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Choose the tool only after matching its controls and contract to the data you will process. Start by classifying the information, identifying whose data it is, and deciding what could happen if it were disclosed, retained, altered, or used in an automated decision. Then compare the product’s training terms, retention and deletion, data location, encryption, identity controls, audit features, connectors, human-review requirements, and regulatory commitments. A consumer chatbot and an enterprise workspace are not interchangeable: the Office of the Australian Information Commissioner (OAIC) advises organisations not to enter personal or particularly sensitive information into publicly available generative-AI tools because of significant privacy risks.
What makes an AI tool suitable for sensitive business data?
Suitability is a risk decision, not a popularity ranking. A tool is acceptable when its technical safeguards, contractual promises, configuration, and operating procedures address the specific data and use case. “No training on customer data” is important, but it does not answer who can see prompts, how long logs remain, where processing occurs, which connectors are enabled, or what happens after an incident.
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Assess the complete path: prompt text, uploaded files, retrieved documents, embeddings, model context, generated output, abuse-monitoring records, analytics, backups, fine-tuning data, and every connected system. The Information Commissioner’s Office (ICO) recommends documenting these movements, deleting unnecessary intermediate files, and considering de-identification, synthetic data, federated learning, or differential privacy where they fit the use case.
Use a harm-based threshold
- Disclosure harm: Would exposure reveal personal data, regulated records, customer secrets, credentials, source code, or an unannounced transaction?
- Integrity harm: Could an incorrect or manipulated answer change a payment, safety decision, legal position, or system of record?
- Availability harm: Would losing access to the service interrupt operations or prevent a required compliance process?
- Accountability harm: Can you identify the user, model, data sources, prompt, output, approval, and retention status after the fact?
The higher the potential harm, the less acceptable an unmanaged public endpoint becomes and the more you need contractual commitments, technical isolation, and documented human approval.
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How should I classify data before testing an AI tool?
Create an inventory before anyone pastes a real prompt. Record the data owner, purpose, legal basis, sensitivity, retention period, permitted locations, and people or systems that may access it. A simple classification helps determine which experiments are safe.
| Class | Typical examples | Default handling |
|---|---|---|
| Public | Published product documentation, public prices, press releases | Low-risk testing is usually possible, subject to copyright and accuracy checks. |
| Internal | Non-public procedures, internal project notes, draft schedules | Use an approved workspace with access controls and logging; do not assume a public chatbot is suitable. |
| Confidential | Customer contracts, unreleased plans, source code, supplier terms | Require a contract, least-privilege access, defined retention, and a documented purpose. |
| Personal or regulated | Employee records, health information, financial identifiers, precise location data | Apply privacy-law, sector, and transfer requirements; minimize or de-identify before processing when possible. |
| Trade secret | Proprietary formulas, designs, algorithms, acquisition plans | Use a segregated environment with strict connector controls, contractual confidentiality, and approval from the owner. |
Also classify the output. A harmless summary may become sensitive if it reproduces names, account numbers, or confidential strategy. If the model will make or recommend a consequential decision, treat the use case as high risk even when the input appears ordinary.
What is the difference between a consumer chatbot, an enterprise workspace, and an API?
They can expose the same underlying model while providing very different governance. Choose the deployment pattern that gives administrators control over identity, data flow, retention, and evidence.
| Deployment | Strengths | Risks and questions |
|---|---|---|
| Public consumer chatbot | Fast to try; little integration work | Organisation-wide controls, contractual terms, residency, administrator visibility, and deletion may be limited or unclear. Do not enter personal or particularly sensitive information without an approved basis. |
| Enterprise workspace | Single sign-on, policy administration, tenant separation, audit, document permissions, and managed sharing | Controls are plan-, region-, connector-, and feature-specific. Verify what is logged, who can search it, and whether optional agents or plugins change the boundary. |
| API integration | Application-level routing, endpoint selection, regional processing, custom filters, and automated controls | Your application still creates logs, traces, caches, embeddings, and backups. Check endpoint retention, abuse monitoring, eligibility for zero-data-retention, and every third-party component. |
| Self-hosted or dedicated environment | Maximum control over network, keys, storage, and model access | You assume responsibility for patching, model security, monitoring, incident response, and training-data provenance; operational cost and expertise are higher. |
Do not infer safety from a vendor’s headquarters or sales region. An enterprise account can still send data to a different processing region, and an API can still retain abuse-monitoring records.
