Before approving an AI cloud provider for sensitive information, prove what each feature receives, stores, exposes and deletes; verify the provider’s terms for the exact service, model, region and plan; and assign every security and governance control to an owner. Cloud migration does not transfer responsibility for your data, identities or configuration to the provider.
Start with a written approval boundary
Do not begin with a vendor’s marketing statement such as “your prompts are not used to train the model.” That answers only one possible use of the data. Start by documenting the proposed application and the limits of an approval decision.
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- Users and purpose: identify the teams, roles and business process involved.
- Data classes: list personal data, health or financial information, confidential business records, credentials, source code and regulated data separately.
- Jurisdictions: record where users, data subjects, systems and administrators are located, including cross-border transfer requirements.
- Outputs and connected systems: specify what the model may generate and whether it can call databases, email, ticketing, code repositories or other tools.
- Deployment model: classify the service as SaaS, PaaS or IaaS. The responsibility boundary changes with the model and with the application you build around it.
- Features: mark whether the design uses file uploads, retrieval-augmented generation, agents, tools, fine-tuning, evaluation jobs or persistent conversation state.
This boundary becomes the scope of your contract review, technical tests and approval. A decision for one model, region and feature set must not silently expand to a different service or deployment.
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Create a diagram and an inventory for data entering, leaving or being retained by the AI application. Ask both the provider and your internal application owner to confirm each item.
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| Data element | Questions to answer |
|---|---|
| Prompts and generated content | Are requests and responses logged, reviewed by people, used for abuse detection, training or service improvement? What deletion control applies? |
| Attachments and multimodal inputs | Where are uploaded files, images and audio stored? Are they copied into a separate workspace, malware-scanned or retained after a conversation ends? |
| Retrieval sources and augmented context | Which documents are indexed, where are indexes held, and can one user retrieve another user’s content? How are deleted documents removed from indexes and caches? |
| Embeddings and vector stores | Are embeddings treated as customer data, and who can query, export or delete them? |
| Fine-tuning and evaluation data | What training, validation and test files are accepted; who can access checkpoints; and how are datasets and resulting models deleted? |
| Stateful history | Does the feature retain message history, agent state, files or tool results between sessions? Is persistence optional, and what is the configured lifetime? |
| Telemetry and security logs | Do logs contain prompts, identifiers, file names, IP addresses or outputs? Which subprocessors and support personnel can access them? |
| Outputs and downstream actions | Where are responses written, and can generated text or code enter a production system without human review? |
Azure documentation illustrates why this inventory matters: it distinguishes prompts and generated content, uploaded data, stateful entities, augmented data and training or validation data. Optional features can persist message history and other content according to their configuration. Treat that documentation as a service-specific example, not a rule for every AI provider.
Read the exact terms for the service you will use
Collect the contract, data-processing addendum, privacy documentation, service terms, subprocessor list and security documentation that apply to the selected product. Confirm the model family, service tier, deployment region and feature flags named in those documents.
Data use and human access
Look for separate rules covering model training, product improvement, abuse monitoring, quality review and legal disclosures. “Not used for training” does not necessarily mean “never retained” or “never seen by a human.” Ask when provider personnel may inspect content, whether access is approved and logged, and whether you can disable optional review.
Retention and deletion
Obtain a retention period for prompts, outputs, uploads, histories, embeddings, fine-tuning files, backups and security logs. Determine whether deletion is immediate, queued or subject to a backup cycle. Ask how you receive confirmation and whether an administrator can export data before deletion.
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Geography and subprocessors
Verify processing and storage locations independently; a region selector may not cover support access, abuse analysis, failover or subprocessors. Record cross-border transfer mechanisms and the notice period for subprocessor changes.
Scoped provider claims
Microsoft states that documented Azure-hosted Models sold by Azure do not use customer data to train foundation models or improve services without permission or instruction. That statement is limited to the documented service category and its terms. It should not be generalized to consumer products, direct provider APIs, previews, other cloud providers or every optional feature.
Assign the shared-responsibility controls
Microsoft’s shared-responsibility guidance summarizes the baseline plainly: “For all cloud deployment types, you own your data and identities.” The statement describes Microsoft’s cloud model; it is not a legal conclusion or a universal provider guarantee. Build a responsibility matrix for the actual architecture.
| Control area | Customer duties to assign | Evidence to retain |
|---|---|---|
| Data governance | Classify data, minimize prompts, approve uses and prohibit unapproved sensitive inputs. | Data inventory, policy, approved-use register and exception records. |
| Identity and access | Provision and remove accounts, enforce strong authentication, separate administrators and apply least privilege. | Role assignments, access reviews and account-lifecycle logs. |
| Application and prompts | Validate inputs, redact secrets, constrain system instructions and require human review for consequential actions. | Code/configuration review, test cases and change approvals. |
| Network and endpoints | Restrict ingress and egress, isolate development from production and prevent unapproved data exfiltration. | Network diagrams, firewall rules and private-endpoint configuration. |
| Keys and encryption | Choose provider-managed or customer-managed keys, protect key administrators and rotate keys under policy. | Key-policy records, rotation evidence and encryption settings. |
| Logging and response | Collect usable audit trails, review anomalies and operate an incident process for leakage or misuse. | Central logs, alert tests, playbooks and incident exercises. |
| Provider access | Define when support access is allowed, who approves it and how sessions are recorded. | Approval tickets, session logs and periodic reviews. |
Service and model layers vary by SaaS, PaaS and IaaS. The provider may operate the infrastructure while you remain responsible for tenant configuration, application permissions, retrieval indexes and the data your users submit.
