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Start with the workflow, not the model
Write down what the tool will do and where its output goes. A chatbot used to draft non-sensitive notes presents a different risk from an agent that can read customer records, send messages, or change production settings. Include the vendor and model, plugins, APIs, connectors, data stores, and any other parties that may handle your content.
- Purpose and users: What task is the tool for, and who will use it?
- Data: What information will users enter, upload, or expose through integrations?
- Outputs and decisions: Will a person review the output, or will it trigger an action or consequential decision?
- Failure impact: What would happen if the tool were wrong, unavailable, or compromised?
OWASP’s AI Exchange general controls frames assessment around describing the system and ecosystem, identifying risks, and selecting controls and assurance needs.
Check data handling and supplier dependencies
Do not assume that a consumer service, business plan, or particular setting handles data in the same way as another. Check current documentation and contract terms for the exact plan and configuration you intend to use. Ask the provider:
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- What inputs and outputs are collected, and how long are they retained?
- Are prompts, files, or outputs used for model training or service improvement?
- Which subprocessors receive data, and can administrators or support staff access it?
- How do deletion, data residency, and access controls work?
- How and when will the provider notify you about security incidents?
NIST’s Generative AI Profile recommends procurement due diligence that considers privacy, security, intellectual property, embedded AI components, and continuing third-party risk. It also recommends tracking third parties that can access organizational content and checking tools or vendors against incident and vulnerability information. Treat provider answers as claims to verify, not as a substitute for assessing your own workflow.
Map permissions, integrations, and agent autonomy
List the identities, credentials, connectors, APIs, and tools the AI can use. For each, determine whether it can read, write, send, purchase, or change systems—and whether those actions are limited to the task. Broad access turns an output problem into a potential operational or security incident.
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- Grant only the smallest permissions needed; avoid unrestricted access to sensitive data or critical systems.
- Separate sensitive environments and use scoped, revocable credentials.
- Require human review before consequential or hard-to-reverse actions.
- Decide how to disable the integration and revoke credentials if the tool behaves unexpectedly.
CISA’s May 2026 agentic AI guidance announcement highlights privilege escalation, emergent behavior, and accountability gaps. It recommends limiting autonomy, managing identity, layering defenses, maintaining oversight, threat modeling, monitoring, and regular assessment. These considerations are especially important when an agent can invoke tools or act on external systems.
Assess conventional and AI-specific threats
An AI service still depends on ordinary software and infrastructure, so assess confidentiality, integrity, and availability risks such as account compromise, insecure APIs, misconfiguration, data exposure, outages, and vulnerable supply-chain components. AI introduces additional attack paths and misuse patterns; a useful review considers both.
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- Input manipulation: Malicious or misleading inputs may influence a model or its downstream actions.
- Data poisoning and extraction: Risks can include compromised training data, model theft, or exposure of training data.
- Privacy and intellectual property: Sensitive or proprietary material may be exposed, retained, or used in ways that conflict with expectations or obligations.
- Unreliable output: Hallucinations can mislead users or cause harm if output is treated as verified fact.
- Re-identification: Data described as anonymized may still create privacy concerns.
CISA’s 2024 user-guidance announcement identifies these kinds of user-facing threats. NIST also cautions that existing frameworks do not yet comprehensively address several machine-learning attack areas; a checklist is a structured aid, not a guarantee that every risk has been found. Do not infer that a provider is secure or insecure solely from a model’s public behavior.
Request security evidence and compare tools consistently
Ask for documentation that covers the service and configuration you will actually use. Relevant evidence may include the scope and date of independent assessments, incident response processes, vulnerability handling, access controls, and subcontractor information. Check what a certificate or assessment covers: it does not automatically establish that every product, integration, region, or workflow is included.
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If you are comparing providers, use the same use case and criteria for each rather than relying on broad claims such as “enterprise-ready.” Record differences in:
- Data collection, retention, training use, and sharing.
- Provider and subprocessor transparency and security evidence.
- Permissions, integrations, and agent autonomy.
- Incident response, availability, and fallback arrangements.
- Scope and recency of verification.
- The impact of failure for the intended users and task.
NIST recommends ongoing due diligence, monitoring, and reassessment of third-party AI risk. For testable requirements, OWASP AISVS 1.0, released in June 2026, provides a versioned catalogue with 191 requirements across 12 chapters and three appendices. Its verification depth should match the risk; cite the standard version when using requirements in procurement or an assessment.
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Set adoption conditions and review them over time
Before approval, document the residual risks and the boundaries under which the service may be used. Specify permitted data, authorized users, integrations, safeguards, an accountable owner, an escalation path, and a fallback if the service is unavailable or compromised. Reassess when the provider, model, terms, integrations, access, or use case changes. NIST recommends contingency planning for third-party AI failures, incident response planning, and continuous monitoring.
Use frameworks as aids, not proof of safety
NIST’s AI Risk Management Framework (AI RMF) is voluntary and intended to help organizations incorporate trustworthiness considerations into AI design, development, use, and evaluation. NIST says it is being revised; its Generative AI Profile, NIST AI 600-1, was released on July 26, 2024. OWASP AISVS 1.0 is an implementation-level set of versioned requirements. Check the current editions when procuring or assessing a service because these resources evolve.
These frameworks can make questions and evidence more systematic, but the sources do not establish a universally sufficient score, test, or certification that proves a service is safe for every purpose. The final decision must account for your data, configuration, controls, users, and consequences of failure.
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