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Integrate generative AI as a governed data-processing path, not as an isolated model call. Map every place sensitive information can travel, enforce the requesting user’s permissions at retrieval and tool boundaries, use a deployment with appropriate identity and network controls, restrict what is persisted, and continuously test the complete application for leakage. The model provider’s training policy is only one part of that design.
Why a model call can become a data-leak path
An enterprise AI feature may expose information even when the underlying model was never trained on that information. A prompt can include confidential text; retrieval can select a document the user is not allowed to see; conversation memory can carry one employee’s data into another session; a tool can return secrets; a vector index can have broader permissions than the source system; and logs or caches can preserve the entire interaction.
NIST’s Generative AI Profile, published July 26, 2024, treats privacy as a contextual risk. Whether data is sensitive depends on the person, purpose, jurisdiction, and surrounding information, not only on a fixed field list. Microsoft guidance on sensitive-information disclosure likewise identifies memory, retrieved snippets, tool outputs, vector-store entries, agent state, and logs as possible exposure points.
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Design the feature as a chain of trust:
- Identity: establish who the user and calling service are.
- Selection: decide which records, memories, and tools that identity may use.
- Transformation: minimize, redact, or pseudonymize data before it becomes model context.
- Generation: constrain the model and validate its output.
- Action: authorize any downstream operation independently of the model’s suggestion.
- Evidence: retain enough audit data to investigate without creating a second sensitive-data repository.
Start with a complete data-flow and trust-boundary map
Before selecting a model, draw the actual request path. Include synchronous and asynchronous paths, failure handling, and administrative access. The inventory should cover:
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- End-user prompts, uploaded files, and system instructions.
- Documents selected by search or retrieval-augmented generation, including snippets and metadata.
- Embeddings, vector databases, indexes, ranking services, and replicas.
- Conversation history, summaries, agent memory, session state, and workflow checkpoints.
- Model inputs, model outputs, safety-classification results, and post-processing queues.
- Tool calls, tool arguments, tool results, web or database connectors, and downstream actions.
- Application logs, traces, analytics events, caches, dead-letter queues, backups, and support exports.
For each flow, record the data owner, sensitivity, lawful or business purpose, geographic boundary, retention period, and systems or identities that can read it. Mark every trust boundary: browser to application, application to retrieval service, application to model endpoint, model orchestration layer to tools, and production systems to observability platforms.
Define allowed data by purpose
Do not use “internal” as a universal permission. Create explicit classes such as public, internal, confidential, regulated personal data, credentials, and highly restricted business secrets. Then define which classes are allowed for each operation. A customer-support summarizer may process a ticket’s contact details but not payment credentials; an engineering assistant may read a repository issue but not production secrets.
Keep the classification attached to records and fields as they move. If a retrieval result loses its tenant, owner, or sensitivity metadata, the next component cannot make a safe authorization decision.
Identify derived and residual data
Embeddings, summaries, tokenized text, prompts, and generated answers can still reveal the original information. Treat them as governed copies, not harmless technical artifacts. A deletion request or retention rule must account for indexes, summaries, memory, caches, backups, and logs, subject to the organization’s legal and audit requirements.
Choose a deployment based on controls, not labels
“Managed,” “private,” or “enterprise” does not by itself establish safe handling. Verify the service and contract used by your specific region, edition, and feature configuration. Ask how prompts, outputs, files, embeddings, and support data are retained; whether they are used for provider training; where they are processed; how deletion works; who can access them; and which tenant-isolation and audit controls are available.
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| Architecture | Data-handling questions | Identity and network questions | Operational trade-offs |
|---|---|---|---|
| Managed cloud AI service | What retention, training-use, residency, deletion, and support-access terms apply to the selected deployment? | Can the application use private connectivity, managed identities, tenant isolation, egress restrictions, and centralized audit logs? | Less infrastructure to operate, but provider configuration, contract terms, service updates, and regional availability must be governed. |
| Self-hosted model | Where are prompts, weights, indexes, backups, and telemetry stored, and who administers them? | Can access be isolated to approved networks and identities, with least-privilege service accounts? | More control over location and retention, but your team owns patching, model updates, capacity, incident response, and evaluation. |
| Hybrid pattern | Which data stays in controlled systems, and which content crosses into an external model service? | How are identities, private links, egress policy, and authorization context propagated across environments? | Can combine local controls with managed capacity, but creates more trust boundaries and failure modes to monitor. |
There is no universal best architecture. Compare candidates on data handling, identity propagation, network boundaries, retrieval and memory scoping, logging, incident response, update procedures, and accountable ownership. A design that meets residency requirements but cannot enforce document-level permissions is not suitable for permissioned enterprise search.
Example: harden an Azure OpenAI deployment
For Azure OpenAI, Microsoft’s security guidance recommends private endpoints to remove public endpoint access and recommends managed identity instead of API keys. These are Azure-specific examples, not requirements for every provider. Confirm that the chosen region and service features support the controls, then test them from the application network rather than assuming a portal setting is sufficient.
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The end user’s permissions must travel with the request. A backend service’s broad database role must not silently replace the user’s narrower rights.