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Verify security settings in the real environment
Do not treat a feature’s existence in documentation as proof that it protects your workload. In a test tenant, capture the default state, enable required controls and confirm their limits in the chosen region and service tier.
Network isolation
Use private endpoints or equivalent private connectivity where supported. Test that public access is disabled or restricted, DNS resolves through the intended private path and outbound calls cannot reach unapproved destinations.
Identity and least privilege
Prefer managed identities over embedded keys. Give the application only the model, storage and tool permissions it needs; separate retrieval read access from document-management rights; and review role assignments on a fixed schedule.
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Confirm encryption in transit and at rest, then determine whether customer-managed keys are available for the specific resource. If you use them, test rotation, revocation and recovery before production. A key option that is not enabled by default still creates a customer task.
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DLP and support access
Configure data-loss-prevention rules for prompts, uploads, outputs and logs. Establish customer-reviewed support access, with approval, time limits and audit records. Azure’s baseline distinguishes defaults from customer-configured options, so verify each setting instead of assuming a platform baseline applies automatically.
Auditability
Ensure logs identify the human or workload identity, model and feature used, data operation, administrative change and outcome. Send logs to a separate protected system and test that sensitive payloads are masked where full content is not required.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test AI-specific failure modes before approval
Conventional cloud penetration testing does not show how an AI application can disclose data through its instructions, context or tools. Run controlled tests with synthetic records first, then define safeguards for production.
- Prompt leakage: attempt to make the model reveal system instructions, hidden context or another user’s conversation.
- Retrieval authorization: use accounts with different permissions and verify that retrieval results follow source-system access rules.
- Prompt injection: place hostile instructions in documents, web pages or tickets and check whether they can override application rules.
- Tool abuse: test whether a model can send email, alter records, execute code or retrieve secrets without an explicit authorization step.
- Output exposure: submit records containing identifiers and confirm that generated responses, citations, logs and downstream systems do not reveal more than intended.
- Jailbreaks and anomalous use: monitor repeated policy-bypass attempts, unusual volume, mass extraction and requests that differ sharply from a user’s role.
Microsoft recommends private networking, managed identity, least privilege, AI-threat monitoring, red teaming, prompt DLP and audit trails for Azure environments. These are platform-specific implementation examples, not universal requirements or guarantees.
Make monitoring and review part of the approval
Approval should name the permitted data classes, users, features, regions and connected systems. It should also state prohibited inputs, retention and deletion requirements, required human review, the exception route and the evidence an owner must maintain.
NIST’s voluntary AI Risk Management Framework and its Generative AI Profile treat risk management as a lifecycle activity spanning design, development, use and evaluation. Apply that approach by monitoring generated content for possible personal-information or sensitive-data exposure, setting collection and retention policies, and reviewing whether controls still work after a model or feature change. The framework’s trustworthiness characteristics include security, resilience, accountability, transparency, privacy and fairness.
Set review triggers rather than relying only on an annual date:
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- the provider changes a model, feature, service tier, region or subprocessor;
- your application adds retrieval, agents, tools, uploads or persistent state;
- the data source or approved classification changes;
- an incident, near miss, unexplained output or access anomaly occurs; or
- contract, retention, support-access or training terms change.
Compare providers using one evidence table
When evaluating more than one service, require equivalent answers and evidence. Mark a cell “not stated” when the provider has not established the point; do not infer protection from a general security page.
| Comparison axis | Evidence to request |
|---|---|
| Data use | Training and improvement rules for prompts and outputs, abuse monitoring, human access and subprocessors. |
| Persistence | Storage and deletion behavior for uploads, retrieval stores, histories, embeddings, fine-tuning data and logs. |
| Geography | Processing and storage regions, residency controls, failover locations and cross-border access. |
| Security configuration | Private networking, identity integration, least privilege, encryption, customer-managed keys, DLP, logging and support-access approval. |
| Governance | Audit evidence, incident notification, response commitments, change notices and customer controls. |
| Responsibility boundary | Provider-operated layers versus customer-configured and customer-monitored layers for the intended SaaS, PaaS or IaaS deployment. |
| Contract scope | Proof that each commitment covers the exact product, model, tier, region and features you will use. |
Final approval checklist
- Document the use case, users, data classes, jurisdictions, outputs and connected systems.
- Inventory prompts, files, retrieval context, embeddings, histories, training data, telemetry and outputs.
- Obtain product-specific terms covering use, retention, deletion, geography, subprocessors and support access.
- Complete a responsibility matrix with named owners and retained evidence.
- Enable and test private networking, identity, least privilege, encryption, keys, DLP and audit controls.
- Run prompt-injection, retrieval-authorization, tool-abuse, jailbreak and output-exposure tests.
- Record approved and prohibited data, exception handling, monitoring and review triggers.
- Reapprove the service when its model, feature, region, contract or data flow changes.
A provider is suitable only when the documented terms, configured controls and tested behavior match the sensitive-data use case you actually intend to run. A general assurance is not a substitute for evidence at that scope.
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