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Use defense in depth. A safe generative-AI deployment ties every request to an authenticated person or workload, checks that identity’s current permission on each source and tool, inspects data before and after model processing, and records decisions for review. No single browser block, network gateway, or model setting can prevent leakage on its own.
This guide gives a practical sequence for consumer and enterprise AI services, custom applications, retrieval-augmented assistants, and autonomous agents. It covers least-privilege authorization, identity and device controls, prompt and output DLP, agent guardrails, monitoring, and incident response.
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What access control and DLP must protect
Generative-AI systems move information through more boundaries than a conventional business application. A user may type a prompt, attach a file, retrieve passages from a repository, invoke a tool, receive an answer, copy it to a document, or export it to an external destination. Controls must cover every stage.
| Stage | What can go wrong | Control objective |
|---|---|---|
| Sign-in and session | Stolen credentials, unmanaged devices, risky locations, long-lived tokens | Strong authentication, conditional access, short sessions, and risk-based decisions |
| Prompt and upload | Confidential text, credentials, payment data, or regulated records sent to an unsuitable service | Classify, warn, block, mask, or tokenize before transmission |
| Retrieval | An assistant returns a document because the application can read it, even though the requesting user cannot | Recheck the user’s permission on every retrieval |
| Tool use | An agent changes records, sends mail, or runs code outside its business purpose | Allowlist tools, narrow scopes, separate read/write credentials, and require approval for high-impact actions |
| Output and export | Generated text reproduces sensitive material or is copied to an unapproved site | Inspect output, apply labels, control copy/download/export, and enforce destination policy |
| Operations | Abuse or policy bypass goes undetected | Log decisions, detect anomalies, review overrides, and respond quickly |
Apply the same design to human users, service principals, plug-ins, scheduled jobs, and agents. A nonhuman identity is still an identity and should have an owner, a purpose, an expiry or review date, and only the permissions required for its task.
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1. Inventory and classify every AI path
Begin with an authoritative register rather than a list of approved chat products. Include shadow use discovered through identity, proxy, DNS, endpoint, and expense data.
Record these assets
- Consumer and enterprise SaaS applications, model endpoints, SDKs, plug-ins, browser extensions, custom applications, and internal assistants.
- Agents, service principals, managed identities, API keys, scheduled jobs, and automation accounts.
- Connected repositories, vector stores, file shares, databases, ticketing systems, source-control projects, and communication tools.
- Every input and export path: browser paste, file upload, API request, clipboard, download, email, collaboration workspace, and external storage.
Assign a policy record
For each item, document an accountable owner, business purpose, data classifications allowed, geography, retention period, model-training terms, connected resources, permitted actions, and required logging. Mark whether the service handles personal, financial, health, legal, export-controlled, customer, or trade-secret data. If a provider cannot meet the required retention or regional controls, prohibit that data class there.
Microsoft Entra guidance specifically recommends identifying nonhuman accounts, removing excessive roles, and using managed or workload identities. Delete unused keys and disable abandoned integrations; an unowned connector cannot be reviewed or revoked reliably.
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Least privilege is not achieved by giving an AI application a broad repository role and trusting the model to filter results. Authorization must be evaluated in the context of the requesting user, the resource, the action, and current policy.
Use RBAC or ABAC deliberately
Role-based access control (RBAC) works well for stable job functions. Attribute-based access control (ABAC) adds conditions such as department, project, data classification, geography, device state, or time. Many environments use both: roles establish a baseline and attributes narrow it for a particular request. NIST SP 800-210 describes RBAC and ABAC patterns for cloud IaaS, PaaS, and SaaS.
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Enforce at three boundaries
- Retrieval: Pass the user identity and relevant claims to the search or retrieval layer. Filter results against the source system’s current permissions before returning passages to the model.
- Tool invocation: Authorize the specific operation, not merely access to the tool. A support agent may read a ticket but not close it, change an account, or export a customer list without a separate permission.
- Export: Re-evaluate policy when content leaves the application. A user who may view a record does not automatically have permission to download, share, or send it externally.
Never infer authorization from a document’s presence in an index or vector database. When a user’s role changes, source permissions and cached embeddings must be updated or filtered so stale access cannot persist.
Use eligible, time-bound privilege
For administration and sensitive data, assign an eligible role that becomes active only for an approved period. Require justification, approval, and stronger authentication for activation, then remove the role automatically. Schedule access reviews for repository groups, service principals, agent tools, and data connectors; revoke access when a person changes role or leaves.
3. Protect identity, device, and session
Identity controls determine who may use an AI system and under which conditions. Microsoft describes Conditional Access as an if-then policy engine that combines signals to enforce organizational policy.
Baseline conditions
- Require phishing-resistant multifactor authentication for privileged administrators and operators of models, repositories, and agents.
- Evaluate sign-in risk, user risk, device compliance, operating-system support, location, network, and application sensitivity.
- Block legacy authentication and unmanaged devices for sensitive applications, or require a managed browser with download and clipboard restrictions.
