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Assess an AI tool as a system placed in a specific business process—not as a brand name or a model in isolation. Define the use case and decision, map data and access, test conventional and AI-specific threats, verify evidence for the exact configuration, apply controls, record residual risk, and assign continuous monitoring. The result should be a documented approval, restriction, pilot, or rejection with an accountable owner and clear reassessment triggers.
What an AI-tool security assessment must establish
An assessment answers five practical questions:
- What will the tool do, for whom, and with which data?
- What systems, identities, vendors, models and infrastructure sit inside its trust boundary?
- How could confidentiality, integrity or availability fail through ordinary software weaknesses or AI-specific attacks?
- Which safeguards are implemented, which are merely claimed, and what evidence covers the exact service and configuration?
- After controls are applied, is the remaining risk acceptable, who accepts it, and how will changes be detected?
Risk is use-case dependent. A text assistant that drafts public copy has a different consequence profile from an agent that can query customer records and change tickets. Proportional assessment means spending more effort where data sensitivity, system privileges, user reach or potential harm are greater, while still recording a minimum baseline for every tool.
NIST’s AI Risk Management Framework (AI RMF) is useful organizing guidance. It is voluntary, non-sector-specific, rights-preserving and use-case agnostic; it is not a certification, seal of approval or guarantee that a tool is safe. AI RMF 1.0 is being revised. Its four functions—Govern, Map, Measure and Manage—can structure an internal process without claiming formal compliance.
Step 1: Define the use case and the decision
Start with a one-page intake record before requesting a vendor demonstration or security questionnaire. Name the tool and service tier, the business owner, technical owner, intended task, user groups, deployment model and decision required. Deployment may be an internally developed system, a hosted model, a third-party software feature, or a combination of these.
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Record the intended outcome
- Task: describe the action in operational terms, such as summarizing internal policy, classifying support requests or proposing code changes.
- Users: identify employees, contractors, administrators, customers and any automated callers. State whether privileged users can access it.
- Data: list data entered directly, retrieved by connectors, generated as output and written to logs or telemetry.
- Decision: choose approval, approval with restrictions, time-boxed pilot, or rejection pending evidence.
- Impact: describe what happens if an answer is wrong, confidential information is exposed, an action is unauthorized, or the service is unavailable.
Set an assessment depth appropriate to that impact. A pilot that cannot reach production systems can use narrower tests than an agent with write access, but the difference must be explicit rather than assumed.
Set boundaries and assumptions
State what the tool is not allowed to do. Examples include processing regulated records, making an employment or credit decision, sending external messages, executing code, or changing production configuration. These exclusions become testable controls and make later reassessment easier when someone requests a new integration.
Step 2: Map the AI system and its trust boundaries
Draw a data-flow diagram and maintain an inventory that covers the service, not only the model. For each component, record its controller, location or region when relevant, purpose, available security settings and evidence source.
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- Inputs and outputs: prompts, uploaded files, retrieved documents, generated text or code, tool-call arguments and final actions.
- Storage and retention: conversation history, uploaded objects, embeddings, caches, backups, abuse-monitoring records and administrator exports.
- Models and dependencies: foundation model, fine-tunes, classifiers, vector stores, orchestration code, open-source packages, cloud services and hardware.
- Extensions: plugins, agents, browser access, retrieval connectors, webhooks, APIs, scheduled jobs and human-approval steps.
- Identity: workforce identity provider, service accounts, delegated tokens, administrator roles, tenant boundaries and session controls.
- Connected resources: file shares, repositories, ticketing systems, databases, messaging platforms, operational technology or payment systems.
- People and context: affected individuals, downstream decision makers, support staff, incident responders and the physical or operational environment.
Mark every arrow as read, write or execute access. A connector that only retrieves public documentation is materially different from one that can update a customer record. Capture whether access is user-scoped, application-wide or shared across tenants, and whether the provider or your organization controls each boundary.
Describe the lifecycle
Identify the stages at which trustworthiness is considered: pre-design, design and development, deployment, use, and test and evaluation. For an acquired service, the same lifecycle logic means revisiting the assessment when configuration, integrations, data use, model or service version, permissions, operating context or vendor terms change.
