backups, and deletion requests.
- Output validation before code, queries, messages, or transactions reach another system.
- Logging, alerting, rate limits, escalation, and evidence preservation.
- Backup restoration, service fallback, rollback, and account or integration disablement.
Set operating conditions in writing. Specify who may approve model, prompt, connector, data-source, or configuration changes; which incidents trigger suspension; what drift or supplier changes require review; and how often the assessment is repeated. NIST’s AI RMF guidance places trustworthiness considerations in pre-design, development, deployment, use, and test and evaluation, so approval is not a one-time sign-off.
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Link each material scenario to its evidence, controls, owner, due date, residual risk, and decision authority. Choose one of these outcomes, with conditions where needed:
- Approve: evidence supports the intended use and residual risk is accepted.
- Approve with conditions: launch is limited by controls such as data restrictions, human review, user groups, rate limits, or a short review interval.
- Limited pilot: exposure is constrained while specified tests and monitoring gather evidence.
- Additional controls required: deployment waits for identified safeguards or supplier commitments.
- Defer or reject: consequences, uncertainty, or uncontrollable dependencies exceed the organization’s tolerance.
Record assumptions, scope, evidence dates, exceptions, and the next review date. A decision that cannot be reconstructed from the record is difficult to govern when the model, supplier, data, or business process changes.
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Compare deployment options on security-relevant axes
When choosing between a managed AI service, organization-controlled hosting, or a constrained pilot, compare the options using the same questions. The point is not to declare one architecture universally safer; it is to expose trade-offs that fit your context.
| Axis | Managed service: verify | Organization-controlled hosting: verify |
|---|---|---|
| Data exposure and retention | Processing region, retention defaults, deletion behavior, support access, and whether prompts or outputs are used for service improvement. | Where data, logs, caches, and backups reside; who administers them; and how deletion is enforced. |
| Identity and tenant isolation | Single sign-on, role controls, tenant boundaries, privileged support paths, and audit evidence. | Administrative separation, key management, network segmentation, and evidence that one workload cannot reach another. |
| Integrations and tool permissions | Connector scopes, outbound destinations, approval gates, and limits on agent actions. | Service accounts, firewall and egress policy, secrets handling, sandboxing, and code or package provenance. |
| Logging and incident support | Available event detail, export and retention limits, alerting, support escalation, and breach-notification terms. | Events collected, protected, correlated, monitored, and retained by your own teams. |
| Testing and change evidence | Independent assessments, release notices, model-change controls, evaluation results, and your ability to test the configured service. | Model, code, data, and infrastructure testing; patching; release approval; and rollback responsibility. |
| Supplier and contractual risk | Subcontractors, data-use terms, availability commitments, audit rights, and termination or export assistance. | Licenses, maintainers, supply-chain dependencies, support arrangements, and responsibility for vulnerabilities. |
| Consequence of error or compromise | What the provider can suspend or restore and what remains your responsibility. | What your team can isolate, restore, or investigate within the required time. |
Use the same use-case boundaries and risk scenarios for each option. A lower-risk architecture on paper can become high risk when configured with broad permissions or unreviewed data sources.
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Classifying the tool instead of the use
“It is only a chatbot” says nothing about the records it can access or actions it can trigger. Reclassify the deployment by data, integrations, autonomy, scale, and consequence.
Reviewing procurement but not configuration
Security questionnaires and contract language do not prove that retention, logging, tenant controls, or connectors are configured as intended. Capture screenshots, policy exports, test results, and access listings from the actual environment.
Trusting the model to police itself
Instructions and refusals are not authorization boundaries. Enforce permissions outside the model, validate outputs before execution, and require human approval for consequential actions.
Ignoring secondary copies
Prompts and files can persist in retrieval indexes, logs, backups, exports, and support systems. Map and control every copy, not just the main conversation screen.
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Models, suppliers, connectors, prompts, data sources, and business processes change. Define reassessment triggers, monitoring, suspension criteria, and a review cadence before launch.
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Inventing precision
No reviewed official guidance supplies a universal probability or cost for workplace AI incidents. Use documented assumptions and uncertainty; do not present an unsupported percentage as a benchmark.
Current NIST guidance and its limits
NIST published CSF 2.0 on February 26, 2024. It describes high-level outcomes rather than a mandatory control recipe.
The AI RMF Playbook is a voluntary companion based on AI RMF 1.0, released January 26, 2023. NIST says the Playbook will be updated after the AI RMF is revised, so treat it as adaptable guidance rather than a certification checklist.
NIST SP 1353, Quick-Start Guide for Using Artificial Intelligence (AI) for CSF Analysis and Reporting, was issued as an initial public draft on August 19, 2026, with comments due October 15, 2026. Its examples concern using AI to analyze and report against the CSF; they are not a complete methodology for securing an AI system deployed at work.
NIST’s Cybersecurity Framework Profile for AI search result was an initial preliminary draft from December 2025. Verify its publication status before treating it as finalized guidance.
These documents do not establish organization-specific legal duties, vendor security guarantees, or a universal quantitative risk threshold. Resolve those questions for the jurisdiction, sector, data, architecture, and intended use of the actual deployment.
Quick Recap
A launch checklist for the review owner
- The use case, affected people, prohibited uses, autonomy, and decision authority are written down.
- The system boundary and all prompt, file, retrieval, output, telemetry, log, backup, and supplier flows are mapped.
- Data classes, retention, deletion, tenant isolation, identity, permissions, secrets, network exposure, dependencies, patching, and recovery are verified in the configured environment.
- AI-specific scenarios cover injection, evasion, extraction, leakage, memorization, membership inference, poisoning, unsafe output handling, tool misuse, abuse, and availability.
- Likelihood, impact, assumptions, and uncertainty are recorded without unsupported precision.
- Preventive, detective, and recovery controls have owners, evidence requirements, and due dates.
- Misuse, access boundaries, data handling, logging, escalation, restoration, and rollback have been tested.
- Approval conditions, suspension triggers, change authority, supplier-change review, monitoring, and reassessment dates are defined.
- The final decision and residual-risk acceptance are traceable to evidence.
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