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Before an AI agent can send messages, modify records, run commands, or call external services, define exactly what it may read, what it may change, and which actions require a person’s approval. The reliable pattern is to inventory every capability, grant the smallest task-specific permission, enforce authorization in the tool or backend, and keep a human gate in front of high-impact or difficult-to-reverse operations. Treat webpages, emails, documents, and tool output as untrusted input because they can contain instructions designed to redirect the agent.
What an agent boundary actually controls
An AI agent is more than a chat interface. As NIST wrote in its August 5, 2025 article, “AI agents can perceive and take actions in environments; the leading AI agent paradigm today embeds general-purpose AI models into systems with software scaffolding that enable a model to manipulate tools to take actions beyond simple text output.” The boundary around that system determines the damage an error, compromise, or malicious input can cause.
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A useful permission description has at least five parts:
- Operation: the precise action, such as reading a record, creating a draft, changing a field, deleting an object, or sending a message.
- Resource: the files, records, accounts, repositories, services, or environments the operation can reach.
- Permission level: read-only, constrained write, or write access.
- Environment: trusted infrastructure or an environment that exposes public or user-controlled content.
- Approval condition: whether a person must authorize the exact proposed action before execution.
“The agent can use a browser” is therefore not a sufficient authorization statement. A browser that can only retrieve pages is materially different from one that can log in, upload files, submit forms, or make purchases.
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Use a permission model that reflects real risk
NIST’s tool-use guidance separates permission level from environment trust. Combining those dimensions with resource sensitivity, impact, reversibility, and approval produces a practical design matrix.
| Permission level | What it allows | Typical control expectation |
|---|---|---|
| Read-only | Retrieve or inspect information without changing the source | Limit the reachable resources and filter sensitive data before it enters the model |
| Constrained write | Create a narrowly defined change, such as a draft, ticket, or proposed record update | Restrict fields, destinations, and values; validate the proposed change before committing it |
| Write | Commit changes or trigger an external effect | Use the narrowest possible scope and require approval when impact is substantial or reversal is difficult |
| Environment | Security question | Design implication |
|---|---|---|
| Trusted | Are the data sources and execution systems controlled by your organization? | Continue to enforce least privilege; trust does not remove the need for authorization checks |
| Untrusted | Can public pages, customer content, email, documents, or other users influence what the agent sees? | Assume ingested content may contain hostile instructions and keep permissions independent of model interpretation |
A read-only agent in an untrusted environment can still leak confidential information if its resource scope is too broad. A write-capable agent in a trusted environment can still make an irreversible mistake. Evaluate both dimensions together rather than treating “internal” or “read-only” as a complete safety argument.
Step 1: Inventory every capability and side effect
Start with an inventory that is concrete enough for an engineer to test. Record direct tools and indirect paths exposed through connectors, shell access, browser automation, plugins, or APIs.
- List each tool and entry point. Include databases, file stores, ticketing systems, messaging, cloud consoles, code repositories, browsers, shells, and any connector that can call another service.
- List operations separately. “Database access” is too broad. Record actions such as select, insert, update, delete, schema change, export, or credential rotation as separate capabilities.
- Identify every reachable resource. Specify account, tenant, project, repository, folder, table, record class, or environment. Note whether a wildcard or inherited role expands the scope.
- Describe side effects. Mark whether the call reads data, creates a constrained change, sends information outside the system, publishes content, spends money, changes availability, or deletes data.
- Capture indirect access. A shell command may reach files or network services that are not listed as separate tools. A browser may expose authenticated sessions, downloads, uploads, and form submissions.
The inventory is the baseline for later tests. If a capability cannot be named and its effect described, it is not ready to be exposed to an autonomous workflow.
Step 2: Define the task boundary
Write the intended job in operational terms: what starts the task, what information it needs, what output it should produce, and where that output may go. Then remove anything outside that job.
