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Build one living assessment for every AI use case, not a one-time questionnaire. Start with an inventory and accountable owner; record the purpose, users, data, integrations, model and lifecycle stage; map threats, harms and legal constraints; score likelihood and impact with documented evidence; assign controls and treatment owners; obtain an explicit authorization decision; and monitor for drift, incidents and material changes.
This guide turns that workflow into an operating process for security, IT, privacy, legal, procurement and business teams. It uses the voluntary NIST AI Risk Management Framework (AI RMF) as the organizing model, connects it to NIST’s seven-step security lifecycle, and shows what to capture in a register so an approval can be defended months later.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchWhat is an AI risk assessment, and why do security and IT teams need one?
An AI risk assessment is a documented decision process for a particular use case, model or AI-enabled feature. It is not merely a vendor security review. The record should show what the system is intended to do, who can be affected, what could go wrong, which safeguards are operating, how much uncertainty remains and who accepted that residual risk.
The NIST AI Risk Management Framework 1.0, published January 26, 2023, is voluntary, non-sector-specific and intended for organizations that design, develop, deploy or use AI. NIST says its purpose is to improve the ability to incorporate trustworthiness considerations into the design, development, use and evaluation of AI products, services and systems. See the NIST AI RMF page and the AI RMF 1.0 publication.
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Security and IT teams need the assessment because an AI system changes familiar attack surfaces and creates new dependencies: prompts and outputs, retrieval stores, model APIs, plugins, training data, cloud infrastructure, vendor updates and human decisions. A system can be accurate in a demonstration yet expose confidential data, make an unsafe recommendation, fail silently after a model change or give an attacker a path into connected systems. A living register makes those conditions visible to the people who must approve, operate, pause or retire the system.
How do you start an AI risk assessment?
Begin with one record for each model, vendor feature, workflow or embedded AI capability. Do not wait for a central “AI project” label; discover uses through procurement, architecture reviews, data-loss-prevention alerts, software inventories and employee disclosures.
1. Create an inventory record
Give every use case a stable system ID and capture:
- Business purpose and the decision or task the AI supports.
- Accountable business owner and technical owner.
- Provider, model name and exact version, including whether the provider can change it without notice.
- Deployment location, tenancy and jurisdictions in which it operates.
- Users, affected people and any human decision point.
- Data classes sent to the model, stored in prompts, used for retrieval or returned in outputs.
- Connected systems, APIs, plugins, identity providers, storage and downstream consumers.
- Lifecycle stage: proposed, piloting, production, suspended or retiring.
- Your role: developer, deployer, procurer or user.
Record the intended use and at least one foreseeable misuse. A general-purpose assistant used for drafting internal text has a different exposure from an automated eligibility, safety or employment decision, even when the underlying model is identical.
2. Define the assessment boundary
State what is inside the review and what remains a supplier responsibility. Include the model, prompts, orchestration code, retrieval index, data pipelines, user interface, connected tools, operators and the decisions influenced by outputs. If a supplier hosts the model, document the hand-off points for logging, incident notification, vulnerability remediation, data deletion and service continuity.
Who must govern the assessment and approve residual risk?
Governance is effective only when someone has authority to approve, pause, remediate or retire the use case. Name those authorities before testing starts.
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Assign accountable roles
- Business sponsor: owns the purpose, affected outcomes and funding for treatment.
- Security: evaluates threats, identity, vulnerabilities, monitoring and incident response.
- IT and architecture: verifies deployment design, integrations, resilience and change control.
- Privacy and legal: identifies personal-data, sector, contract and jurisdictional obligations.
- Procurement and vendor management: obtains supplier evidence, terms, subprocessor information and notification commitments.
- Data science or engineering: explains model behavior, data provenance, testing limits and version changes.
- Independent assessor: checks that evidence supports the claimed controls and is not simply accepting the builder’s assertion.
- Authorizing official: accepts residual risk, imposes conditions or rejects the deployment.
Publish escalation paths for a serious incident, an unapproved model change, a control failure and a new legal restriction. Set a review cadence and a maximum approval period so an authorization cannot remain open indefinitely.
NIST’s crosswalk to ISO/IEC 42001 links inventory, roles, competence, awareness, communication and leadership accountability to management-system requirements. Keep those responsibilities in the register and in operating procedures rather than relying on an informal committee.
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How do you map AI harms, threats and obligations?
Describe both cybersecurity events and consequences to people or the business. A threat is an action or condition that can cause harm; a harm is the resulting effect, such as a privacy breach, unsafe recommendation or discriminatory outcome.