- Use separate identities for human administrators, applications, and agents. Do not embed a person’s credentials in an automation.
- Issue short-lived tokens and rotate secrets. Prefer managed or workload identities over long-lived API keys.
Step-up authentication should occur before high-risk retrieval, privilege activation, bulk export, or a tool action that changes data. A compliant device alone is not proof that a request is safe; combine device, identity, behavior, and resource signals.
4. Prepare data before the model sees it
Classification is the foundation for DLP. Apply sensitivity labels or an equivalent taxonomy to files, records, messages, and database fields. Fix overshared repositories before connecting them to retrieval; an assistant faithfully enforcing a badly configured source will still expose too much.
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Minimize and transform sensitive values
- Mask or tokenize personal identifiers, payment numbers, credentials, and proprietary fields when the task does not require the raw value.
- Send only the fields and passages needed for the answer. Limit retrieval count, document size, and conversation history.
- Keep secrets out of prompts, system instructions, logs, and evaluation datasets. Use a secret manager and pass an opaque reference when a tool needs a credential.
- Apply retention and deletion rules to prompts, attachments, embeddings, conversation history, and provider-side logs.
Microsoft Purview documentation states that supported AI applications can use existing tenant permissions so content is not returned to users who lack access; sensitivity labels provide an additional protection layer. Verify the exact applications, workloads, and label behaviors your edition supports before relying on them.
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5. Design DLP for prompts, context, outputs, and destinations
A prompt-only rule is incomplete. Inspect the data entering the model, the context retrieved on the user’s behalf, the generated response, and every route by which either can leave the organization.
Recommended inspection points
| Inspection point | Examples of detections | Possible action |
|---|---|---|
| Prompt text | Credential patterns, payment-card numbers, regulated identifiers, confidential project names | Allow, warn, redact, or block |
| Attachments and uploads | Classified files, source code, customer exports, hidden metadata | Block upload, require justification, or route to an approved tenant |
| Retrieved context | Documents above the user’s clearance or outside the task’s purpose | Remove passage, stop the request, or escalate |
| Generated output | Reproduced secrets, personal data, restricted source text, unsafe instructions | Redact, label, hold for review, or block export |
| Clipboard, download, and print | Movement from a managed AI app to local or removable storage | Warn, block, or require a business reason |
| Destination | Consumer AI site, personal email, public repository, unmanaged cloud storage | Block, warn, or permit only approved domains |
Match enforcement to risk
Use a graduated response. A low-risk classification may generate an unobtrusive audit event. A moderate-risk event can warn and require the user to confirm a business reason. High-risk data should be blocked or quarantined. Record the rule, classification, user, application, destination, and action so an override is reviewable.
Microsoft documents Endpoint DLP policies that can warn or block sharing sensitive information with third-party generative-AI sites in a browser, including pasting payment-card data into a chat service. Configure equivalent controls for uploads, clipboard, downloads, and browser destinations rather than relying on one paste rule.
Cover unmanaged and consumer use
For services outside organizational administration, route traffic through an approved proxy, secure web gateway, CASB, or egress firewall where feasible. AWS notes that these network controls can detect and prevent some unauthorized use but are not airtight: a user can switch networks, use a personal device, or employ an unobserved channel. Pair network inspection with endpoint DLP, identity policy, training, and contractual restrictions.
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6. Constrain agents and high-impact actions
An agent combines model-generated decisions with real permissions. Treat its plan as untrusted input and make the deterministic control plane authoritative.
Build an explicit tool policy
- Allowlist tools by agent and environment; deny discovery of arbitrary plug-ins or endpoints.
- Define narrow scopes such as “read open tickets for this customer” instead of broad database access.
- Separate read credentials from write credentials and place write operations behind a distinct approval path.
- Limit records, amount, frequency, geography, and time window for each operation.
- Require human approval or adaptive authentication for deletion, financial transfer, external communication, permission changes, or bulk export.
For systems with weak downstream authorization, AWS recommends a deterministic broker that can inspect the request, apply policy, and require adaptive authentication before high-risk data changes. The broker should validate structured parameters independently of the model and fail closed when policy information is missing.
Harden the application layer
Sanitize inputs, treat retrieved text as data rather than instructions, and isolate system prompts from user-controlled content. Validate tool arguments against schemas, reject unexpected URLs or commands, and apply output filtering before rendering or execution. Do not allow a model to decide that it has permission; it may propose an action, but an external policy engine must authorize it.
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Logging is part of the control, not an optional observability feature. Capture enough context to reconstruct a decision while collecting no more prompt content than policy and law permit.
Events to retain
- Sign-ins, MFA and conditional-access results, device and risk signals.
- Retrieval requests, repositories queried, documents or records returned, and permission decisions.
- Tool calls, parameters, approvals, denials, token or secret use, and resulting changes.
- DLP matches, warnings, blocks, user justifications, overrides, downloads, and external destinations.