Does the AI vendor train on our prompts and files?
Ask the question separately for every product, workspace, API endpoint, connected agent, and optional improvement program. Obtain the answer in the service agreement or data-processing addendum rather than relying on a sales presentation.
- Is customer content used to train or improve foundation models?
- Is the setting an opt-in, an opt-out, or a contractual prohibition?
- Are human reviewers or contractors allowed to inspect content for abuse investigations?
- Do prompts, retrieved documents, embeddings, outputs, and feedback receive the same treatment?
- Do connectors, fine-tuning jobs, evaluation tools, or agent traces have separate terms?
OpenAI states that business and API inputs and outputs are not used to train models by default. Its business documentation also describes encryption in transit and at rest, configurable retention for qualifying organisations, and regional storage or inference options for eligible customers. Those statements do not eliminate the need to check the exact endpoint, region, feature, and contract you will use.
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Microsoft says organisational Copilot prompts, responses, and Microsoft Graph data are not used to train foundation models. It describes inherited identity permissions, sensitivity labels, retention policies, audit, encryption, and tenant isolation under enterprise data protection. In a separate work-or-school Copilot Chat description, Microsoft says prompts, Bing queries, and responses are logged and may be available to an IT administrator. “Not used for training” therefore does not mean “not stored or visible.”
How long are prompts, files, outputs, and logs retained?
Request a retention schedule that names each data type: prompts, uploaded files, generated answers, abuse logs, traces, embeddings, analytics, backups, support tickets, and deleted-account remnants. Ask who can delete each item, how quickly deletion propagates, and which legal, security, or backup exceptions apply.
OpenAI’s API documentation says abuse-monitoring logs may be retained for up to 30 days by default, with endpoint-specific controls and exceptions. Zero-data-retention and feature-level controls have eligibility and endpoint limitations, so confirm that your exact workflow qualifies rather than treating the label as universal.
Questions to put in the retention schedule
- What is the default period and what shorter period can the customer configure?
- Are deleted records removed from active systems, replicas, backups, and search indexes?
- Can administrators export records for legal hold or e-discovery without exposing unrelated tenants?
- Are support staff, abuse investigators, or subprocessors able to access retained content?
- What happens when a trial ends, a user leaves, or the organisation terminates the contract?
Where will the data be stored and processed?
Separate storage location, inference location, support access, and subprocessor location. A regional data-centre option may not cover model inference, telemetry, customer support, backups, or a connector that copies documents elsewhere. Ask for a current subprocessor list, transfer mechanism, applicable jurisdictions, and a process for notification when those change.
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What encryption and key controls should procurement require?
Require encryption in transit and at rest, then ask what it actually protects. Confirm the algorithms or standards in the security documentation, how keys are generated and rotated, who can administer them, and whether customer-managed keys or external key management (EKM) are available for your plan and region.
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- Can you revoke a key and immediately stop access to stored content?
- Are backups, caches, vector indexes, and logs encrypted under the same policy?
- Is key use recorded in an audit trail with administrator identity and timestamp?
- What recovery process applies if a key is lost, disabled, or rotated incorrectly?
- Can different tenants, environments, or business units use separate keys?
Encryption does not replace authorization. A properly encrypted database still exposes information if a connector, service account, or administrator can retrieve more than necessary.
How do identity, permissions, and connectors prevent oversharing?
Use single sign-on, multifactor authentication, role-based administration, and automatic deprovisioning. Apply least privilege to documents, data sources, tools, plugins, and agents. The assistant should retrieve only what the requesting user is already allowed to see, and the system should preserve sensitivity labels and access-control decisions during retrieval.
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Connector and agent controls
- Inventory every connector and the fields it can read or write.
- Start with read-only access and a narrow pilot scope.
- Block personal drives, unmanaged mailboxes, and external sharing unless approved.
- Require separate approval for agents that can send messages, change records, execute code, or call financial systems.
- Test revocation: remove a user’s document permission and verify that retrieval stops promptly.