Separate required access from convenience access
For each proposed tool, ask whether the task can succeed without it. Convenience access—such as a broad search index, an unrestricted shell, or an all-project API token—creates exposure without improving the defined outcome. OWASP’s Excessive Agency guidance identifies unnecessary functionality, permissions, and autonomy as avoidable risk.
Scope resources as narrowly as the task permits
Prefer one repository over an organization, one queue over every queue, and one folder over an entire drive. Restrict both the objects the agent can read and the destinations to which it can write. If the task spans multiple resources, enumerate them rather than granting a general role.
Choose the least powerful operation
If the agent only needs to prepare a response, provide a draft operation rather than send. If it needs to update a status, expose a status-transition function with an allowed set of values rather than unrestricted record editing. Narrow operations make authorization and auditing easier than a general-purpose tool.
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Step 3: Enforce authorization where tools execute
Prompts, system messages, and model-generated plans are not access-control mechanisms. The backend or tool execution layer must check every call independently of what the model says it is trying to do. OWASP’s AI security guidance recommends enforcing authorization at backend boundaries and limiting permissions rather than relying on prompts.
- Authenticate the calling workflow. Bind each tool request to an agent identity, user identity, service account, or execution run that can be audited.
- Authorize the exact operation. Check the operation, resource, parameters, destination, and current state. Do not authorize a broad tool and then trust the model to self-limit.
- Validate parameters. Enforce allowed fields, value ranges, file types, destinations, query shapes, and record transitions. Reject unexpected arguments before the downstream service sees them.
- Apply tenant and object boundaries. Verify that the requested object belongs to the permitted account, project, or workspace. Do not rely on an identifier supplied by the model to establish ownership.
- Return the minimum result. Filter secrets, unrelated records, and unnecessary personal data from tool responses. Read permission should not become unrestricted disclosure.
- Log the decision and result. Record who or what requested the action, the authorized scope, parameters after validation, approval evidence, execution result, and any denial.
A denied call should fail closed. Do not silently broaden the scope, substitute a more powerful tool, or ask the model to find another route around the policy.
Step 4: Design approval gates before deployment
Human approval is most valuable before a consequential action, not after an agent has already committed it. OWASP recommends approval for high-risk actions and describes approval routines for publishing workflows.
Actions that normally deserve approval
- Sending an external message or publishing content under a person’s or organization’s identity.
- Deleting, overwriting, or migrating data when recovery is uncertain or costly.
- Changing production configuration, access rights, credentials, billing, or service availability.
- Executing code or commands with broad filesystem, network, or infrastructure reach.
- Exporting confidential, regulated, or customer information to a new destination.
The correct gate is tied to the exact proposed operation. An approval such as “you may handle customer support” is too vague. A useful approval screen identifies the target, material inputs, destination, side effects, and whether the action can be reversed.
Make approval meaningful
- Pause before execution, after the agent has produced a concrete action proposal.
- Show the normalized parameters that the backend will execute, not only the agent’s natural-language summary.
- Identify data leaving the current trust boundary and the account or identity used to perform the action.
- Require an explicit approve or reject decision tied to that proposal and execution run.
- Expire stale approvals if the parameters, destination, or underlying record changes.
For low-impact, reversible actions, you may permit automatic execution within a constrained scope. The threshold should be set in advance, documented, and enforced by code rather than improvised by the model.
Step 5: Assume external content can try to hijack the agent
A webpage, email, document, ticket, code comment, or tool description can contain text that looks like an instruction. NIST’s CAISI technical blog, published January 17, 2025, describes this as agent hijacking through indirect prompt injection: instructions embedded in ingested data attempt to redirect the agent into unintended actions.
Do not solve this only by adding a longer system prompt. If the model follows hostile content, the tool layer must still prevent unauthorized reads, writes, exports, and external effects.
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- Keep browsing and document-reading tools read-only unless a separate, reviewed operation is needed.
- Separate content from control data. Treat text retrieved from the world as data to analyze, not as policy that can alter permissions.
- Prevent untrusted content from selecting arbitrary tools, destinations, or credentials.