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Step 3: Assess conventional security properties
NIST emphasizes that AI security includes ordinary information-security fundamentals. Evaluate confidentiality, integrity and availability for the AI service, its data, underlying software and hardware, and every connected system.
Confidentiality
- Can prompts, files, retrieval results, outputs or logs be viewed by another tenant, provider employee, support contractor or unauthorized administrator?
- Are data in transit and at rest protected, and who controls encryption keys?
- Can users disable history, training use, retention or diagnostic collection, and are those settings enforced centrally?
- Do connectors enforce the source system’s permissions for each user, or does the AI service receive a broad shared credential?
- Can model responses reveal sensitive content through direct answers, summaries, autocomplete, error messages or retrieval citations?
Integrity
- Can an attacker alter prompts, retrieved documents, system instructions, model files, packages, policies, tool definitions or audit records?
- Are model weights, training data and evaluation sets protected from unauthorized modification? NIST specifically calls for considering their integrity and, where applicable, confidentiality.
- Are outputs authenticated and traceable to a model version, configuration, user and source data?
- Can a compromised integration submit commands or change records without an independent authorization check?
Availability and resilience
- What happens during provider outage, quota exhaustion, rate limiting, dependency failure or a revoked credential?
- Are there safe fallbacks, manual procedures, export capabilities and recovery time expectations?
- Can an attacker consume capacity through automated prompts, oversized files or repeated tool calls?
- Are backups, disaster recovery and regional dependencies documented and tested for the actual service tier?
Baseline control review
Review authentication, authorization, tenant isolation, secure configuration, vulnerability and patch handling, secrets management, network isolation, endpoint security, logging, incident response and change control. Tie each control to the architecture you mapped; a generic security statement is not evidence that a particular connector or agent is protected.
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Step 4: Examine AI-specific attack surfaces
Generative AI introduces failure modes that conventional software reviews may miss. NIST’s Generative AI Profile (AI 600-1), released July 26, 2024, identifies information-security risks including AI systems that enable cyber capabilities and the new attack surface created by systems themselves.
Prompt injection
Prompt injection occurs when user text, retrieved content or an external page attempts to override instructions or redirect the model. Test both direct attacks typed by a user and indirect attacks hidden in documents, web pages, tickets or code comments. Determine whether the model can be induced to disclose system prompts, reveal retrieved data, ignore approval rules or call a tool with attacker-selected arguments.
Controls should separate untrusted content from trusted instructions, restrict tool permissions, validate arguments outside the model, require confirmation for consequential actions and prevent secrets from being placed in prompts. Treat model output as untrusted input to downstream systems.
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Data poisoning
Poisoning can affect training data, fine-tuning sets, retrieval indexes, feedback data or other sources that influence behavior. Ask who can add or edit those sources, how changes are reviewed, whether provenance is retained and how a bad item can be removed. Test whether a deliberately misleading document changes retrieval or recommendations, and whether the system can identify and quarantine the source.
Leakage through prompts and outputs
Leakage may occur when users paste secrets, when retrieval returns records beyond a user’s authorization, or when outputs reproduce memorized or confidential content. Establish data-minimization rules, block sensitive patterns where practical, enforce source permissions, review logs for exposure and define deletion and notification procedures. Do not assume that a provider’s statement about model training covers retention, support access, abuse monitoring or connected repositories.
AI-enabled misuse
AI can accelerate phishing, exploit development, reconnaissance, social engineering or harmful automation. Assess how the proposed tool could be repurposed, who can access high-risk capabilities, whether rate limits and abuse detection exist, and whether an authorized security team can investigate suspicious use. The source material identifies this risk category; likelihood and impact must be established from your tool, users and controls rather than borrowed from a generic claim.
Unsafe autonomous action
Risk rises sharply when the system can send messages, execute code, modify records, purchase services or change infrastructure. Use least privilege, short-lived credentials, allowlisted destinations, transaction limits, sandboxing and human approval for high-impact actions. Keep read and write permissions separate and log the request, model output, tool call, approver and resulting state.
Step 5: Request and validate evidence
Ask the provider and internal engineering team for evidence that matches the proposed use, service tier, region, integrations and date. Evidence should let you test a control, not merely repeat a marketing assertion.
