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How can an AI agent expose company data?
An agent can expose information in more ways than returning it in a visible answer. Microsoft identifies outputs, logs, memory, and downstream actions as potential leakage paths. The risk depends on what information enters the agent’s context, what it can retain, and which tools can send or change data.
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- Visible responses: The agent may include sensitive information in an answer shown to a user who should not receive it.
- Logs and traces: Operational records can capture more than intended. Microsoft Agent Framework documentation warns that trace-level logging can capture full chat messages, and that sensitive-data telemetry can include messages, function calls, and results.
- Memory and session storage: Information retained between turns or saved in serialized sessions may be exposed to a later interaction or someone who gains access to that storage.
- Tool actions: An agent may transmit information through email, files, APIs, or other connected tools, or make a change that exposes data indirectly.
Prompt injection can contribute to any of these paths. Instructions intended to manipulate an agent may be embedded in websites, documents, emails, or tool results. OWASP identifies prompt injection, tool abuse, data exfiltration, memory poisoning, and sensitive-data exposure among agent risks. Treat content retrieved from outside the trusted instruction channel as data to assess, not as authority to change the agent’s rules.
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How to reduce the risk: a seven-step control plan
1. Map where data and actions flow
Before changing permissions, inventory each agent’s identity, model, tools, connected data stores, retrieval sources, memory, logs, and outbound destinations. Trace how sensitive information can enter the context and how a tool call could transmit, modify, or retain it. Include plugins, connectors, and data sources in the inventory: Microsoft treats these, along with models and tools, as security dependencies.
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Record which tasks the agent performs, what data each task needs, and which operations it can take. This map gives owners a basis for setting access limits and identifying exposure paths that a review of chat responses alone would miss.
2. Limit access and permissions to the task
Give an agent only the data, tools, and operations required for its current purpose. Prefer narrowly scoped resources and read-only permissions when the task does not require changes. Separate tools with different trust levels rather than giving one agent a broad set of capabilities, and use a unique, auditable identity for each agent.
Use deny-by-default behavior: access or actions should be unavailable unless explicitly authorized. Apply least privilege to data access and least action to operations. A useful design test is whether the agent can still complete its assigned task if a particular permission, data source, or tool is removed. If so, remove it.
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3. Treat retrieved content and tool arguments as untrusted
Keep trusted system or application instructions separate from retrieved documents, websites, emails, and tool results. The agent may need to summarize or analyze that content, but the content must not be able to grant itself permissions or override trusted rules.
Validate model-generated tool arguments as if they came from an untrusted caller. For tools that access files, databases, or commands, use allowlists, type and range checks, safe path resolution, and parameterized queries. Reject malformed or out-of-scope arguments before a tool executes; do not rely on the model to identify every malicious instruction or unsafe parameter.
4. Enforce authorization at the application boundary
A model instruction is not an authorization control. Before executing a tool call, have the application or a policy component independently verify the caller, permitted tool, target resource, parameters, and any required approval. The component that executes the action—not just the model that proposed it—must enforce those checks.
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Validate and filter outputs before displaying or transmitting them, including checks for confidential information. For sensitive or high-impact operations, fail closed if authorization, risk classification, approval, or required audit logging cannot be verified. A model’s statement that an action is safe is not a substitute for these checks.
5. Require specific approval for consequential actions
Put a human approval gate in front of actions such as sending communications, sharing sensitive information externally, making purchases, changing administrative settings, deleting data, or taking another high-impact or difficult-to-reverse action. OpenAI’s prompt-injection guidance recommends carefully reviewing consequential actions and the information being shared.
Make approval specific to the proposed action and its target: the reviewer should be able to see what will be sent or changed, where it will go, and which resource is affected. A generic “confirm” prompt is not authorization. The application should verify that approval applies to the exact action before allowing it to proceed.
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6. Minimize and protect context, memory, and logs
Pass only the information needed for the task. Classify data and apply handling rules so the system can distinguish information that must not be included in an answer, retained in memory, or sent to an external destination. Restrict retention and deletion, encrypt stored and transmitted data, and protect serialized session data from unauthorized access.
Review telemetry settings before deployment. Microsoft Agent Framework documentation says trace-level logging can capture full chat messages and that enabling sensitive-data telemetry can include full messages, function calls, and results; it advises against enabling these settings in production. Configure production logs to retain enough information to investigate behavior without routinely capturing unnecessary sensitive content.
7. Monitor behavior and govern changes
Keep auditable records sufficient to detect unusual tool use and investigate incidents, while avoiding unnecessary sensitive payloads. Monitor which tools an agent invokes, what operations it attempts, and whether its behavior departs from its assigned task or permissions.
Test for prompt injection, unsafe tool selection, and data leakage before deployment and as the system changes. Review changes to models, connectors, plugins, and data sources because each can alter the agent’s security dependencies. Assign owners for agent registration, review, expiration, and decommissioning so that unused or unmaintained agents do not remain active without oversight.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should happen when a control cannot be verified?
Do not let the agent proceed with a sensitive operation when the system cannot establish that the caller is authorized, the target and parameters are within scope, or required approval and logging are in place. Deny the action, avoid sending the sensitive content, and route the case for human review where appropriate. This fail-closed behavior limits the damage from missing policy checks, ambiguous requests, or unavailable approval systems.
Apply the same principle to uncertain tool inputs: reject arguments that fail validation rather than letting the model retry with broader access. The agent may still explain that it could not complete the task, but it should not bypass the boundary to provide a more convenient answer.
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How to judge whether the safeguards are working
Evaluate the complete data path, not only whether an agent’s final response looks appropriate. Test whether untrusted content can change its behavior, whether unauthorized tool calls are blocked at execution time, whether sensitive information appears in outputs or production telemetry, and whether approval is required for the intended high-impact actions.
Revisit those checks when permissions, tools, data sources, models, memory, or logging settings change. The objective is to make exposure harder, constrain the impact of mistakes, and detect abnormal behavior—not to claim that a prompt or filter makes leakage impossible.
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