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Before sending sensitive information to an AI cloud provider, prove exactly what leaves your systems, what the service stores, who can access it, where it is processed, and which safeguards you must configure. Approval should cover the specific product, model, service tier, region, contract and enabled features—not a provider’s general marketing statement.
Why an AI cloud approval is different from an ordinary cloud migration
Moving an AI workload does not transfer all responsibility to the provider. Microsoft’s shared-responsibility guidance states, “For all cloud deployment types, you own your data and identities.” In practice, you remain accountable for data classification, user and service identities, permissions, application configuration, prompts, connected systems and the way employees use generated results. The provider operates some infrastructure and service layers; the boundary changes between software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS).
An AI application can also create more data paths than a conventional hosted application. A request may include a prompt, an attachment, retrieved passages, an image or audio file, an embedding, conversation state and telemetry. The response can then be copied into a ticketing system, database, log, email or another tool. Each path needs an owner, a purpose and a retention rule.
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1. Define the use case and the data boundary
Write a short “allowed use” specification before evaluating vendors. It should identify:
- Business purpose and expected benefit.
- Users, service accounts and administrators.
- Data sources and classification levels, including personal, financial, health, legal, trade-secret and regulated information.
- Expected inputs and outputs, including whether the model can generate decisions, code, communications or records.
- Jurisdictions in which data may be collected, processed, stored or accessed.
- Connected systems, agent tools, plugins, retrieval indexes and export destinations.
- Deployment type (SaaS, PaaS or IaaS) and the model, version and service tier.
Mark prohibited inputs explicitly. For example, you might permit redacted support tickets but prohibit unredacted identity documents, production credentials and data belonging to another customer. Define whether de-identification, tokenization or field-level masking is required before data reaches the model.
Questions that expose hidden scope
- Does the feature accept files, images, audio, video or archives in addition to text?
- Does retrieval send passages from an internal index with every request?
- Can an agent call email, databases, code repositories or external websites?
- Are conversations, workspaces or “memory” objects stateful?
- Will prompts or outputs be used for evaluation, abuse detection, service improvement or human review?
- Can administrators export, duplicate or re-share histories and generated files?
2. Map every data flow, not just the prompt
Request a data-flow diagram from the provider and create one for your application. Record the data element, destination, purpose, storage location, retention period, access group and deletion method for every step.
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| Data category | What to verify | Typical risk |
|---|---|---|
| Prompts and completions | Whether content is logged, retained, reviewed or used for training or improvement; controls for disabling or limiting those uses. | Confidential text appears in provider logs or support workflows. |
| Uploads and attachments | Object storage location, indexing behavior, retention, virus scanning, sharing and deletion of originals and derivatives. | A deleted chat leaves a file or extracted text behind. |
| Retrieval sources and augmented context | Which repositories are queried, how access permissions are enforced, and whether retrieved passages are cached. | One user receives another user’s restricted content. |
| Embeddings and vector indexes | Where vectors and metadata reside, who can query them, backup retention and destruction procedures. | Semantic search reveals sensitive relationships even when raw text is hidden. |
| Fine-tuning, evaluation and validation data | Training job isolation, copies, checkpoints, artifacts, retention and staff access. | Examples persist in model artifacts or test systems. |
| Conversation state and memory | Scope (user, workspace or tenant), expiration, administrator visibility, export and deletion behavior. | Old context silently influences a later answer. |
| Telemetry, safety and abuse signals | Fields collected, recipients, cross-border transfers, retention and human review. | Operational logs contain the same sensitive values as the request. |
| Outputs and downstream records | Where responses are stored, who can edit or distribute them, and whether generated content becomes a business record. | Incorrect or sensitive output is copied into an authoritative system. |
Azure documentation illustrates why this inventory must be feature-level: it distinguishes prompts and generated content, uploaded data, stateful entities, augmented data and training or validation data. Some optional capabilities persist message history or other content according to configuration. Treat each enabled feature as a separate data-processing decision.
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3. Read the exact terms, settings and service scope
Collect the contract, data-protection addendum, privacy documentation, service terms, subprocessor list, regional availability statement and support-access policy for the exact product. Save the version or effective date reviewed. Confirm that the commitment covers the model, tier, region and features in your design.
Data-use language
Separate these questions instead of accepting a single “no training” statement:
- Are prompts and completions used to train foundation models?
- Are they used to improve the provider’s service, safety systems or quality measurements?
- Are they retained for abuse monitoring, troubleshooting or billing?