Map the trustworthiness dimensions
- Confidentiality and privacy: Can prompts, training material, retrieval documents or outputs reveal restricted information?
- Integrity: Can an attacker or faulty pipeline alter data, prompts, model files, policies or tool calls?
- Availability and resilience: What happens during an outage, rate limit, provider withdrawal or connectivity loss?
- Safety and reliability: Could an incorrect or unstable output cause physical, financial or operational harm?
- Fairness and accessibility: Are error rates or access barriers uneven for affected groups?
- Explainability and human oversight: Can a reviewer understand the basis for an output and override it?
- Authenticity and provenance: Can users distinguish generated content, and can you trace model and data versions?
Map dependencies and misuse
Draw a data-flow and trust-boundary diagram. Include training and fine-tuning data, retrieval stores, APIs, plugins, cloud infrastructure, identity systems, logging destinations, vendors and downstream users. Test misuse cases such as prompt injection, jailbreaks, malicious documents in a retrieval corpus, excessive tool permissions, account takeover, model extraction, poisoning and deliberate generation of unsafe content.
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List sector rules, contractual promises, records-management duties and jurisdiction-specific requirements that apply to the data, users or decision. A legal prohibition or mandatory human review is a constraint, not a factor that can be averaged away by a low probability. Record the source, effective date, interpretation and owner for each obligation.
How should AI risk be scored?
No source establishes a universal numeric probability or impact scale. Choose a repeatable organizational scale, publish definitions and calibrate it with examples from your own incidents and control tests.
A practical five-band design
One workable design uses five bands for likelihood and impact. Treat these as an internal convention, not an industry-standard measurement.
| Band | Likelihood definition | Impact definition |
|---|---|---|
| 1 — Rare / minimal | Requires unusual conditions; controls are well tested. | Negligible disruption, no material harm and rapid recovery. |
| 2 — Unlikely / minor | Could occur under specific conditions or through a known weakness. | Limited users, data or service impact that existing response can contain. |
| 3 — Possible / moderate | Credible during the normal life of the system. | Material operational, privacy, financial or fairness effect requiring management action. |
| 4 — Likely / major | Expected without additional treatment or has occurred in comparable systems. | Serious harm, regulatory exposure, broad outage or loss of trust. |
| 5 — Almost certain / severe | Already occurring, trivially exploitable or inevitable under planned use. | Catastrophic safety, rights, financial or mission impact. |
Multiply or otherwise combine the two dimensions only after recording evidence confidence. A score based on a small test set should not look as certain as one supported by production telemetry. Keep assumptions, test dates, affected populations, control effectiveness and unresolved uncertainty beside the number.
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Define what each result means before a project asks for approval:
- Acceptable with conditions: launch is allowed only with named controls, monitoring and an expiry or review date.
- Needs remediation: the system remains in a sandbox or limited pilot until specified findings are closed or formally compensated.
- Reject or retire: the use is prohibited, or an existing deployment is stopped, when obligations cannot be met or residual risk exceeds authority.
Do not let a low average conceal a high-severity tail risk. Record separate risks for confidentiality, integrity, availability, privacy, fairness, safety and legal compliance when their owners or treatments differ.
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Which controls should security and IT test before approval?
Controls must be demonstrated in the actual architecture and operating process, not copied from a supplier brochure.
Baseline security controls
- Strong identity, least-privilege access and separate administrator, developer and runtime accounts.
- Secrets management, key rotation, network isolation and restrictions on outbound connections.
- Input and output handling that prevents sensitive data from entering unapproved services and prevents unsafe output from reaching automated actions.
- Vulnerability management for model-serving software, dependencies, containers, plugins and infrastructure.
- Immutable or access-controlled logs for prompts, tool calls, model versions, approvals, overrides and incidents, with privacy-aware retention.
- Backups, recovery tests, service-rate-limit handling and a manual fallback for critical work.
- Supplier controls covering subprocessors, breach notification, model changes, support access, deletion and evidence of testing.
- Incident playbooks that identify who can disable the model, revoke credentials, preserve evidence and notify affected parties.
Joint guidance from the NSA AI Security Center, CISA, FBI, ASD ACSC, CCCS, NCSC-NZ and NCSC-UK emphasizes improving confidentiality, integrity and availability, mitigating known vulnerabilities, and providing ways to protect, detect and respond to malicious activity. The guidance is available through Deploying AI systems securely.
Additional tests for generative AI
- Prompt-injection and jailbreak tests using untrusted documents, user content and multi-turn conversations.
- Sensitive-data leakage tests, including attempts to recover system prompts, retrieval records or memorized content.
