Before an AI agent can access organizational accounts, data, tools, or infrastructure, require a named owner and identity, narrowly scoped permissions, protected credentials, an isolated execution environment, verified software and tool sources, independent authorization checks, approval for consequential actions, and monitoring and security tests. These controls limit what an agent can reach if it misunderstands a task, follows malicious instructions, or is compromised. Evaluate the entire deployment—not just the model—because the connected tools, orchestration layer, and runtime shape the consequences of a failure.
Why an agent needs more than a trustworthy model
An AI agent can do more than produce text: depending on its configuration, it may retrieve files, call services, change records, run code, or communicate with other systems. That makes installation a decision about authority. The key question is not only whether the model usually behaves as intended, but what it is technically able to do when it does not.
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Anthropic’s response to a NIST request for information describes four layers to consider: the model, tools, orchestration harness, and execution environment. That is the respondent’s framing, not an official NIST finding, but it is a useful way to locate controls. The model interprets instructions; tools expose capabilities; the harness coordinates work; and the runtime determines access to files, networks, and credentials. A weakness in one layer can affect the whole deployment. Anthropic’s RFI response
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Controls to require before installation
1. Assign an owner, identity, purpose, and authority
Document the accountable person or team, the agent’s approved task, the users or services it acts for, the data it may read, and the actions it may take. Give the agent an identifiable workload or agent identity rather than hiding its activity under a broad shared administrator account. Make the authority specific enough to review: “summarize approved support tickets” is more useful than “help the support team.”
Define the credential lifecycle before connection: who issues credentials, how scopes are selected, where secrets are stored, how credentials are rotated, and how access is revoked when the deployment changes or is retired. Logs should make it possible to distinguish the agent’s activity from a human’s and establish which user context authorized a request.
NIST’s NCCoE concept paper announced February 5, 2026, identifies agent identification, authentication, authorization, credential management, auditing, and binding agent actions to human authorization as questions for deployments. It is a concept paper seeking input, not a finalized agent-control standard. NIST’s announcement
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Grant only the data, functions, and downstream permissions necessary for the approved task. Separate read access from write access where possible. Prefer a narrow, purpose-built operation over an unrestricted shell, broad database connection, or arbitrary URL-fetching tool. If the task only needs to look up a record, the agent should not also be able to delete records or alter account permissions.
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Enforce authorization in the system receiving each request, using the relevant user or workload context. Do not rely on the model to remember or self-police its limits. OWASP describes excessive functionality, excessive permissions, and excessive autonomy as common causes of excessive agency. Its guidance is useful for evaluating permissions, but it is not a substitute for checking how a particular deployment enforces them. OWASP LLM06:2025 and the OWASP AI Agent Security Cheat Sheet
3. Contain the runtime and limit blast radius
Run local tool servers and code execution in a sandbox or otherwise restricted environment. Limit filesystem access to the paths the task requires, disable network access unless it is needed, and allowlist destinations when connectivity is necessary. Keep tools for sensitive payments, authentication, or personal data separate from general-purpose tools rather than making all capabilities available to every agent task.
Set practical rate limits, quotas, and timeouts so an unexpected loop or burst of tool calls cannot consume resources indefinitely. Determine what the runtime can access if the agent is manipulated: a sandbox is only meaningful if its actual filesystem, network, process, and credential boundaries are restricted. OWASP cautions that using standard input/output transport for an MCP server does not itself limit what the process can access. OWASP MCP Security Cheat Sheet
4. Verify the software, publisher, and tool definitions
Before installing a local component or connecting a remote tool, identify its source and publisher. Review the code or tool definitions, requested permissions, and dependencies; verify signatures or checksums when available; and pin versions and schemas so the deployed component does not silently change. Review the complete local command before running it, and obtain explicit consent before connecting a new server.
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Consent should not be a one-time blanket approval. Re-review the connection when tool definitions change, since a previously acceptable integration could gain new capabilities. Do not let a web page or other untrusted content trigger an installation. OWASP’s MCP guidance also flags package-name typosquatting and post-installation “rug pulls”—changes made after an initial review—as supply-chain concerns. OWASP MCP Security Cheat Sheet
5. Treat prompts, retrieved material, and tool output as untrusted
External documents, messages, web pages, and results returned by tools may include instructions designed to alter the agent’s behavior. Keep this material distinct from trusted control instructions. Validate tool inputs and outputs in trusted code, and independently authorize every follow-on operation rather than allowing returned text to grant authority.
Filtering can help flag suspicious content, but a filter cannot establish that everything it permits is safe. The important safeguard is to prevent untrusted content from changing permissions or bypassing the checks applied to actions. See the OWASP MCP Security Cheat Sheet and OWASP AI Agent Security Cheat Sheet.
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Require explicit human approval or policy-engine authorization before destructive, financial, data-sharing, permission-changing, externally visible, or otherwise high-impact actions. The approval screen should show the complete target and parameters—not just a short agent-generated summary—so the approver can understand what will happen.
