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Approve an AI tool only after you can explain what data it receives, where that data goes, who can access it, how the system can fail, and how you will detect and contain problems. The safest approach is a documented, reversible lifecycle decision: define the use, map data, classify impact, test security and AI reliability, verify contractual promises, authorize specific uses with conditions, and monitor the service after launch. A privacy policy or a vendor claim that data is “secure” is not enough to establish whether prompts, files, outputs, telemetry, or account information are retained or used for training.
What does it mean to approve an AI tool?
Approval is a risk decision for a particular product, plan, configuration, user group, data class, and purpose—not a permanent endorsement of every future use. The same model may be acceptable for drafting public marketing copy but unsuitable for handling health records, source code, customer disputes, or confidential strategy.
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Use the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF) as a lifecycle backbone: Prepare, Categorize, Select, Implement, Assess, Authorize, and Monitor. NIST describes the framework as voluntary guidance intended to improve the incorporation of trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. Your approval record should also reflect local privacy, sector, employment, records-management, and cybersecurity obligations.
How do I scope the proposed use before looking at vendors?
Start with a one-page use case statement. It prevents a general “AI is allowed” decision from quietly expanding into high-impact processing.
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Record the intended purpose and users
- State the business problem and the decision or workflow the tool supports.
- Name user groups, administrators, contractors, and any external recipients of outputs.
- List integrations such as email, document stores, customer systems, code repositories, calendars, browsers, plug-ins, and APIs.
- Describe the model capabilities enabled: chat, retrieval, fine-tuning, autonomous actions, image or audio processing, classification, or code execution.
Define inputs, outputs, and failure modes
- Give examples of prompts, uploaded files, retrieved records, and telemetry the service will receive.
- Specify whether outputs are advice, drafts, rankings, recommendations, automated actions, or decisions affecting people.
- Identify foreseeable failures: disclosure of confidential information, fabricated content, biased recommendations, malicious instructions in retrieved data, unsafe code, service outage, or an incorrect deletion or transaction.
- Set a risk tolerance in plain language—for example, “no autonomous external communication” or “every customer-facing answer requires human review.”
The UK Department for Science, Innovation and Technology’s AI Risk Management Toolkit (2026) says organizations must understand and justify their risk-treatment strategies. A scope statement is the evidence that you have made that justification rather than relying on a generic product description.
What data should I map, and what privacy questions must the vendor answer?
Build a data-flow inventory from the user’s screen to every system that stores, transforms, or receives the information. The Office of the Australian Information Commissioner (OAIC) checklist is a useful prompt even outside Australia; its legal conclusions are written for Australian Privacy Principles, so adapt them to your jurisdiction.
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- Collection: prompts, attachments, images, audio, code, account identifiers, device information, IP addresses, usage telemetry, cookies, and inferred attributes.
- Processing: the primary model, safety filters, retrieval indexes, analytics systems, human support access, and any fine-tuning or evaluation pipeline.
- Storage: conversation history, backups, caches, logs, vector databases, abuse-monitoring records, and administrator exports.
- Sharing: parent companies, cloud hosts, subprocessors, plug-ins, model providers, support personnel, and connected enterprise systems.
- Disposition: deletion timing, backup expiry, account closure behavior, export formats, legal holds, and what remains in de-identified or aggregated form.
Ask direct privacy questions
- Can the developer or a human reviewer access prompts, files, outputs, or telemetry? Under what circumstances?
- Are customer inputs or outputs used to train, fine-tune, evaluate, or improve any model? Is the setting different by plan, workspace, API, or opt-out choice?
- What retention period applies to each data type, and can an administrator set a shorter period?
- Where is data processed and stored, including backups? Which countries may receive it through subprocessors?
- How are deletion requests verified, completed, and evidenced across replicas and backups?
- What information is inferred about users, and can those inferences be corrected or deleted?
- How are people informed about AI processing, and what notice or consent is required for the intended use?
- Does connecting a private data source make its contents available to the model provider, plug-in developer, or support team?
OAIC specifically asks whether inputs are accessible to the developer, whether cloud hosting introduces risks, whether collection and disclosure are lawful, whether data is accurate, and whether affected people are informed. Treat a public privacy policy as a starting point, not proof that your exact plan has the promised settings. Obtain the applicable data-processing terms and product-specific controls in writing.
How should I classify the impact if the AI fails?