Microsoft’s description of Copilot enterprise data protection illustrates the desired model: identity permissions, sensitivity labels, retention policies, auditing, encryption, and tenant isolation are applied together. Your own configuration and license still determine which controls are active.
What audit and incident-response features are essential?
Administrators should be able to see who used which model or endpoint, when, with what data source, and what action followed. Useful evidence includes prompt and output records where lawful, connector access, policy decisions, blocked requests, administrator changes, key use, exports, and deletion events.
Confirm that logs can feed your security information and event-management or data-loss-prevention systems, support legal hold and e-discovery, and be restricted to authorised investigators. Ask how quickly the vendor will notify you of a breach, subprocessors change, model change, or loss of isolation. Your runbook should let you suspend a user, connector, endpoint, agent, token, or key independently rather than shutting down the entire business.
How should we handle inaccurate, biased, or leaked outputs?
Generative models can be confidently wrong, reproduce sensitive text, follow adversarial instructions, or act on poisoned documents. Require human review before consequential decisions, external publication, legal advice, financial actions, safety instructions, or changes to a system of record.
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Controls for high-impact workflows
- Show citations or source passages where possible, and require the reviewer to open them.
- Keep the original prompt, retrieved sources, model version, output, reviewer, and final decision.
- Use deterministic business rules for eligibility, payment limits, and safety thresholds instead of asking the model to invent them.
- Test for prompt injection, data exfiltration, discriminatory recommendations, and unsafe tool calls.
- Provide an appeal or correction path for people affected by an automated recommendation.
AWS identifies hallucinations, data poisoning, adversarial prompts, model inversion, and agentic-AI risks as security concerns. The Canadian Centre for Cyber Security recommends security by design, vendor assessment, validation of training-data provenance, robust collection and transfer security, and avoiding personally identifiable or sensitive corporate data in prompts.
What questions should procurement ask an AI vendor?
Send a written questionnaire and require evidence for each answer. The following checklist covers the controls most often missed in demonstrations.
- Data use: Identify every training, evaluation, abuse-review, and human-access purpose and obtain a contractual prohibition where required.
- Retention: Map default and configurable periods for content, logs, traces, embeddings, backups, and support records.
- Location: List storage, inference, support, subprocessor, and transfer jurisdictions for the selected plan.
- Security: Document encryption, key ownership, customer-managed keys, rotation, recovery, isolation, and vulnerability management.
- Identity: Confirm SSO, MFA, role-based administration, automatic offboarding, document-level permissions, and service-account limits.
- Connectors: Record scopes, write actions, agent autonomy, plugin review, and emergency-disable procedures.
- Audit: Verify administrator visibility, export, e-discovery, DLP integration, immutable timestamps, and retention of audit records.
- Accuracy: Request evaluation methods, known limitations, model-change notices, source attribution, and human-approval features.
- Compliance: Obtain the data-processing agreement, security reports, subprocessor terms, transfer safeguards, and sector-specific commitments relevant to your use.
- Response: Set breach-notification deadlines, cooperation duties, deletion certification, exit assistance, and a named escalation path.
How do I compare shortlisted tools objectively?
Score each candidate against the same use case and data class. Weight the controls that reduce your greatest harm; do not let a slightly better model score compensate for an unacceptable retention or residency gap.
| Category | Evidence to record | Decision prompt |
|---|---|---|
| Data use and training | Contract clause, product and endpoint terms | Is secondary use prohibited or controllable for this workflow? |
| Retention and deletion | Schedule, deletion test, backup exception | Can we meet our legal and customer deadlines? |
| Location and sovereignty | Storage/inference map, subprocessors, transfer mechanism | Are all jurisdictions acceptable? |
| Identity and isolation | SSO, MFA, RBAC, tenant and document tests | Can the assistant retrieve only authorised data? |
| Encryption and keys | Architecture, key options, rotation and recovery evidence | Can we control and investigate key access? |
| Audit and response | Sample logs, export, alert, suspension and notification procedures | Could we reconstruct and contain an incident? |
| Accuracy and oversight | Evaluation results, citations, approval workflow | Can a qualified person review every consequential result? |
| Integration and cost | Engineering effort, usage limits, support tier, exit plan | Does total operational cost fit the risk-reduction benefit? |
Weighting depends on context. A Microsoft-centred organisation may value inherited Microsoft 365 permissions and sensitivity labels. An API-led team may prioritise endpoint-level retention controls, regional processing, and eligibility for zero-data-retention. A regulated organisation may rank contractual terms, residency, audit evidence, and a documented risk assessment above raw model capability.