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Evidence package
- Architecture and data-flow diagrams showing model providers, subprocessors, storage, regions and trust boundaries.
- Data-use, retention, deletion, training-use and support-access terms for the exact plan.
- Identity, role, tenant-isolation, API-key, secret-management and connector documentation.
- Secure-development, vulnerability-disclosure, patching, dependency and incident-response processes.
- Independent assessment reports, penetration-test scope and remediation status where available.
- Model, dataset and evaluation documentation, including known limitations, versioning and change notices.
- Logging, export, alerting, abuse-monitoring and investigation capabilities.
- Business-continuity, outage, backup, recovery and exit procedures.
Check scope and freshness. A report for a consumer edition does not establish controls for an enterprise tenant; a test of the public API does not cover an agent with write access; and a document from an earlier model version may not describe the current behavior. Record unanswered questions and compensating controls instead of converting missing evidence into a pass.
Step 6: Select, test and document controls
Translate each material risk into a required control, an owner, a test and an acceptance criterion. The AI RMF Playbook provides voluntary suggested actions and references for the Govern, Map, Measure and Manage functions; use it to organize work, not to claim certification.
Data controls
- Permit only the minimum data categories needed for the task.
- Classify prompts, uploads, outputs and logs; block or tokenize secrets and regulated identifiers where feasible.
- Set retention and deletion rules, verify them with a deletion test and document exceptions such as backups or security logs.
- Keep retrieval indexes synchronized with source-system permissions and removal events.
Access and action controls
- Use workforce single sign-on, multifactor authentication and role-based administration.
- Issue separate, least-privilege credentials for each connector and environment.
- Place high-impact actions behind deterministic policy checks and human approval.
- Constrain destinations, commands, file paths, transaction values and execution time.
- Isolate experiments from production and revoke access automatically when a pilot ends.
Quality, safety and oversight controls
- Define when a human must review an output and what evidence the reviewer must check.
- Test representative benign, adversarial, malformed and prompt-injection scenarios.
- Measure error, refusal, leakage and unsafe-action outcomes against thresholds appropriate to the use case.
- Preserve enough context to reproduce an incident without retaining unnecessary sensitive content.
Operational controls
- Centralize logs for authentication, prompts or prompt metadata, retrieval, tool calls, approvals, configuration changes and administrative access, subject to privacy rules.
- Alert on unusual volume, privilege escalation, repeated injection attempts, sensitive-data matches and unauthorized destinations.
- Maintain an incident playbook covering account disablement, token revocation, connector isolation, evidence preservation, user notification and recovery.
- Define a rollback or manual fallback for model, prompt, index and integration changes.
Step 7: Test the real workflow
Do not stop at a document review. Use a controlled environment and representative data or safe synthetic substitutes.
- Verify that each user sees only the records permitted by the source system.
- Submit direct and indirect prompt-injection cases, including malicious instructions embedded in retrieved content.
- Attempt to induce disclosure of system instructions, secrets, another user’s data and restricted documents.
- Try malformed files, oversized inputs, repeated requests and dependency outages.
- Attempt unauthorized tool calls, argument changes, destination changes and replay of old approvals.
- Confirm that logs identify the user, model or service version, source, action, approver and outcome.
- Exercise incident response, credential revocation, connector isolation and manual fallback.
Record the scenario, expected result, observed result, severity, evidence and remediation owner. A failed test is not automatically a rejection; it is a decision input that may require a narrower use case, stronger control or explicit risk acceptance.
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Produce a decision record that a procurement team, system owner and incident responder can all use. Include:
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- approved purpose, users, data classes, integrations and environments;
- implemented controls and test results;
- known limitations, open evidence requests and compensating measures;
- residual risks, their business consequences and the person authorized to accept them;
- restrictions such as read-only access, prohibited data, pilot duration or mandatory human review;
- monitoring metrics, alert recipients and incident escalation paths;
- expiry date or review date and events that trigger an earlier reassessment.
Do not describe “AI RMF aligned” as a certification or guarantee. The framework is voluntary guidance. Your decision should say what you actually tested and what remains uncertain.