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The component that executes the action must independently verify both the approval and the action’s authority. An agent-generated confirmation, a natural-language claim that approval was obtained, or a prior approval for a different action must not bypass that check. OWASP discusses excessive agency and MCP tool risks in its LLM06:2025 guidance and MCP Security Cheat Sheet.
7. Protect data and credentials
Minimize sensitive information sent to the agent in the first place. Apply the organization’s data classification and retention rules, and use encryption in transit and at rest as appropriate to the deployment. Keep credentials out of plain-text configuration and prevent tools that do not need a secret from accessing it.
For OAuth tokens, OWASP’s MCP guidance recommends OS-native secure credential storage and cautions against sharing credentials across MCP servers. Scope a credential to the specific integration and authority it needs instead of reusing a broad secret across unrelated tools. OWASP MCP Security Cheat Sheet
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Capture tool calls with timestamps, parameters, and user context, while redacting secrets and personal data from records. Route relevant events into the organization’s monitoring process. Alert on meaningful changes or anomalies, including new tools, unusual call frequency, or privileged queries.
Before release, test adversarial cases for prompt injection, unauthorized data access, permission escalation, and unintended high-impact actions. Repeat testing after significant changes to prompts, tools, memory, retrieval, policy, or model provider. Retain the tested configuration and version, abuse cases, expected outcomes, observed approval or denial behavior, and accepted residual risks. This turns a test result into evidence about a specific deployment rather than a general claim about a model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use a pre-installation decision sequence
Apply the controls as a release gate, not as a set of optional improvements to consider after an agent has been connected.
- Define the use case. Write down the task, intended users, data involved, and permitted actions. If the task cannot be stated narrowly, do not grant broad access to compensate for the ambiguity.
- Map the capabilities. List every tool, account, data source, local process, and network destination the agent can reach. Identify which capabilities are read-only and which can change state.
- Set identity and scopes. Assign a named owner and distinct workload identity; issue credentials with the minimum required scopes and document how they will be rotated and revoked.
- Inspect and contain components. Verify publishers, code or definitions, versions, dependencies, and permissions. Configure the sandbox, filesystem boundary, network restrictions, quotas, and timeouts before connecting sensitive systems.
- Exercise the authorization path. Test ordinary and high-impact tasks, including malicious instructions in retrieved content. Confirm that downstream checks deny out-of-scope requests and that consequential actions cannot execute without independently validated approval.
- Review evidence and accept risk. Preserve test cases, observed results, configuration details, logging coverage, and any residual risks with an accountable approver. Do not move to production if a critical access boundary or approval path cannot be demonstrated.
- Monitor and re-evaluate. Use the logs and alerts to detect changes in behavior or access. Reassess after changes to tools, permissions, prompts, memory, retrieval, policy, or model provider, and repeat relevant tests before relying on the modified deployment.
Compare candidate agents by evidence, not assurances
When multiple candidates can perform the same task, compare how each implements the controls below. Ask for configuration details or test evidence rather than treating a general security statement as proof. In particular, a claim that a model resists prompt injection does not replace execution boundaries, narrow permissions, or independent authorization.
| Evaluation area | What to establish |
|---|---|
| Capabilities and scopes | Can access be limited by tool, data source, action, and user context? Can read and write authority be separated? |
| Identity and credentials | Is there a distinct, attributable agent or workload identity? Are issuance, storage, scope, rotation, and revocation defined? |
| Isolation | Can filesystem, network, process, and user boundaries be restricted and verified? Are resource limits available? |
| Approval and policy | Can high-impact operations require approval, and does the execution path check approval and authority independently? |
| Tool provenance | Can the publisher, package, dependencies, definitions, and version be inspected and pinned? Are changes subject to renewed review? |
| Monitoring and audit | Are calls attributable and timestamped, can sensitive values be redacted, and can relevant anomalies trigger alerts? |
| Security-test evidence | Are adversarial cases, expected and observed outcomes, tested versions, and accepted residual risks documented? |
What current guidance does—and does not—establish
Agent-specific controls are still evolving. NIST’s February 5, 2026 announcement described a concept paper from its National Cybersecurity Center of Excellence and set April 2, 2026 as the public-comment deadline. NIST’s AI security overview, accessed October 13, 2026, describes single-agent and multi-agent control overlays as work in development. These sources support treating identity and authority as important design questions, but they do not establish a finished, universally applicable agent-control standard. NIST concept-paper announcement and NIST AI security overview
OWASP’s Agent Control Standard page, dated September 1, 2026, describes runtime middleware hooks for policy enforcement. It is a resource in an evolving landscape, not a binding law or a universal certification requirement. Use guidance to shape a deployment-specific risk decision; verify the actual controls in the candidate system. OWASP Agent Control Standard
No risk percentage or incident rate is established by these guidance materials, so a deployment decision should not be justified with an invented probability. Base it instead on the agent’s reachable systems, the impact of its possible actions, the effectiveness of boundaries and approvals, and evidence from tests of the configuration you intend to install.
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