Classify the highest credible impact across confidentiality, integrity, availability, privacy, safety, legal, financial, and reputational dimensions. NIST’s RMF calls for an impact analysis and controls commensurate with the resulting category.
| Impact area | Questions to answer | Examples of escalation |
|---|---|---|
| Confidentiality | What happens if a prompt, file, or output reaches another tenant, a reviewer, or the wrong employee? | Trade secrets, credentials, regulated records, or personally identifiable information are exposed. |
| Integrity | Could an inaccurate or manipulated output alter a record, recommendation, codebase, payment, or public statement? | A poisoned document or prompt injection changes an approval or sends false information. |
| Availability | What work stops if the service, API, account, or connected data source is unavailable? | A critical support or operational process has no fallback. |
| Privacy and fairness | Could processing, inference, or an error harm an identifiable person or group? | Incorrect personal information affects eligibility, employment, access, or reputation. |
| Safety | Could an output or autonomous action cause physical, environmental, or cybersecurity harm? | Unsafe instructions, uncontrolled code, or an action against equipment. |
| Legal and financial | Which duties, contracts, licenses, or penalties apply? | Cross-border transfer, confidentiality, copyright, recordkeeping, or sector rules are breached. |
| Reputation | How would customers, regulators, employees, or partners react if the failure became public? | Loss of trust after an undisclosed automated decision or breach. |
Use the worst reasonable outcome, not the average answer quality, to set safeguards. A low-risk drafting use can often tolerate sampling and human review; a high-impact decision may require a different system, a formal legal assessment, or a prohibition on AI use.
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What security evidence should I demand from the vendor?
Request evidence that covers the entire service, including the model’s surrounding APIs, plug-ins, identity systems, cloud infrastructure, and connected data. CISA’s 2024 joint guidance focuses on protecting, detecting, and responding to malicious activity in externally developed AI systems; it is not limited to the model weights.
Architecture and isolation
- Current architecture and data-flow diagrams showing tenants, model providers, storage, logs, backups, support access, and subprocessors.
- How tenant isolation is enforced and tested, especially for retrieval indexes, caches, fine-tuning data, and shared workspaces.
- Which components execute code, call tools, browse the web, or write to connected systems.
Identity and access
- Single sign-on, multi-factor authentication, role-based administration, service-account controls, and privileged-access review.
- Separate permissions for viewing conversations, exporting data, changing retention, managing integrations, and reading audit logs.
- Immediate deprovisioning when an employee, contractor, or application loses access.
Encryption and key management
- Encryption in transit and at rest, the algorithms or protocols used, and where keys are held.
- Whether customers can manage, rotate, or revoke keys and what happens to encrypted backups after deletion.
Logging, detection, and response
- Administrative, authentication, API, data-export, integration, model-change, and safety-event logs.
- Log retention, tamper resistance, export to your security monitoring system, and alerting for unusual prompts, downloads, or permission changes.
- Incident severity definitions, notification deadlines, investigation support, customer communication, and lessons-learned procedures.
Secure development and resilience
- Vulnerability disclosure, dependency management, patch timelines, penetration testing scope, and remediation tracking.
- Backup frequency, restoration testing, recovery objectives, regional failover, and an offline or manual fallback for critical work.
- Controls for prompt injection, malicious files, data exfiltration, model abuse, denial of service, and compromised plug-ins.
Ask for independent assurance reports or audit summaries that match the service and region you will buy. Check the report period, exclusions, subservice organizations, and whether the controls were tested rather than merely described. A badge or certificate without scope and dates is not enough to establish coverage.
How do I test AI-specific reliability and privacy risk?
Security controls cannot make an unreliable output safe by themselves. Test the intended use with representative, approved data and define where a person must verify the result.
Test accuracy and limitations
- Create a test set that reflects real languages, formats, edge cases, and accessibility needs.
- Measure the types of errors that matter to your workflow, not just a vendor’s general benchmark.
- Record confidence signals, abstention behavior, citation quality, and how the system handles missing or conflicting information.
- Check whether model or retrieval updates change results, and preserve a way to reproduce a material output.
Test adversarial behavior
- Place instruction-like text in documents and web pages to test prompt injection and indirect data exfiltration.
- Try malicious files, oversized inputs, encoded requests, role-confusion prompts, and attempts to bypass safety controls.
- Test whether a user can retrieve another tenant’s content, hidden system instructions, secrets, or restricted records.
- For tool-using systems, verify that every external action requires the intended permission and confirmation.