- Can authorized personnel view content, and under what approval process?
- Are uploaded files, retrieval indexes, embeddings, fine-tuning sets and evaluation data treated differently?
- Can your organization opt out, and is the setting enabled by default?
Microsoft’s Azure material says the documented Azure Foundry models do not use customer data to train foundation models or improve services without permission or instruction. The same material describes service processing and optional persistence. This is a scoped vendor claim for the documented service, not a universal rule for all AI products.
Retention and deletion
Obtain concrete timelines for active data, backups, replicas, caches, safety logs, support tickets, model artifacts and derived indexes. Ask what “delete” means operationally, when deletion starts, what exceptions apply and how completion is evidenced. Test deletion in a non-production account before approval, including a conversation, attachment, retrieval record and exported output.
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Geography and transfers
Identify processing and storage regions separately. A region selector may govern primary storage while support, abuse monitoring, backups or subprocessors operate elsewhere. Document cross-border access, transfer mechanisms and any customer controls for residency. Have privacy counsel assess obligations for your jurisdictions and data categories; a framework or provider statement is not a legal determination.
4. Assign the shared-responsibility controls
Create a responsibility matrix with a named owner, configuration location and evidence for every control. The following baseline prevents “the provider handles security” from becoming an unowned task.
| Control area | Customer responsibilities to assign | Evidence to retain |
|---|---|---|
| Data governance | Classification, minimization, masking, approved use cases and prohibited inputs. | Policy, data inventory and approval record. |
| Identity and lifecycle | Joiner/mover/leaver process, service-account ownership, multifactor authentication and privileged access review. | Identity configuration, role assignments and review logs. |
| Application and prompts | Input validation, instruction hierarchy, tool allowlists, output handling and secret filtering. | Code or configuration review and test results. |
| Network | Private connectivity, egress restrictions, DNS controls and separation of development from production. | Network diagrams and firewall or endpoint settings. |
| Keys and encryption | Key ownership, rotation, recovery, separation of duties and coverage for backups and derived stores. | Key-policy records and encryption configuration. |
| Logging and detection | Audit collection, retention, alert thresholds, review schedule and protected log access. | Sample events, alert tests and review minutes. |
| Support access | Approval rules, time limits, session recording and post-access review. | Support-access tickets and provider attestations. |
| Incident response | Escalation contacts, evidence preservation, notification decisions and recovery exercises. | Runbook and exercise results. |
| People and training | User training, acceptable-use rules and reporting channel for suspected exposure. | Training records and policy acknowledgements. |
Azure’s shared-responsibility and AI guidance place customer data, identities and configurations on the customer side, while responsibility for model and service layers varies by deployment. Use that principle to assign work in your own architecture rather than copying a generic checklist.
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Do not treat a feature listed on a product page as an enabled control. Inspect the tenant, subscription or project that will process sensitive data and record defaults, prerequisites, limits and exceptions.
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Identity and least privilege
- Use managed identities or equivalent workload identities instead of embedded keys.
- Give the application only the model, index and tool permissions it needs.
- Separate authoring, deployment, operations and audit roles.
- Require multifactor authentication and time-bound elevation for administrators.
- Review access after every organizational or architecture change.
Network and data protection
- Use private endpoints or equivalent private connectivity where the service supports it.
- Restrict outbound destinations and prevent an agent from reaching arbitrary hosts.
- Confirm encryption in transit and at rest for prompts, files, indexes, logs and backups.
- Evaluate customer-managed keys when your threat model or policy requires independent key control; verify which data stores they actually cover.
- Configure DLP or prompt inspection to block secrets, regulated identifiers and prohibited file types before transmission.
Microsoft’s Azure baseline distinguishes provider defaults from customer-configured options, including customer-managed keys, customer-reviewed support access and DLP. Confirm equivalent capabilities and their actual settings with whichever provider you select.
6. Test AI-specific failure modes before production
Conventional penetration testing is necessary but insufficient. Build test cases that exercise the model, retrieval layer, tools and logs together.
Prompt and retrieval exposure
- Submit direct requests for secrets and verify that policy blocks them.
- Attempt prompt injection through an uploaded document or retrieved web page.
- Test whether a user can induce the system to quote another tenant’s indexed content.
- Check that redaction occurs before logging and before tool calls, not only in the final answer.
Agent and permission abuse
- Ask the agent to call a tool outside its allowlist or to combine individually harmless permissions into a sensitive action.