- Output validation for code, commands, personal-data fields, citations and any content that can trigger an action.
- Explicit allowlists and confirmation steps for retrieval, plugins, browsers, messaging, payments or other tools.
- Model and data provenance, version pinning where possible, evaluation sets and red-team evidence.
- Abuse monitoring, rate controls, safe fallback paths and a way for users to report harmful or incorrect output.
Controls for general-purpose models with systemic risk
Where a provider’s model may create systemic risk in the European Union, European Commission guidance calls for standardized evaluation, adversarial testing, systemic-risk mitigation, serious-incident reporting and cybersecurity safeguards. Confirm which obligations apply to your role and region rather than assuming that a provider’s classification transfers automatically to your organization. See the European Commission guidance on general-purpose AI providers.
How do NIST’s AI RMF functions fit the security lifecycle?
Use the four AI RMF functions to organize risk thinking, then use NIST’s seven-step Risk Management Framework (RMF) to make security decisions repeatable. The AI RMF is broad enough for design, development, deployment and use; the RMF supplies concrete gates for system authorization and continuous monitoring.
| AI RMF function | What your team does | Related RMF steps |
|---|---|---|
| Govern | Set policy, roles, authority, competence, communication, documentation and accountability. | Prepare; Categorize; Authorize |
| Map | Describe context, intended use, affected people, dependencies, harms, threats and obligations. | Prepare; Categorize |
| Measure | Test performance, security, privacy, fairness, reliability and control effectiveness; document uncertainty. | Select; Implement; Assess |
| Manage | Prioritize treatment, accept or reject residual risk, respond to incidents and monitor change. | Implement; Authorize; Monitor |
NIST’s security RMF has seven steps: Prepare, Categorize, Select, Implement, Assess, Authorize and Monitor. NIST describes it as a comprehensive, flexible, repeatable and measurable process for information-security and privacy risk; the current project page is at NIST Risk Management Framework.
ISO/IEC 23894:2023, published February 6, 2023, provides AI-risk guidance for organizations developing, producing, deploying or using AI products, systems and services. Its abstract is available from the International Organization for Standardization. Use it when your management system, customer contracts or auditors require an ISO-aligned vocabulary; it does not replace your technical tests.
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The UK AI Risk Management Toolkit, published September 8, 2026, includes guidance and a workbook for multidisciplinary teams. It can help a UK-facing organization structure workshops and evidence collection; always verify which legal requirements apply to your specific use. See the UK AI Risk Management Toolkit.
How should assessment, authorization and evidence review work?
Separate the person who builds or sponsors the system from the person who assesses the evidence. Independence does not require an external auditor; it requires enough separation to challenge assumptions and reject incomplete tests.
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- Assemble the evidence package. Include architecture and data-flow diagrams, inventory fields, threat and harm analysis, test plans and results, supplier responses, control configurations, incident procedures, user training and open findings.
- Run the assessment. Reperform critical tests, sample logs, inspect access and secrets, verify recovery and challenge the claimed scope. Mark evidence as current, partial, stale or unavailable.
- Write the decision record. State the approved purpose, users, geography, model/version, prohibited uses, operating conditions, compensating controls, residual risks, expiry or review date and named authorizing official.
- Track conditions to closure. Give every finding an owner, due date, severity and retest requirement. A conditional approval is not complete until those conditions are either closed or explicitly reaccepted by an authorized person.
NIST SP 800-37 explains categorization, control selection, implementation, assessment, authorization and continuous monitoring with explicit accountability. The publication is available at NIST SP 800-37.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should an AI risk register contain?
Use one row per material risk, linked to one inventory record. Keep evidence links and test dates in the same system so an approver can reconstruct why a decision was made.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute| Column | What to record |
|---|---|
| System ID and AI use case | Stable identifier, purpose, workflow and lifecycle stage. |
| Owner and provider/model/version | Accountable business and technical owners, supplier, model and change behavior. |
| Users and affected people | Operator groups, decision subjects, vulnerable populations and human review points. |
| Data classes and integrations | Personal, confidential, regulated or public data; APIs, retrieval, plugins and downstream systems. |
| Jurisdiction and obligations | Countries, sectors, contracts, retention rules and required human or reporting steps. |
| Threat or harm | Specific event, consequence, affected population and foreseeable misuse. |
| Likelihood, impact and evidence confidence | Defined bands, rationale, assumptions, test date and uncertainty. |
| Existing controls and control test/date | Implemented safeguards, responsible owner, test result and next test. |
| Residual risk and treatment | Risk after controls, remediation, compensating control or acceptance decision. |
| Due date and approver | Named treatment owner, deadline and authorizing official. |
| Monitoring metric and next review | Operational signal, threshold, review date and escalation route. |
| Retirement trigger | Conditions for shutdown, access revocation, record preservation and deletion verification. |
How do you monitor an approved AI system?