Design human oversight
Require a trained reviewer before an output affects a person, changes a record, publishes externally, executes code, or sends a transaction. Give reviewers enough context to check the source data, assumptions, and uncertainty; a nominal “human in the loop” who must approve hundreds of opaque recommendations in seconds is not an effective control. OAIC recommends testing intended use, understanding limitations and training data, human verification, transparency, staff training, and regular audits.
Correct personal information
Document how a person can challenge an inaccurate output or inference, who investigates it, how source data is corrected, and how future outputs are prevented from repeating the error. Reliability and transparency are privacy safeguards when an AI result affects an identifiable person.
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What should the contract and plan terms say?
Put material protections in the order form, data-processing agreement, security addendum, or other binding terms—not only in a changeable help page.
| Contract topic | Terms to confirm |
|---|---|
| Data use | Purpose limitation; whether prompts, files, outputs, and telemetry may train, fine-tune, evaluate, or improve models; and your opt-out rights. |
| Retention and deletion | Per-data-type retention, administrator controls, deletion verification, backup treatment, legal holds, and account-closure procedures. |
| Subprocessors and geography | Named or category-based subprocessors, processing and support locations, transfer mechanism, notice of changes, and objection rights. |
| Confidentiality and access | Confidentiality duties, least-privilege support access, access logging, and restrictions on human review. |
| Security and incidents | Required controls, audit evidence, vulnerability cooperation, breach notice timing, investigation assistance, and customer communication. |
| Changes | Notice and approval rights for model, feature, subprocessors, data-use, and geographic changes; treatment of major version updates. |
| Exit and portability | Export format, migration assistance, deletion certificate, transition period, and continued read-only access needed for records. |
| Liability and support | Indemnities relevant to the use, liability caps and exclusions, service levels, support response, and responsibility for connected integrations. |
Marketing language such as “enterprise-grade,” “private,” or “never used for training” should be treated as unverified until the specific product and plan terms support it. If a vendor will not answer a material question, record that uncertainty and reduce the allowed data or reject the use.
How do I decide whether sensitive information can be used?
Use a data-classification gate tied to the controls you can actually verify. Do not make “sensitive” a single category with a single answer.
Public or low-impact data
Public facts and non-confidential drafts may be permitted when accounts are controlled, outputs are reviewed, and no restricted integration is enabled. Even here, avoid uploading personal information unnecessarily and keep an audit trail for published material.
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Internal or confidential business data
Require a written no-training or restricted-use commitment for the applicable plan, defined retention, approved regions, access controls, logging, and a human review point. Start with redacted or synthetic examples and expand only after testing.
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Escalate to privacy, legal, security, records, and subject-matter owners. Approval may require customer-managed keys, dedicated tenancy, strict regional processing, contractual audit rights, special notices, or a prohibition on sending the data to an external model. If those controls are unavailable or cannot be evidenced, keep the data out of the tool.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should an approval record contain?
A named senior owner should make the authorization decision and accept residual risk. NIST defines authorization as a senior official’s risk-based decision; it is not the same as a procurement checkbox.
- Business purpose, product name, edition, configuration, users, integrations, and date assessed.
- Approved data classes and explicit prohibited examples.
- Impact classification and the reasoning behind it.
- Evidence reviewed: architecture, test results, contracts, audit reports, incident history, and vendor responses.
- Required controls, human-review points, training, and monitoring measures.
- Residual risks, exceptions, compensating controls, and the person accountable for each one.
- Review date, change-notification requirements, and suspension or rollback triggers.
Examples of suspension triggers include a confirmed cross-tenant disclosure, missed incident notification, unapproved model-training change, loss of regional processing, repeated harmful output, unexplained access-log gaps, or a connected integration acting outside its approved permissions.
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Approval is conditional and reversible. Set a cadence based on impact—more frequent for high-impact uses—and review both the vendor and your own operation.
Monitor access and data flows
- Review user, administrator, service-account, export, and integration permissions.
- Sample prompts and uploads for prohibited data, unexpected destinations, and accidental secrets.
- Verify retention, deletion, subprocessor, and geographic settings after product updates.
Monitor security and vendor change
- Track vulnerabilities, incidents, audit-report updates, model versions, feature releases, and subprocessors.
- Re-run prompt-injection, isolation, and permission tests after material changes.
- Confirm backup restoration and the manual fallback for critical workflows.
Monitor output quality and people
- Measure error, escalation, override, complaint, and correction rates by relevant user or case segment.