- Administrative changes to models, connectors, policies, roles, service principals, and retention settings.
Useful detections
- Unusual prompt or output volume, rapid bulk retrieval, or a sudden increase in exports.
- Repeated policy overrides, failed step-up authentication, or attempts to activate privileged roles.
- Access to repositories outside a user’s normal geography, team, or work pattern.
- Agent behavior that calls tools in an unexpected sequence or exceeds rate, record, or value limits.
- Attempts to exfiltrate secrets, disable logging, alter DLP rules, or route traffic through unapproved channels.
Incident response playbook
- Contain the channel: revoke sessions, disable the connector or agent, block the destination, and rotate exposed credentials.
- Preserve relevant logs, policy versions, retrieved-resource identifiers, approvals, and endpoint evidence.
- Determine what data was entered, retrieved, generated, or exported and which identities were involved.
- Remove unauthorized access, correct source permissions, purge affected caches or embeddings where appropriate, and notify required parties.
- Test the fix with a replay or controlled case, then update rules, training, and access reviews.
Choosing controls by deployment
The right enforcement mix depends on where the model runs and how sensitive the data is.
| Deployment | Priority controls | Typical limitation |
|---|---|---|
| Consumer SaaS | Endpoint DLP, browser destination policy, identity restrictions, user training, proxy or CASB inspection | Limited tenant-level retention, logging, and permission integration |
| Enterprise SaaS | Tenant permissions, sensitivity labels, conditional access, provider audit logs, DLP for uploads and exports | Feature and regional availability varies by edition |
| Custom application with retrieval | Source-permission checks at retrieval, scoped service identity, data minimization, application and export DLP | Engineering must maintain authorization synchronization and cache invalidation |
| Agent with write actions | Tool allowlists, separate credentials, deterministic broker, approval gates, rate and value limits, detailed logs | More friction and operational cost; unsafe defaults can cause real-world changes |
Evaluate each design on authorization granularity, ability to preserve source permissions, enforcement location, logging quality, override handling, geographic and regulatory fit, user friction, and operating cost. A control that is powerful but routinely bypassed is weaker than a narrowly scoped control that users can follow.
Governance, training, and regulatory alignment
Publish an approved-use standard that names allowed services, prohibited data classes, retention expectations, review contacts, and consequences for bypassing controls. Train staff to recognize that a prompt, attachment, conversation history, or generated answer can contain sensitive information. Provide an approved workflow for legitimate exceptions so employees do not move work to unsanctioned tools.
Federal guidance in 15 U.S.C. Chapter 119 supports risk-based limits on particular generative-AI services, safeguards for lower-risk use, staff training, suitable vendor terms, confidentiality and data-protection measures, and controls against misuse. Map those principles to your sector’s privacy, security, records, and localization obligations; the exact legal requirements depend on your jurisdiction and data types.
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- First 30 days: inventory applications, identities, repositories, tools, and export paths; block clearly prohibited destinations; require MFA for administrators; and fix the most serious overshared repositories.
- Next phase: implement user-aware retrieval checks, sensitivity labels, prompt and upload DLP, conditional access, and centralized audit logging for approved applications.
- Agent readiness: create per-agent identities, tool allowlists, read/write separation, approval gates, schema validation, and rate or value limits before enabling write actions.
- Continuous operation: review entitlements and connectors on a schedule, test DLP with realistic prompts and files, investigate anomalies, measure override rates, and rehearse incident response.
Common failure modes and fixes
“The model is private, so all data is safe”
Privacy terms do not correct excessive repository permissions or an over-privileged agent. Enforce authorization at retrieval and tool boundaries and minimize the data sent.
“The firewall blocks AI leakage”
Network controls miss personal devices, alternate networks, encrypted or novel destinations, and offline copy. Add endpoint, identity, application, and data controls.
“The index already has permissions”
Indexes and embeddings become stale. Check the live user-to-resource relationship at query time or maintain a demonstrably synchronized authorization layer.
“We log every prompt forever”
Over-collection creates another sensitive repository. Define retention, access, redaction, and legal requirements; retain investigation context without storing unnecessary raw content.
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Expect legitimate exceptions. Require a reason, apply stronger controls to higher classifications, alert on repeated overrides, and review whether a rule is too broad or too weak.
Quick Recap
Control checklist
- Every AI application, model, connector, agent, and workload identity has an owner and documented purpose.
- Retrieval, tool calls, and exports evaluate the requesting user’s current permissions.
- Privileged access is phishing-resistant, time-bound, approved, and periodically reviewed.
- Prompts, uploads, retrieved context, outputs, clipboard, downloads, and destinations have DLP coverage.
- Sensitive values are masked or tokenized when raw data is unnecessary.
- Agents use allowlisted tools, narrow scopes, separate read/write credentials, and approval gates.
- Logs cover policy decisions, retrievals, tool calls, exports, overrides, and administrative changes.
- Anomaly detection and an exercised response plan can revoke access, rotate secrets, preserve evidence, and remediate caches.
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