Assess the specific AI service and the way you plan to use it—not “AI” as one uniform risk. Before sharing data or connecting accounts, map what information flows through the service, who can access it, what actions it can take, and what happens if it is wrong, unavailable, or compromised. Then check the provider’s security evidence, set limits, and decide what would trigger a review.
Start with the workflow, not the model
An AI service carries the familiar cybersecurity risks of software and infrastructure: confidentiality, integrity, availability, account security, configuration, APIs, and supply-chain components. AI adds attack surfaces and misuse patterns of its own. NIST cautions that current frameworks do not comprehensively cover several machine-learning attack areas, so no checklist should be treated as proof that a system is safe.
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Write down the system’s intended task and boundaries before evaluating it. Include:
- Who will use it and for what business or personal purpose.
- What data users will enter, upload, or connect.
- What outputs it will produce and whether people or other systems will act on them.
- What could happen if the service gives a wrong answer, is unavailable, or is compromised.
- Which provider, models, plugins, APIs, connectors, data stores, and other parties can handle the content.
This ecosystem view follows OWASP AI Exchange’s approach: describe the system and its surroundings, identify risks, then decide what controls and assurance are needed. OWASP AI Exchange: General Controls
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Trace the data and check the supplier
Before using a service with sensitive information, establish what happens to both inputs and outputs. Ask the vendor, and verify the answers against documentation and contract terms for the exact plan and configuration you intend to use:
- What is collected, retained, or made available to administrators?
- Is content used to train or improve models or services?
- Which subprocessors receive data, and where is it processed or stored?
- How do deletion, access controls, and incident notifications work?
- What happens to your content if the service or provider relationship ends?
Do not infer that a feature, privacy setting, or vendor promise applies to every plan or configuration. NIST’s Generative AI Profile recommends procurement due diligence that includes privacy, security, intellectual-property, and embedded-AI concerns; it also calls for monitoring third-party risks and keeping an inventory of third parties with access to organizational content. NIST Generative AI Profile
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Map permissions and integrations—especially for agents
List every account, connector, API key, and tool the AI can use. Be specific about whether it can read, write, send messages, make purchases, or change production systems. A chat tool that only drafts text has a different risk profile from an agent that can execute actions across connected services.
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- WORKS WITH 1000+ ACCOUNTS: Compatible with popular accounts like Google, Microsoft, and Apple. A single YubiKey 5 NFC secures 100+ of your favorite accounts, including email, password managers, and more
- FAST & CONVENIENT LOGIN: Plug in your YubiKey 5 NFC via USB and tap it, or tap it against your phone (NFC), to authenticate. No batteries, no internet connection, and no extra fees required
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Consider conventional and AI-specific threats
Assess ordinary security failures alongside AI-related attacks. A useful threat review should include:
- Conventional risks: compromised accounts, insecure APIs, misconfiguration, service outages, exposed data, and vulnerable software or suppliers.
- Input and model risks: prompt or input manipulation, data poisoning, model stealing, and extraction of training data.
- Output and privacy risks: hallucinations that lead to bad decisions, intellectual-property exposure, or re-identification of supposedly anonymized data.
- Agent risks: misuse of tools or permissions, unintended actions, and unclear accountability when an automated action causes harm.
CISA’s 2024 announcement of partner guidance lists data poisoning, input manipulation, hallucinations, privacy and intellectual-property threats, model stealing, training-data exfiltration, and re-identification risks. NIST describes some machine-learning attacks as active research challenges. A model’s public-facing behavior alone does not establish whether its provider or deployment is secure. CISA user guidance announcement · NIST AI research: Security and Resilience
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- WORKS WITH 1000+ ACCOUNTS: Compatible with Google, Microsoft, and Apple. A single Security Key NFC secures 100 of your favorite accounts, including email, password managers, and more.
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Request evidence, then record the decision
Ask for security documentation relevant to the specific service, plan, and configuration under consideration. Useful evidence can include the scope and date of independent assessments, incident-response procedures, vulnerability handling, access controls, and subcontractor information. Check whether the evidence actually covers your workflow: a certificate or questionnaire by itself does not establish that every integration or use case is included.
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NIST recommends due diligence, checking providers or tools against incident and vulnerability databases, using approved-provider lists where appropriate, and monitoring risks over time. OWASP’s AI Security Verification Standard (AISVS) provides testable requirements that can support procurement and assessment; choose a verification level that matches the risk, and cite the standard version used. AISVS 1.0, released in June 2026, contains 191 requirements across 12 chapters and three appendices. NIST Generative AI Profile · OWASP AISVS
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NIST’s AI Risk Management Framework (AI RMF) is voluntary. NIST says the framework was developed with contributions from more than 240 organizations and released AI RMF 1.0 on January 26, 2023. Its Generative AI Profile, NIST AI 600-1, was released July 26, 2024; NIST says the framework is being revised. These are structured aids, not a universal pass/fail test. NIST AI Risk Management Framework
Set conditions for use and reassessment
Record the decision in a way users and administrators can follow. Specify permitted data, approved users, allowed integrations, safeguards, a responsible owner, incident escalation, and a fallback if the service is unavailable or compromised. Reassess when the provider, model, terms, integrations, permissions, or intended use changes. NIST recommends contingency processes for third-party AI failures, incident-response planning, and continuous monitoring. NIST Generative AI Profile
Compare tools using the same use case
If you are choosing between services, compare each against the same workflow and risk level rather than relying on broad claims about “secure AI.” Record the evidence and date checked for each item.
| Comparison area | What to check |
|---|---|
| Data handling | Collection, retention, training use, sharing, deletion, and access by administrators or subprocessors. |
| Provider and supply chain | Transparency about dependencies, relevant security evidence, and third-party access to your content. |
| Permissions and autonomy | Available integrations, actions the system can take, and limits on agent access. |
| Incidents and continuity | Incident-response arrangements, service availability, and a practical fallback. |
| Verification | What the assessment or test covered, when it was performed, and whether it matches your configuration. |
| Impact of failure | Likely consequences for the users and decisions in your intended workflow. |
Use a framework as a guide, not a guarantee
NIST AI RMF is intended for voluntary use, and its Generative AI Profile proposes actions organizations can prioritize. OWASP AISVS offers versioned requirements that can be used in procurement or technical assessment. Both can help structure questions and evidence, but neither supplies a universal score or certification that proves any AI service is safe for every use. Check the current framework versions when making a procurement decision.
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