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Keep live credentials and unnecessary personal or business data out of AI prompts. Use an organization-approved AI service and secret manager, restrict what each assistant or connected tool can access, and protect data throughout prompts, retrieval, memory, logs, and outputs. These controls reduce exposure; they do not make an AI workflow risk-free.
What not to send to AI tools
Do not paste API keys, passwords, connection strings, access tokens, or other live secrets into a prompt—even in a chat you believe is private. Microsoft’s Security and responsible AI for Windows development guidance warns that prompt content can appear in logs.
As Microsoft puts it: “Never paste API keys, passwords, or connection strings into a prompt.” For customer examples, substitute synthetic names, email addresses, and usage data. Before submitting proprietary code or internal business logic, check your organization’s policy and the terms for the specific AI service and account.
Unless the approved service and its applicable controls establish otherwise, treat a prompt as information disclosed to an external service. A “private chat” label alone does not establish that the content is not retained, logged, or otherwise accessible.
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Choose an AI environment that fits the data
Use an organization-approved AI environment for sensitive work, and verify the service’s current terms and settings for retention, tenant isolation, logging, and model training. These vary by service, account, plan, and configuration; “enterprise” is not a universal guarantee that data is handled identically across providers.
Compare services and configurations on the controls that affect your workflow:
- Data exposure: Which prompts, retrieved records, tool outputs, and logs leave your organization’s control?
- Retention and training: What information is retained, for how long, and whether customer data is used for training under the applicable terms and settings.
- Access boundaries: How are users, tenants, sessions, and connected systems separated? What permissions do connectors receive?
- Lifecycle coverage: Do controls apply to prompts, retrieval, memory, logs, outputs, and downstream actions?
- Approval and audit: Are sensitive data access and consequential tool calls reviewed, and are their actions recorded in usable audit trails?
Check current documentation for the exact service and account you plan to use rather than assuming a feature or protection applies to every offering.
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Keep credentials out of prompts, code, and system instructions
Store credentials in a secret manager or credential vault approved for your environment. Do not hardcode them in source code or place them in a system prompt. Microsoft documents PasswordVault for Windows application development; that example is specific to Windows, not a universal recommendation for every platform.
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Keep credentials and sensitive operational state outside model-visible context where possible. They should not appear in retrieved documents, connector responses, tool outputs, or logs unless there is a defined, necessary, and protected reason.
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Limit what assistants and connected tools can do
A basic chat interaction exposes the content entered into the service. An assistant connected to mail, repositories, retrieval indexes, or tools that can take action introduces more exposure points: it may read additional data or pass information to another system. Treat each connection and capability as a separate access decision.
- Give each agent, connector, and service identity only the permissions required for its task.
- Limit the data, functions, and actions available to each AI component; do not grant broad access merely for convenience.
- Require human approval when a tool accesses sensitive information or can make consequential changes.
- Use explicit boundaries for which sources an assistant may consult and which destinations it may write to.
Microsoft’s Agent Safety guidance covers approval for sensitive-data tools and secure session storage. Treat these as design controls to apply in the relevant implementation, not as proof that an agent is safe by default.
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Prompt injection can be direct or indirect. In an indirect attack, instructions are embedded in material an assistant is asked to process, such as a webpage, email, attachment, or document. The content may include hidden text, quoted email instructions, or embedded and obfuscated directions intended to influence the model.
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Microsoft’s Prompt Injection (Direct / Indirect) guidance supports a layered response:
- Treat user-supplied and retrieved content as data, not as authority to change the assistant’s rules or permissions.
- Constrain tools and permissions so that an influenced model cannot freely retrieve sensitive data or perform high-impact actions.
- Establish clear grounding boundaries and prepare or filter content where appropriate.
- Inspect outputs and monitor tool behavior for unexpected access or actions.
No single prompt wording or detector is a complete defense. Microsoft describes prompt defenses and DLP protections for its Copilot products in Microsoft Copilot prompt defense in depth; those product-specific measures are an additional layer, not a substitute for runtime safeguards.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect conversation history, memory, retrieval, and logs
Prompt text is only one part of an AI system’s data path. Sensitive information may also persist in outputs, conversation histories, retrieved snippets, vector stores and embeddings, caches, summaries, scratchpads, connector results, agent state, tool traces, and logs. These stores can expose data independently of whether a model memorizes it during training. Microsoft describes these disclosure surfaces in Sensitive Information Disclosure (Data Leak).
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Inventory which of these surfaces exist in your workflow, who can access them, and how long they retain information. Then minimize and isolate what is stored:
- Keep context short-lived and limited to the task; store only necessary fields and set retention limits.
- Separate data by user, session, task, agent, and retrieval scope so one workflow cannot casually expose another’s context.
- Apply classification and data loss prevention (DLP) checks before sensitive data is persisted to memory or shown to users or agents.
- Scan prompts, outputs, memory writes and reads, retrieved context, and tool outputs; block, redact, or require approval according to policy.
- Monitor memory access and tool activity for suspicious extraction, cross-user access, or sensitive markers, while avoiding unnecessary collection of sensitive prompt content in logs.
Validate outputs before using or forwarding them
AI-generated output is not automatically safe to display or pass to another system. Microsoft’s Output Safety and Downstream Handling guidance recommends output validation, secret scanning, redaction, and downstream controls.
Quick Recap
- Validate outputs against an expected schema and allow-list permitted values where practical.
- Scan for secrets and regulated or otherwise restricted data before displaying output or forwarding it to another tool.
- Redact or block material that violates policy, or route it for review.
- Require confirmation before high-risk downstream actions, even if the AI-generated request appears well formed.
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