- Verify approval gates for sending messages, changing records, executing code or downloading files.
- Use separate credentials for read and write operations and test revocation during a session.
Output and monitoring tests
- Feed known personal and confidential values and check whether they reappear in generated content, citations, logs or analytics.
- Test jailbreak attempts, anomalous request volume and repeated probing for restricted data.
- Confirm alerts contain enough context for response without copying the sensitive payload into a wider audience.
Microsoft recommends private endpoints, managed identity, least privilege, AI threat monitoring, red teaming, prompt DLP and audit trails for Azure environments. These are platform-specific examples, not guarantees. NIST’s Generative AI Profile recommends monitoring generated content for possible exposure of personally identifiable information or other sensitive data.
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When more than one service remains viable, fill one evidence table using the same questions and scope for each candidate. Mark an item “not stated” rather than inferring an answer.
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| Comparison axis | Evidence to record |
|---|---|
| Data use | Training and improvement rules for prompts and outputs, abuse monitoring, human access and subprocessors. |
| Persistence | Retention of uploads, histories, retrieval stores, embeddings, fine-tuning data, logs and backups; deletion timelines. |
| Geography | Processing and storage regions, residency controls and cross-border support or subprocessor 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, model or feature change notices and customer controls. |
| Responsibility boundary | Provider-operated layers versus customer-configured and customer-monitored layers in the actual SaaS, PaaS or IaaS design. |
| Contract and evidence | Whether each commitment applies to the selected product, model, tier, region and enabled feature, with an accountable provider contact. |
A service with fewer advertised controls can be safer for your use case if its relevant settings are available, enabled and verifiable. Conversely, a long feature list is not evidence that your tenant is configured securely.
8. Approve with conditions and monitor continuously
Record the final decision as an operational authorization, not a one-time procurement checkbox. Include:
- Approved data classes and explicitly prohibited inputs.
- Named users, applications, regions, models and features.
- Required identity, network, encryption, DLP, logging and support-access settings.
- Retention and deletion requirements, including evidence expected from the provider.
- Incident contacts, notification thresholds and evidence-preservation steps.
- Exception process, expiry date and compensating controls.
- Review triggers for a model, feature, contract, subprocessor, region, data source or architecture change.
Monitor usage and outputs for policy violations, unusual volume, prompt-injection attempts, jailbreaks, unauthorized tool calls and exposure of personal or confidential information. Review access and logs on a defined schedule, and repeat red-team or adversarial tests after significant changes.
NIST describes its AI Risk Management Framework as voluntary guidance for AI design, development, use and evaluation. Its trustworthiness characteristics include security, resilience, accountability, transparency, privacy and fairness. Use the framework to structure ongoing governance, while applying the legal and sector-specific requirements that govern your organization.
Practical go/no-go checklist
- The business purpose, users, data classes, jurisdictions and connected systems are documented.
- Every prompt, file, retrieved passage, embedding, state object, training item, telemetry field and output destination has a data-flow owner.
- Training, improvement, abuse-monitoring, human-access and subprocessor terms are confirmed for the exact service and configuration.
- Retention, backup, deletion and residency behavior is understood and testable.
- Customer and provider responsibilities are assigned in writing.
- Identity, private networking, least privilege, encryption, key, DLP, logging and support-access controls are enabled and tested.
- Prompt injection, retrieval leakage, agent abuse, jailbreak and output-exposure tests have passed or have documented compensating controls.
- An incident runbook, review cadence and change-trigger policy is in place.
- The approval names prohibited data and an exception owner.
Frequently Asked Questions
Does a provider’s “no training on prompts” promise make sensitive data safe?
No. You still need separate answers for uploads, retrieval context, embeddings, conversation history, fine-tuning and evaluation data, abuse monitoring, human access, logs, backups and generated outputs. The promise must also cover the exact product, model, region, tier and enabled features.
Who is responsible when an AI cloud application leaks data?
Responsibility depends on the deployment and contract, but customers retain responsibility for their data, identities, permissions and configuration. Providers operate infrastructure and service layers they control. A written responsibility matrix and tested controls are needed to determine who prevents, detects and responds to each failure.
Is a voluntary AI framework enough for regulatory compliance?
No. NIST’s AI Risk Management Framework and Generative AI Profile help organize lifecycle risk management, but they are voluntary guidance. Privacy, security, records and sector obligations vary by jurisdiction, data type and purpose; obtain qualified legal or privacy advice for a concrete determination.
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