Define monitoring before launch and connect each metric to an action. Useful signals include incident count and severity, model or data drift, error and override rates, measurable false positives and negatives, unusual access, data-quality indicators, control-test results, supplier changes and unresolved findings.
Trigger reassessment after a model, data set, prompt, integration, owner, user population, threat or legal requirement changes. An apparently minor prompt-template change can alter tool permissions or the type of data exposed, so classify changes by their potential effect rather than by the developer’s estimate of effort.
When retiring a system, disable integrations, revoke keys and accounts, preserve records required for investigations or regulation, notify users and verify deletion or retention obligations with the provider and internal storage owners. Record the retirement decision and any lessons that should change controls for future systems.
How should teams compare candidate AI systems?
Compare candidates against the same use case and evidence standard. A cheaper or more capable model is not automatically safer if it has weaker transparency, recovery or change controls.
| Comparison axis | Questions to ask |
|---|---|
| Regulatory exposure | Which jurisdictions, sectors and decision rules apply, and can the design meet them? |
| Security and privacy | Where is data processed, who can access it, how are secrets and logs protected, and how is deletion verified? |
| Reliability and performance evidence | Are evaluation data, error bounds, limitations and version-specific results available? |
| Explainability and human oversight | Can a reviewer understand, challenge and override an output before harm occurs? |
| Resilience and recoverability | What are the outage, rate-limit, provider-change and rollback procedures? |
| Vendor transparency and provenance | Are model, data, subprocessor, incident and change-notification details documented? |
| Integration effort and operating cost | What identity, logging, isolation, testing and support work is required over the full lifecycle? |
| Control maturity and residual risk | Which controls are evidenced, which are promised, and what remains after treatment? |
Keep the decision outcomes distinct: acceptable with conditions, needs remediation, and reject or retire. This prevents procurement pressure from turning an unresolved technical or legal issue into an informal acceptance.
What commonly goes wrong, and how can you fix it?
The register lists tools but not use cases
A software inventory may show a provider but not the workflows, data or decisions using it. Require a separate record whenever purpose, users, data or integrations differ, even if the same model is involved.
The score is precise but unsupported
A decimal risk rating based on a short demo creates false confidence. Store the evidence confidence, assumptions and test date, and lower confidence or increase treatment when production conditions are unknown.
Security reviews the model but not the surrounding system
Attackers often target identity, retrieval stores, plugins, logs or deployment pipelines rather than model weights. Make the data-flow diagram and connected services part of the assessment boundary.
Approval has no expiry or change trigger
A signed document can outlive its model, owner or legal basis. Put the next review date and explicit reassessment triggers in the authorization record, then block material changes until the review is complete.
Human oversight is nominal
“A person checks it” is not a control unless the person has time, expertise, relevant context and authority to override the output. Test override rates, escalation behavior and what happens when the reviewer disagrees.
Retirement is forgotten
Leaving API keys, copies of prompts or retrieval indexes active after a project ends creates avoidable exposure. Make revocation, record handling and deletion verification mandatory closure tasks.
Frequently Asked Questions
Can a small IT team use this process without a dedicated AI governance office?
Yes. Start with a single accountable owner, a security/privacy reviewer and an authorizer, then add specialists when the use case involves regulated data, safety decisions or broad public impact. A concise register with evidence and explicit conditions is more useful than a large committee with no decision authority.
What should we request from an AI supplier before signing a contract?
Request the model and version policy, data-use and retention terms, subprocessors, security testing summary, incident-notification timing, vulnerability-remediation process, service-continuity commitments and procedures for deletion and access revocation. Ask how customers are notified of material model, training-data or hosting changes.
How can we test an AI system when there is no reliable ground-truth data set?
Use a documented evaluation set assembled with subject-matter experts, include adversarial and edge cases, and label uncertainty instead of inventing an accuracy figure. Combine output review with control tests, abuse scenarios and a limited pilot that has a manual fallback.
When should an approved AI use be paused immediately?
Pause or restrict it when there is a credible serious incident, uncontrolled sensitive-data disclosure, an unapproved model or integration change, a failed critical control, or evidence that outputs are causing material harm. Preserve logs and evidence first when doing so is safe, then follow the incident and authorization paths.
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