What is a safe implementation process?
Move from a low-risk pilot to production in controlled stages. Keep real customer or employee records out of the pilot until the contract, configuration, and review process are approved.
- Define the use case. Name the users, data classes, outputs, systems affected, decision owner, and harm scenarios.
- Minimize the input. Remove fields the model does not need; mask identifiers; use synthetic or de-identified examples when they preserve the task.
- Map the data flow. Include prompts, uploads, retrieval, embeddings, context windows, outputs, logs, analytics, backups, and downstream tools.
- Complete privacy and security assessments. Document lawful basis, impact assessment, threat model, transfer analysis, and trade-secret controls.
- Negotiate terms. Set training prohibition, retention, deletion, residency, subprocessors, audit rights, incident notice, confidentiality, and exit duties.
- Configure boundaries. Enforce SSO and MFA, least privilege, labels, DLP, blocked connectors, regional settings, key policies, and human approval.
- Test abuse and failure. Try prompt injection, oversharing, revoked permissions, malicious documents, incorrect answers, service outage, and deletion requests.
- Launch with monitoring. Review logs, blocked events, data leakage signals, quality metrics, user feedback, and policy exceptions.
- Reassess on change. Repeat the review when the model, endpoint, connector, region, contract, data class, or applicable law changes.
OAIC guidance, updated 17 January 2025, calls due diligence a continuing responsibility rather than a “set and forget” exercise. Treat model and vendor changes like other production changes: record the owner, test the impact, and retain approval evidence.
What are the most common selection mistakes?
- Choosing by model quality alone: A fluent answer does not compensate for unacceptable data handling.
- Assuming enterprise means private: Verify plan, endpoint, region, connectors, logs, and administrator visibility.
- Relying on an opt-out toggle: A setting can be reset, bypassed by an API or agent, or excluded from another feature; contractual language and technical enforcement are stronger.
- Ignoring intermediate data: Embeddings, traces, caches, exports, and backups can outlive the visible chat.
- Granting broad retrieval rights: An assistant with a user’s excessive permissions can surface information that should have been restricted earlier.
- Skipping human accountability: Automation without a named reviewer makes errors difficult to detect and explain.
- Failing to plan exit: Require export, deletion certification, credential revocation, and connector removal before signing.
How should the decision be documented?
Keep a short decision record that links the use case to the approved tool, data classes, regions, retention settings, contract version, controls, test results, residual risks, owner, reviewer, and next review date. Include rejected alternatives and the reason for rejection, such as unsupported residency or inability to delete logs.
That record turns a procurement choice into an auditable control. It also gives security, privacy, legal, and business teams a common trigger for reassessment when the model, connector, data, or law changes.
Frequently Asked Questions
Can I paste a redacted customer record into a public chatbot for a quick answer?
Redaction lowers exposure but may not remove re-identification risk, contractual restrictions, or confidential context. Use synthetic data or an approved enterprise environment unless your privacy and security owners have documented that the remaining information is genuinely non-sensitive.
Do I need a data-processing agreement for an AI API?
Usually, if the provider processes personal data on your behalf, your privacy team should determine whether a data-processing agreement and international-transfer terms are required. The agreement should cover subprocessors, security, deletion, assistance with data-subject requests, and breach notification, not just model training.
What should we do if an assistant reveals a document a user should not see?
Stop the affected connector or agent, preserve logs and the retrieved document, and suspend the relevant account or token if necessary. Then correct the source permission, test revocation and re-indexing, assess who received the content, and follow your incident-notification procedure.
How often should an approved AI tool be reviewed?
Set a date based on risk, with at least an annual review for established deployments and an immediate review after a material model, endpoint, connector, region, contract, data-classification, or legal change. Continuous monitoring should handle access and leakage events between formal reviews.
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