How to compare multiple AI tools fairly
Use one use case, one data profile and the same evidence requests for every candidate. A score is useful only when the underlying observations are comparable; avoid awarding points for an unsupported claim.
| Comparison axis | Questions to answer | Decision evidence |
|---|---|---|
| Data control | What data can enter, where is it retained, how is deletion enforced, and does the organization control those settings? | Current terms, configuration export and a deletion or retention test |
| Identity and permissions | How are users authenticated, and can access to files, applications and APIs be limited per user and task? | Role matrix, connector design and authorization test |
| Security of components | How are confidentiality, integrity, availability and resilience protected for the service, model components and dependencies? | Architecture, independent assessment scope and recovery evidence |
| AI attack exposure | What happens under direct or indirect prompt injection, poisoning, output leakage or unsafe tool requests? | Scenario results, mitigations and residual-risk record |
| Monitoring and response | Can the organization detect misuse, investigate a failure and revoke access quickly? | Log samples, alert rules, export capability and exercised playbook |
| Evidence quality | Which claims are documented or tested, for which configuration, and how current are they? | Document owner, coverage, date and identified gaps |
| Operational fit | Can the team enforce restrictions, human review and change control as the service evolves? | Administration workflow, staffing owner and reassessment triggers |
Monitoring after approval
Approval is a point in the lifecycle, not the end of assessment. Assign a service owner to review telemetry, provider notices, incidents, user reports and control tests at a defined cadence.
Reassessment triggers
- New model, service tier, region, retention term or provider subprocessors.
- New data category, user population, connector, plugin, agent capability or permission.
- Change from read-only assistance to automated or write-capable action.
- Prompt, policy, retrieval-index, fine-tuning, dependency or infrastructure change.
- Security incident, suspected leakage, poisoning event, repeated injection or material evaluation failure.
- Regulatory, contractual, business-process or threat-environment change.
Metrics that support decisions
Track measures tied to the approved use: unauthorized-access attempts, sensitive-data detections, blocked tool calls, injection findings, human-override rates, incident response time, unresolved vulnerabilities, availability and the age of the last evidence review. Define thresholds and owners before deployment so monitoring produces action rather than a passive dashboard.
How NIST guidance fits into the assessment
NIST’s AI RMF 1.0 supplies the voluntary Govern, Map, Measure and Manage structure. The Generative AI Profile, NIST AI 600-1, was released on July 26, 2024 and discusses information-security risks such as prompt injection, data poisoning and AI-enabled cyber capabilities. NIST’s security guidance also emphasizes conventional confidentiality, integrity and availability for systems and data, including the integrity and, where applicable, confidentiality of code, training data and model weights.
NIST finalized its adversarial machine learning taxonomy, AI 100-2e2025, in March 2025. NIST IR 8596, the Cybersecurity Framework Profile for Artificial Intelligence, was published as an initial preliminary draft on December 16, 2025. It groups focus areas into securing AI system components, conducting AI-enabled cyber defense and thwarting AI-enabled cyber attacks. Because it is a preliminary draft, treat it as emerging guidance rather than a finalized standard.
A NIST CSF 2.0 Quick-Start Guide on using AI for CSF analysis and reporting was shown as seeking comments through October 15, 2026 on the page accessed October 10, 2026. Its status may have changed, so verify the current page before relying on it. NIST also states that the AI RMF Playbook will be updated after the framework revision. Keep document versions and status labels in your governance records.
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A compact assessment worksheet
- Purpose and owner: task, users, owner, deployment model and decision.
- Boundary: inputs, outputs, storage, dependencies, identities, connectors and actions.
- Impact: confidentiality, integrity, availability, safety, legal and operational consequences.
- Threats: ordinary compromise plus injection, poisoning, leakage and misuse.
- Evidence: architecture, terms, settings, tests, incident process and recovery proof.
- Controls: minimization, least privilege, isolation, approval, logging, alerting and fallback.
- Testing: representative and adversarial scenarios with expected and observed results.
- Decision: approval, restriction, pilot or rejection; residual-risk owner and conditions.
- Monitoring: metrics, review cadence, incident path and reassessment triggers.
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