You can’t make confidential-data exposure impossible, and no provider, encryption setting or system-prompt instruction will do that for you. You can shrink the exposure a lot by doing four things: send the model less data, enforce each user’s permissions before content reaches the model, pick a provider on verified terms for the exact service you deploy, and test for cross-user leakage before release and after every change.
The model is only one stop on the data path. Prompts, retrieved records, embeddings, memory, caches, logs, tool results and agent state can all keep or reveal confidential information. Most of the controls that matter are yours, not the vendor’s. This guide covers the architecture, a provider-evaluation framework and a test plan.
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Map the full data path first
Microsoft’s guidance on sensitive information disclosure in AI systems and its security planning for LLM-based applications both treat the application, not just the model, as the thing to secure. Draw every place data lands, then treat each as a sensitive data store with its own owner, scope and retention period.
The Tool Desk
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|---|---|---|
| Prompts and outputs | Users paste secrets in; the model repeats context it was given | Input/output scanning, DLP, user guidance, output handling |
| Retrieved records | Retrieval runs under a broad service identity, so a user sees documents they shouldn’t | Per-user authorization applied before context is assembled |
| Embeddings and vector indexes | Derived copies of source documents, often stored without the source’s access rules | Index isolation per tenant or permission group, deletion that reaches the index |
| Memory, histories, summaries | Content from one session or user resurfaces in another | Scope to user, tenant, purpose and retention period |
| Caches | A cached answer to one user’s question is served to another | Include identity and permission scope in cache keys, or don’t cache sensitive responses |
| Logs and traces | Full prompts and outputs sit in observability tools with wide access | Redact PII and secrets, limit what is logged, set retention |
| Tool results and agent state | Connectors return more than the task needs; agents can send data outward | Least-privilege tools, restrictions on outbound and write actions |
Who is responsible for what
Microsoft puts it plainly: “The customer-controlled cloud components vary by service type.” (Microsoft Learn, Security planning for LLM-based applications). A managed API shifts model hosting, patching and some infrastructure security to the provider. It doesn’t shift your retrieval permissions, your logging, your vector store or your connectors.
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- Provider commitments cover training use, retention, processing location, staff access and certifications. They apply to specific products, endpoints and eligible customers.
- Your responsibilities cover what you send, who may see what, what your own systems store, and how you configure the provider’s options.
A no-training commitment doesn’t stop your application from showing user A’s document to user B. Those are separate failure modes.
Step 1: Set data boundaries before writing code
Most exposure is cheaper to prevent at the source than to filter later. Microsoft’s Azure Well-Architected AI design principles and its AI risk assessment guidance point to the same groundwork.
- Inventory and classify. List each data source with its sensitivity, owner, permitted use and retention rule.
- Assign allowed uses per class. For each class, decide whether it may be used for inference, retrieval, evaluation, fine-tuning, or none of these.
- Record provenance. Note where acquired data came from and who approved it. Validate and review content before ingestion. Don’t assume inference data is safe if it might later feed a training or evaluation set.
- Reduce at the source. Remove confidential and personal data you don’t need from primary stores, indexes, caches and application artifacts. Data that was never sent can’t leak.
Step 2: Evaluate the provider for the exact service you’ll use
Provider policies depend on product, endpoint, configuration, customer eligibility and region. A vendor-wide marketing claim isn’t enough. Write a review for the specific API, model and feature set you intend to ship, and answer these questions from current contract terms and documentation:
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|---|---|
| Are inputs and outputs used for training or improvement by default? What opt-in or exception paths exist? | Defaults can differ between consumer, business and API products |
| What is retained, for how long, and where? Does this change for abuse monitoring, stateful features, tools, files or logs? | Features that store state or files can have different retention from plain inference calls |
| Can you control retention, data residency, processing region, encryption keys and access? | Some controls exist only for eligible customers or certain endpoints |
| Which attestations and contractual commitments cover this product and region? | Certifications attach to named services, not to a vendor as a whole |
| Does your threat model need confidential computing or similar isolation? | It addresses a specific threat (see below), not all of them |
Example: what one major provider states
OpenAI’s business data privacy, security and compliance page says: “By default, we do not use data from ChatGPT Enterprise, ChatGPT Business, ChatGPT Edu, ChatGPT for Healthcare, ChatGPT for Teachers, or our API platform—including inputs or outputs—for training or improving our models.” It also says: “Qualifying organizations are able to configure how long OpenAI retains business data, including opting for our zero data retention policy in the API platform.”
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These are vendor statements, and the qualifiers matter. “Qualifying organizations” means eligibility applies, and you need to confirm which endpoints a retention option covers. Check the terms that apply when you sign, not the page as it read when you first evaluated. Terms change, so make this a recurring review rather than a one-time gate.
Where confidential computing fits
Microsoft describes confidential AI as using trusted execution environments to protect data and model artifacts in specified training and inference scenarios. It is relevant when your threat model includes privileged infrastructure access, such as a cloud operator or a compromised host. It does nothing about an application that retrieves the wrong documents for a user. Treat it as an addition to authorization and data minimization, and confirm your specific workload is supported.
Step 3: Build the application-layer controls
This is where most real-world leaks originate, and it is entirely your responsibility.