Use ACL-aware retrieval
- Authenticate the user and resolve the tenant, groups, roles, and document-level permissions.
- Pass an authorization context to the retrieval layer, or pre-filter candidates in a policy-enforcing service.
- Apply the permission filter before snippets, titles, metadata, or embeddings are returned to the orchestrator.
- Re-check authorization immediately before assembling model context, because permissions may have changed during a long workflow.
- Record which policy decision allowed each source, without logging the full sensitive text by default.
Filtering only after generation is too late: unauthorized content may already have reached the model, a cache, a trace, or an intermediate agent. Also protect metadata. A document title, filename, embedding-neighbor score, or error message can disclose confidential projects even when the body is hidden.
Scope memory and agent state
Give memory an explicit owner and purpose: user, tenant, case, workflow, or none. Do not place all users in one undifferentiated conversation store. Enforce scope on reads and writes, encrypt the store, define expiration, and provide deletion and correction paths. Summaries created for convenience should inherit the source permissions and retention policy.
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Authorize tools independently
Tool access is an action boundary, not a prompt feature. The model may propose a database query, email, refund, deployment, or file operation, but an application policy must validate the actor, target, parameters, and business state before execution. Use separate service identities for separate tools; avoid a single agent credential that can read and modify every system.
For high-impact actions, require deterministic validation and, where appropriate, human approval. Constrain arguments with schemas, allow-list destinations and operations, set timeouts and rate limits, and return the minimum result needed by the model. Never treat a tool result as trusted merely because it came from an internal service.
Minimize and protect prompts and outputs
Minimize before sending
- Send only fields needed for the task; remove unused columns and free-text history.
- Redact credentials, private keys, session tokens, and regulated identifiers.
- Use pseudonyms or stable internal IDs when the model does not need a person’s name.
- Truncate old context and retrieve the smallest relevant passages.
- Keep system instructions free of secrets and operational credentials.
Redaction is not a substitute for authorization. A user who is not allowed to read a record should not receive a redacted version unless policy explicitly permits that use.
Validate generated output
Apply output checks before displaying or acting on a response. Detect secrets and regulated data, enforce destination and audience rules, validate structured output against a schema, and block or route uncertain cases for review. A confident answer can still contain an unauthorized quote, an invented sensitive attribute, or a tool instruction hidden in retrieved content.
Keep model output separate from executable instructions. Do not concatenate generated text into SQL, shell commands, HTML, policy expressions, or access-control decisions without safe parsing, parameterization, and deterministic validation.
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Govern logs, caches, indexes, and observability
Telemetry often has wider access and longer retention than the application database. Establish a specific policy for:
- Prompts and outputs: log identifiers, policy decisions, latency, and model version by default; capture full text only when justified and protected.
- Retrieval artifacts: record source IDs and authorization outcomes rather than unrestricted snippets.
- Memory and vector stores: apply tenant or user scope, encryption, retention, deletion, backup, and audit controls.
- Tool calls: capture actor, tool, operation, target, approval, result status, and correlation ID; redact arguments that contain secrets.
- Caches and queues: prevent cross-tenant keys, encrypt contents, set expiry, and purge failed or abandoned jobs.
- Support and debugging exports: require approval, minimize fields, time-limit access, and audit downloads.
Use data-loss-prevention or equivalent policy checks where available, but understand their limits. A detector may miss a novel identifier, an encoded secret, or a sensitive fact spread across several fields. Combine automated detection with classification, access controls, retention enforcement, and human review for high-risk workflows.
Defend against prompt injection and untrusted retrieved content
Retrieved documents are data, not instructions. A malicious or compromised document can tell an agent to ignore its rules, reveal hidden context, or call a dangerous tool. Separate trusted system policy from untrusted content, label the source of each passage, and instruct the model to treat retrieved text as evidence only.
- Scan documents and tool results for instruction-like content and suspicious encodings.
- Use an allow-list of tools and operations for each workflow.
- Keep secrets and privileged instructions out of model-visible context whenever possible.
- Require application-level approval for external communication, financial changes, code deployment, or destructive operations.
- Limit retries and agent loops so an injection cannot cause unlimited extraction or actions.
Prompt-injection defenses are layered controls, not a promise that a model will always obey. The decisive safeguard is that a compromised response still lacks the permissions to read or change anything it should not.
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A useful reference flow is:
- Authenticate: establish the human identity, tenant, device or workload identity, and session risk.
- Authorize the use case: confirm that this user may invoke this AI feature with this class of data.
- Minimize: classify, redact, and transform the prompt and uploaded material.
- Retrieve: query only sources filtered by the user’s current permissions.
- Assemble context: enforce size, sensitivity, tenant, and retention rules; label untrusted content.
- Generate: call the approved model endpoint through private, monitored network paths.
- Validate: check the output for policy violations, sensitive disclosure, schema errors, and unsafe instructions.
- Authorize actions: re-check permissions and business rules before every tool call or downstream change.
- Persist selectively: save only the memory, history, and audit evidence required by policy.
- Monitor: correlate access, retrieval, model, tool, and deletion events for investigation.
