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Keep live credentials and unnecessary personal or proprietary data out of AI prompts. Use an approved secrets manager for credentials, restrict each AI tool and agent to the access it needs, and protect information across prompts, retrieval, memory, logs, outputs, and connected actions. These measures reduce exposure; they do not make an AI workflow risk-free.
What not to send to AI tools
Microsoft Learn puts the rule plainly: “Never paste API keys, passwords, or connection strings into a prompt.” The same caution applies to access tokens and other live secrets. A private chat is not, by itself, a safe place for credentials; prompt content may appear in logs. See Microsoft’s security guidance for Windows development.
- Use synthetic names, email addresses, and usage data in customer examples instead of real records.
- Check your organization’s policy before sharing proprietary code or internal business logic with an external AI service.
- Assume a prompt discloses its contents to an external service unless the service and account you are using have approved controls for that information.
For sensitive work, use an organization-approved AI environment and verify the terms that apply to your specific service, account, and settings—including retention, logging, tenant isolation, and model training. Enterprise branding alone does not establish how those controls work.
Store credentials outside prompts and code
Credentials belong in an approved credential vault or secrets manager, not in source code, retrieved documents, or system instructions. Microsoft documents PasswordVault for Windows applications; that example is platform-specific, so use the credential-management mechanism approved for your environment. See Microsoft’s credential-handling guidance.
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A system prompt is not an authorization boundary or a secret store. OWASP’s 2025 guidance on system prompt leakage likewise warns against treating prompt contents as secrets. Enforce access checks in the application and tool layer, and keep tokens and sensitive operational state outside model-visible context wherever possible.
Limit what connected tools and agents can do
A chat assistant that can only respond to a prompt has a different exposure profile from an agent connected to mail, repositories, retrieval indexes, or tools that can change systems. Each connector, agent, and service identity should have only the data access and actions needed for its task.
- Scope identities and permissions narrowly; separate access by user, session, task, and agent where appropriate.
- Restrict which records, functions, and downstream actions a model can reach.
- Require human approval when a tool accesses sensitive information or can make consequential changes.
- Protect session state and avoid returning tokens or other secrets in tool results.
These controls align with Microsoft’s agent safety guidance. They reduce the impact of misuse or compromise, but do not guarantee that an agent will behave safely.
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Protect memory, retrieval, and logs—not just the prompt
Sensitive information can persist or reappear outside the visible conversation. Inventory every place an AI workflow can store or pass content:
- Prompts, responses, conversation history, and summaries
- Retrieved snippets, retrieval indexes, vector stores, and embeddings
- Caches, scratchpads, agent state, and connector results
- Tool traces, monitoring data, and application logs
Microsoft’s guidance on sensitive information disclosure highlights these broader data surfaces. Information stored in them can be exposed independently of whether a model memorizes training data.
Keep only necessary fields, prefer short-lived context, set retention limits, and isolate stored data by user, session, task, agent, and retrieval scope. Apply access controls to reads as well as writes. Monitor prompt and output events, memory operations, retrievals, and tool activity for sensitive markers, suspicious extraction, or cross-user access—but avoid logging more sensitive prompt content than monitoring requires.
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Treat webpages, emails, and retrieved files as untrusted
Content an AI reads may contain instructions designed to influence its behavior. In an indirect prompt-injection attack, the instructions can be embedded in a webpage, email, attachment, or document, including in hidden or obfuscated text. A model asked to summarize a file may encounter text that tries to redirect it or trigger an action.
Microsoft describes these risks in its guidance on direct and indirect prompt injection. Treat user-supplied and retrieved material as data, not as authority to change permissions or override policy. Defense should come from layers: constrain tools and access, establish clear boundaries for retrieved context, prepare or filter content where appropriate, inspect outputs, and monitor behavior. No single instruction in a prompt or detector is a complete defense.
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Classification and data-loss prevention (DLP) should cover more than the text a user types. Where your environment supports it, inspect prompts, retrieved context, memory writes and reads, tool outputs, and generated responses. Depending on policy, block sensitive content, redact it, or require approval before it is stored or passed onward.
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Microsoft documents prompt defenses and DLP protections for Microsoft Copilot; those are product-specific capabilities, not a guarantee that other AI services offer the same coverage. Check the feature scope and terms for the service, plan, and configuration you actually use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Validate output before using or forwarding it
Do not treat generated text or tool output as safe merely because an AI produced it. Before showing a response to a user, writing it to memory, or passing it to another tool:
- Validate it against an expected schema and allow-listed values.
- Scan for credentials, regulated data, and other restricted information; redact or block according to policy.
- Require confirmation before high-risk downstream actions.
Microsoft’s output safety and downstream handling guidance describes these safeguards. They are especially important in agentic workflows, where an output may become an input to an action rather than just text a person reads.
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Evaluate an AI service before using it with sensitive data
Compare services and configurations using the controls that matter to your workflow, rather than assuming that all enterprise AI offerings behave alike.
| What to evaluate | Questions to verify |
|---|---|
| Data exposure | Which prompts, retrieved records, tool outputs, and logs leave your organization’s control? |
| Retention and training | What does the applicable service, plan, and configuration retain, and may customer data be used for model training? |
| Access boundaries | Can you scope identities and connector permissions and isolate data by tenant, user, session, or task? |
| Lifecycle coverage | Do controls inspect prompts, retrieval, memory, logs, outputs, and downstream handoffs? |
| Approval and audit | Can sensitive access and consequential tool calls require review, and do they produce usable audit records? |
Verify current terms and feature scope for the service you choose. Retention, logging, training, tenant, licensing, and DLP provisions can vary by provider, plan, and settings.
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