Carry authorization through retrieval
A user who can call the AI feature shouldn’t inherit everything the service identity can read. Apply the caller’s permissions at query time, so unauthorized documents never enter the prompt. In practice that means storing access metadata (owner, tenant, group, label) with every chunk and filtering on it in the retrieval query, or using per-tenant indexes for stronger separation. Don’t put restricted content in the context and instruct the model not to reveal it. Models can be talked out of instructions, and the content has already crossed the boundary.
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Treat retrieved content as untrusted
Documents, web pages, emails and tool output can contain instructions aimed at the model. Microsoft’s security planning guidance lists prompt injection as a core risk. Keep untrusted content separate from system instructions, limit what it can influence, and don’t let a model that has read untrusted text hold powerful tools without extra checks.
Scope memory, caches and indexes
Tie conversation history, summaries, long-term memory, caches and vector stores to the intended user, tenant, purpose and retention period. Build deletion in from the start. When a source document or a user’s data is deleted, the derived embeddings, summaries and cached answers should go too.
Constrain tools and agents
Give each tool the narrowest permissions it needs. Be most careful with outbound transfers and write operations. Microsoft’s Azure AI security best practices recommend least privilege and, where appropriate, human review for high-risk external transfers or configuration changes. Use separate credentials for read-only and write functions so a manipulated agent can’t escalate on its own.
Step 4: Protect storage, networks and operations
- Encryption. Encrypt sensitive data at rest and in transit. Consider customer-managed keys where your risk level justifies them and the service supports them.
- DLP and labels. Apply data loss prevention and sensitivity labels to data your AI application can reach and, where supported, to prompts. The Azure guidance notes that “AI applications often interact with sensitive data.”
- Logging. Redact PII and secrets from logs. Decide which prompt and output content you need for monitoring and how long to keep it. Debug logs that capture full prompts are a common way to create a second, less protected copy of your most sensitive data.
- Monitoring. Watch data access, privileged activity, connector behavior and unusual retrieval or output patterns. Keep audit trails that match your retention and privacy obligations.
- Change control. Revisit data boundaries whenever you add a tool, connector, model, agent or memory feature. Each opens a new place for content to persist or cross a trust boundary.
- Incident handling. Include AI-specific scenarios in your runbooks, such as a cross-user disclosure or a poisoned document. You need to be able to switch off a connector, purge an index or cache, and find which users’ data was involved from your audit logs.
Choosing an architecture: what each option does and doesn’t solve
These approaches address different problems, and the guidance available doesn’t rank them. No independent benchmark shows one is best, so the right mix depends on your data classes, jurisdiction, threat model and operating capacity.
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| Approach | What to compare |
|---|---|
| Hosted enterprise AI API | Training-use terms, retention, endpoint scope, region, access and audit controls |
| Self-hosted or private deployment | Operational burden, patching, model supply chain, data boundary, who owns security |
| Retrieval-augmented generation (RAG) | Per-user authorization, source freshness, index isolation, deletion, prompt-injection handling |
| Fine-tuning | Whether sensitive examples are truly necessary, who can query the tuned model, how exposure is evaluated |
| Confidential computing | Whether your threat model includes privileged infrastructure access, and whether your workload is supported |
| DLP and governance platform | Coverage across prompts, outputs, retrieval, memory, logs, connectors and the points where it can enforce policy |
Two trade-offs deserve emphasis. Self-hosting gives you a tighter data boundary but moves patching, model provenance and security monitoring onto your team. Fine-tuning on sensitive records can bake that information into model weights, which are hard to scope per user, so prefer retrieval with enforced permissions when different users have different access rights.
Step 5: Test for leakage before release and on every change
Use controlled test data, not real customer records, and run red-team exercises as Microsoft’s AI risk assessment guidance describes. Record each failure with an owner, a mitigation and retest evidence.
| Test | How to run it | Pass condition |
|---|---|---|
| Cross-user and cross-tenant retrieval | Seed distinct tenants with unique marker strings. As one user, ask directly and indirectly for another’s content | No marker from another tenant appears in answers, citations or retrieval logs |
| Prompt injection in content | Plant instructions in documents, web pages and tool output that try to exfiltrate data or call tools | The instructions are ignored, and no unauthorized tool call or outbound transfer occurs |
| Secret and PII detection | Insert fake secrets and PII at input, in retrieved context, in output, in memory writes and in logs | Detection or redaction triggers at each point you’ve defined |
| Deletion and retention | Delete a source document or user, then query for it. Check caches, histories, summaries and indexes | Derived copies are gone within your stated retention window |
| Connector and tool permissions | Attempt writes and external sends with a read-only role or low-privilege user | Denied or routed to human approval |
| Provider configuration drift | Periodically verify endpoint, region, retention and training-use settings against your approved baseline | No unapproved change since the last review |
| Access review | Review service identities, connector scopes and who can read logs and indexes | Access matches current need |
Passing these tests shows you found no leak by these methods. It doesn’t prove none exists. The cited sources don’t claim a test suite can prove that, and none of them quantifies how much any safeguard reduces exposure. Rerun the matrix whenever you change a model, prompt template, connector, index schema or provider setting.
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