- Audit whether reviewers actually perform the required checks and whether staff training remains current.
- Investigate inaccurate personal information and provide a documented correction path.
NIST calls for continuous monitoring, OAIC calls for ongoing audits and reviews, and CISA emphasizes protect, detect, and respond practices. Record findings, owners, deadlines, and the decision to continue, restrict, remediate, or suspend the use.
How should I compare two AI tools?
Compare equivalent products and plans under the same use case, data class, region, and integration design. A cheaper or more capable model is not automatically the lower-risk choice.
| Comparison axis | Questions |
|---|---|
| Retention and training | Are prompts, files, outputs, and telemetry retained or used for training, and can settings be enforced centrally? |
| Geography and subprocessors | Where are processing, support, backups, and model calls performed, and how are changes notified? |
| Identity and isolation | Does it support SSO, MFA, least privilege, tenant isolation, key controls, and administrator separation? |
| Evidence and history | Are independent assurance reports, incident information, vulnerability processes, and test results available for the purchased service? |
| Change management | Can model, feature, data-use, and subprocessor changes be detected, tested, and rejected? |
| Human review and logs | Can you enforce approvals, preserve source context, export audit logs, and investigate a disputed result? |
| Integration and exit | What connected systems can the tool reach, and can data and workflows be moved elsewhere? |
| Operational fit | Can your team administer controls, train users, handle incidents, and maintain a fallback? |
| Failure severity | Which tool creates the smaller credible harm for this exact workflow, not merely the better demo output? |
Score evidence and control effectiveness separately from convenience. “Not stated” is a meaningful result: it identifies a risk that must be mitigated, limited, or accepted explicitly rather than hidden in an average score.
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- Approving the brand instead of the use: a safe low-impact pilot does not authorize sensitive data or autonomous actions.
- Relying on a privacy policy alone: policies may not specify plan-level training, retention, support access, or subprocessors.
- Ignoring the surrounding system: APIs, plug-ins, identity providers, retrieval stores, and connected applications can create the actual breach path.
- Testing only normal prompts: indirect instructions, malicious files, and permission abuse need deliberate tests.
- Calling a rubber stamp “human review”: reviewers need time, source context, authority to reject, and a way to correct errors.
- Failing to plan exit: without exports, deletion evidence, and a fallback, a service outage or contract termination becomes a security and continuity event.
- Making approval irreversible: model and vendor changes can alter risk; define reapproval and suspension triggers.
What is the practical approval sequence?
- Write the purpose, users, integrations, outputs, failure modes, and risk tolerance.
- Map every input, output, storage location, subprocessor, processing country, retention rule, and training use.
- Classify impact across confidentiality, integrity, availability, privacy, safety, legal, financial, and reputational harm.
- Request architecture, access, encryption, isolation, logging, incident, development, backup, and recovery evidence.
- Run intended-use, accuracy, limitation, prompt-injection, data-isolation, and tool-permission tests.
- Negotiate data-use, deletion, geography, subprocessor, incident, change, audit, portability, and liability terms.
- Authorize named uses and data classes with required mitigations, residual risk, review date, and suspension triggers.
- Monitor access, data flows, incidents, vulnerabilities, model changes, output quality, training, and compliance throughout operation.
This sequence turns a one-time procurement decision into accountable governance. It also gives you a defensible reason to approve a limited use, require stronger safeguards, or decline the tool when the evidence does not match the harm a failure could cause.
Frequently Asked Questions
Can a vendor still see our prompts if it says data is not used for training?
Yes, potentially. A no-training promise addresses one use of the data, not necessarily operational logging, abuse review, support access, subprocessors, backups, or legal disclosures. Check the plan-specific contract and access controls for each of those paths.
How often should an approved AI tool be reassessed?
Set the interval according to impact and change rate, then reassess immediately after a material model, feature, subprocessor, processing-location, incident, or integration change. High-impact uses need a tighter cadence than low-impact drafting.
What evidence should we keep for an AI approval audit?
Keep the scoped use case, data-flow map, impact analysis, vendor answers and contracts, test cases and results, control exceptions, authorization decision, training records, monitoring reports, incidents, and reapproval history. Preserve the product edition and assessment date so later reviewers know what was actually evaluated.
What should employees do when an AI tool asks for confidential data?
Stop and check the approved-use and data-classification rules before uploading anything. Redact or replace the information with synthetic content when possible, and report an accidental disclosure promptly through the organization’s incident channel so access, retention, and deletion can be investigated.
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