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Start with the task and its access boundary
Before connecting a company system, define what the agent must do, which data classes it needs, which systems it may reach, and which operations it may perform. Default to denying access, then add only what the task requires. Where possible, separate read-only tools from tools that can write, send, delete, or administer.
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Scope access to the relevant user, tenant, resource, and task rather than giving the agent a broad standing identity. Do not rely on instructions such as “never access payroll” to enforce that boundary. Microsoft recommends deterministic controls that block prohibited actions regardless of model output, and AWS describes policy enforcement outside the reasoning loop. See Microsoft’s agent-risk guidance and AWS’s agentic AI security guidance.
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Give the agent a distinct identity and protect credentials
Use an identity that makes the agent’s activity distinguishable in access records and audit logs. Bind it to a least-privilege role and the approved tools; avoid sharing a person’s broad credentials or placing long-lived secrets in prompts, conversation history, or model-visible memory.
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Keep credentials in controlled storage and retrieve or broker them at runtime. The right mechanism depends on your environment: AWS documents credential brokering and secrets storage, while Google Cloud describes service identities and restricted API keys as deployment options. These are provider-specific examples, not a universal identity design. See AWS guidance and Google Cloud’s agent security architecture.
Enforce every tool call outside the model
Put a policy-enforcing handler or gateway between the model and company tools. For every invocation, check who is calling, what operation is requested, which resource is targeted, and whether the parameters are valid and permitted. A typed tool schema helps constrain model-generated arguments, but it does not replace authorization: the server must still reject an otherwise well-formed request the identity is not allowed to make.
Maintain an approved, reviewed, version-controlled inventory of tools and servers, with an owner and data classification for each. Treat remote Model Context Protocol (MCP) servers as third-party dependencies: assess their authentication, code or service, data handling, network path, and version changes. In a May 20, 2026 announcement, the NSA warned that agentic systems—especially those using MCP—can introduce risks involving dynamic tool invocation, implicit trust relationships, and context sharing, alongside the need for traditional controls such as authorization and input validation. Read the NSA announcement on MCP security.
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Treat retrieved content and tool results as untrusted
A prompt injection can be embedded in an email, document, web page, database record, or tool response—not just typed directly by a user. Keep retrieved content separate from trusted instructions. A document or tool result may supply information, but it must not be allowed to grant itself authority or change what the agent is permitted to do.
Validate and constrain parameters deterministically before execution. Input and output checks can help, but prompt filters are not a complete defense. OWASP identifies direct and indirect prompt injection, tool abuse, and data exfiltration among agent risks; its prompt-injection guidance is a useful reference for threat modeling.
Require review for high-impact actions
Use a human approval step for actions such as deleting data, changing permissions, sending an external message, or making financial or administrative changes. The review screen should show the proposed operation, its target, and enough relevant context for the reviewer to assess it. Provide a way to interrupt the workflow or stop the agent, and keep an audit trail of the request, decision, and result.
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Approval is a safeguard, not a guarantee: a reviewer can approve a harmful action by mistake or by over-trusting the agent. Combine review with narrow permissions and server-side authorization. AWS, Microsoft, and Google Cloud discuss these controls in their respective guidance: AWS, Microsoft, and Google Cloud.
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Send the model only the fields needed to complete the task. Classify sensitive information, govern retention, isolate session state and memory by user or tenant, and check outputs for disclosures before they reach people or downstream systems. Keep secrets outside model-visible context and retrieve them through a controlled runtime mechanism.
Application permissions are only part of the boundary. Consider network restrictions and resource limits as well. AWS’s architecture guidance describes provider-specific measures including encryption, session isolation, credential brokering, private network paths, rate limits, and logging. Apply the controls that fit your deployment rather than assuming one cloud’s reference design transfers unchanged.
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Monitor calls and make incidents containable
Record enough information to establish what happened without needlessly retaining secrets or sensitive payloads. Useful audit fields include the agent identity, selected tool, authorization result, invocation time, validated action, outcome, and any required approval.
Monitor for unusual call volumes, repeated authorization failures, validation errors, and unexpected chains of tool calls. Test that administrators can revoke access, pause or stop the agent, and investigate an incident. Rate limits can help contain runaway activity, but they should be part of security operations, not treated only as a way to control cost. AWS’s security guidance discusses observability and operational controls.
Test the boundary before rollout and after changes
Test both what information enters the agent and what the tool server permits at execution time. Include realistic attempts to misuse the integration, such as:
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- Instructions hidden in documents or tool responses that try to trigger an unauthorized action.
- Requests to retrieve records outside the agent’s assigned user, tenant, or task.
- Attempts to change access rights or use a read-only tool for a write operation.
- Malformed or unusually large tool arguments.
- Attempts to access another user’s session or memory.
- Unexpected combinations of individually low-privilege tools, or repeated calls that create a runaway loop.
Repeat relevant checks after changing the model, prompt, permissions, tool, or server. OWASP and AWS support adversarial validation and reviewed tool inventories, but the cited guidance does not establish one test protocol suitable for every organization. Build tests around your own data, tools, and impact scenarios.
Evaluate the integration, not just the vendor name
AWS, Microsoft, and Google Cloud provide implementation guidance for their own environments; those materials are not neutral product rankings or comparative performance tests. For any approach, assess whether it provides:
- Runtime authorization: Checks on each call, scoped by tool, operation, resource, user, and task.
- Identity and delegation: A distinct, auditable identity with safely scoped access.
- Data controls: Visibility into what reaches the model, memory, logs, and downstream tools, with session isolation and retention controls.
- Injection resilience: Untrusted treatment of retrieved content and tool results, plus parameter validation before execution.
- Human control: Previews, approvals, interruption, and audit records for consequential actions.
- Tool governance: Reviewed, owned, version-controlled integrations, with remote servers handled as external dependencies.
- Operational response: Detection of anomalous activity, limits on runaway calls, access revocation, and incident investigation.
Choose the design that can enforce these controls in your organization’s actual identity, network, and data environment. No cited architecture establishes that a particular deployment is compliant or prevents every attack.
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