Keep these decisions in application code or a dedicated policy service rather than relying on a prompt to enforce them. An illustrative policy result might be:
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{
"allow": true,
"tenant": "resolved-tenant",
"sources": ["documents-authorized-for-user"],
"tools": ["read-case-status"],
"retention": "session-only",
"redactions": ["credential", "payment-token"]
}The values above are conceptual. Use your platform’s actual policy language, and make a denied decision fail closed with a useful, non-sensitive error.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test the complete system before production
Unit-testing a prompt or model in isolation cannot reveal cross-component leakage. Build adversarial and regression tests around the full application.
Authorization and isolation tests
- Ask a user to retrieve another user’s document, memory, title, metadata, and embedding-neighbor result.
- Switch tenants within one browser session and verify that caches, conversation IDs, and queued jobs cannot cross the boundary.
- Revoke a document permission, then test retrieval, memory summaries, indexes, and existing sessions.
- Attempt tool calls with a role that can view data but cannot modify it.
Injection and disclosure tests
- Place instructions in a retrieved document telling the agent to reveal its system prompt or call an unauthorized tool.
- Use encoded, fragmented, or indirect requests for credentials and personal data.
- Check whether errors, traces, analytics, and support exports contain full prompts or tool results.
- Test sensitive-output detection with realistic names, identifiers, secrets, and combinations of innocuous fields.
Operational and resilience tests
- Verify private-network failure behavior and ensure the application does not silently fall back to a public endpoint.
- Confirm timeouts, rate limits, retry policies, queue expiry, and deletion jobs.
- Test provider or model-version changes against a fixed safety and authorization suite.
- Exercise incident procedures: disable a connector, revoke an identity, preserve audit evidence, purge affected artifacts, and notify the right owners.
Microsoft recommends security evaluation before deployment and integrating security testing into delivery workflows. NIST SP 800-218A, published in July 2024, provides a generative-AI-specific companion profile for secure software-development practices. Use those practices to assign owners, acceptance criteria, and release gates rather than treating evaluation as a one-time demonstration.
Monitor production for evidence of misuse or drift
Collect privacy-conscious signals that answer who accessed what, under which policy, and what action followed. Useful indicators include denied retrievals, unusual extraction volume, repeated requests for restricted fields, cross-tenant key collisions, tool calls outside normal workflows, prompt-injection detections, and sudden changes in output-sensitive-data rates.
Correlate events with a request ID across the gateway, policy engine, retrieval service, model endpoint, tools, and deletion system. Protect the monitoring data itself with least privilege and retention limits. Alerting should identify a suspicious pattern without copying the underlying confidential text into a broad notification channel.
Plan for model and provider changes
Model updates, new context-window behavior, retrieval changes, and provider policy changes can alter both functionality and exposure risk. Record the model and configuration used for each high-risk action, review release notes and contractual terms, rerun the evaluation suite, and obtain approval before enabling materially different capabilities.
Common design failures and their fixes
| Failure | Why it is unsafe | Fix |
|---|---|---|
| “The provider does not train on our prompts, so we are safe.” | Retrieval, memory, tools, logs, caches, and support access can still disclose data. | Govern every persistence and access point, not only model training use. |
| One service account retrieves all documents. | The model receives content the end user is not authorized to see. | Propagate user permissions and enforce ACL-aware retrieval before context assembly. |
| All conversations share one memory store. | Summaries and facts can cross users, tenants, cases, or purposes. | Scope memory, keys, indexes, retention, and deletion by the correct owner. |
| Full prompts are copied into debug logs. | Observability systems often have broader access and longer retention. | Redact by default; log identifiers and decisions, with controlled exceptions. |
| The model directly executes generated commands. | Prompt injection or hallucination can become a privileged action. | Use schemas, allow-lists, independent authorization, limits, and approvals. |
| Security testing covers only the model. | Leaks often occur in retrieval, orchestration, storage, or downstream tools. | Test the full application and its trust boundaries before and after release. |
A practical launch checklist
- Every prompt, retrieval result, memory item, tool output, cache, and log has an owner, classification, purpose, and retention rule.
- User and service identities are distinct, least-privileged, and auditable.
- Retrieval and memory enforce tenant, user, task, and document permissions before model exposure.
- Network paths, endpoint exposure, encryption, egress, and provider access are documented and tested.
- Prompts and outputs are minimized, redacted where appropriate, and checked for sensitive disclosure.
- Tools use constrained schemas and independent authorization; high-impact actions have approval or deterministic controls.
- Logs and traces exclude sensitive content by default and are protected with their own access and retention policy.
- Adversarial tests cover cross-user leakage, revoked access, prompt injection, unsafe tools, and failure paths.
- Monitoring, incident response, deletion, rollback, and model-update procedures have named owners.
What “without exposing sensitive data” can honestly mean
No architecture can promise that a generative-AI system will never encounter sensitive information. The defensible objective is controlled processing: sensitive data enters only when the use case permits it, reaches only authorized components, is retained only for an approved purpose and period, and cannot trigger unauthorized disclosure or action. That objective requires controls around the model and evidence that those controls work in the deployed configuration.
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