- Require approval for actions whose justification originates in external content, especially publication, deletion, transfer, or financial activity.
- Return only the portion of a document or page needed for the task, reducing opportunities for hidden instructions to influence planning.
Test with hostile examples: a page that tells the agent to reveal its system prompt, an email that requests a password reset, a document that instructs it to upload files, and a tool response that proposes a new destination. The expected result is a denied or paused action, not merely a polite model refusal.
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Step 6: Compare configurations before granting access
When choosing between two designs, score both against the same questions:
- Permission: Is the operation read-only, constrained write, or write?
- Environment: Can public or user-controlled content influence the agent?
- Resource scope: Which exact files, records, accounts, or services are reachable?
- Impact: What could be exposed, changed, sent, published, or deleted?
- Reversibility: Can an operator reliably undo the result, and how quickly?
- Approval: Must a person authorize this exact high-impact call?
For every proposed grant, ask: “If the agent is manipulated or simply wrong, what is the worst plausible action available to it?” Reduce that worst case by narrowing resources, replacing general tools with constrained operations, moving the action behind approval, or removing the capability entirely.
Practical boundary patterns
Draft-first communications
Give the agent read access to the relevant conversation and a constrained operation that creates a draft in a specified mailbox or workspace. Keep sending separate, approval-gated, and limited to approved recipients. The agent can prepare useful work without possessing an unrestricted outbound channel.
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Constrained record updates
Expose a function such as “set ticket status” with an allowed status list and a project boundary. Do not expose arbitrary field updates if the task only needs a state transition. Require approval for closure, deletion, or changes that affect contractual or financial records.
Code assistance without production authority
Allow the agent to inspect a selected repository and propose a patch or test run in an isolated environment. Keep production deployment, secret access, and infrastructure changes outside the automatic path. A reviewer should approve the exact change and target environment.
Browser research with a hard stop
Permit navigation and retrieval from public pages while blocking uploads, purchases, account changes, and form submission. If a workflow genuinely needs a submission, create a separate operation that displays the destination and fields for human approval.
Verification, monitoring, and change control
Permissions are not finished when the initial configuration is deployed. Test the controls as an operator would experience them.
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- Negative tests: attempt another tenant, a disallowed field, an unapproved destination, an oversized query, and a destructive operation.
- Prompt-injection tests: place hostile instructions in pages, messages, documents, and tool responses and verify that authorization remains unchanged.
- Approval tests: change a parameter after approval and confirm that the old approval cannot be reused.
- Audit tests: verify that denials, approvals, tool inputs, outputs, and execution identities are recorded without storing unnecessary sensitive content.
Review the inventory whenever tools, connectors, roles, models, prompts, data sources, or execution environments change. Product-specific behavior can change as vendors retire or replace features; OpenAI’s “Safety in building agents” guidance, for example, has noted Agent Builder deprecation, so implementation instructions should be checked against current documentation rather than treated as permanent.
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Common failure modes and fixes
“The system prompt says not to delete anything.”
Why it fails: the model can misunderstand, ignore, or be redirected by hostile content.
Fix: remove delete authority or enforce a backend denial. If deletion is necessary, expose a narrowly scoped operation behind explicit approval.
“Read-only means safe.”
Why it fails: broad read access can expose secrets or personal data, and retrieved content can carry prompt-injection instructions.
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Fix: narrow the resource set, filter responses, and treat the environment as untrusted when outside users can influence content.
“One service account is simpler.”
Why it fails: a shared, powerful identity makes attribution difficult and expands the impact of a compromised run.
Fix: use task- and resource-specific identities or scopes, and log the initiating user and execution run.
“Approval once per session is enough.”
Why it fails: later calls may target different records, recipients, or destinations.
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“The agent can use a general shell because the task is small.”
Why it fails: shell access may reach files, credentials, network services, or destructive commands outside the stated task.
Fix: replace it with narrowly defined functions or an isolated environment with explicit filesystem and network restrictions.
Deployment checklist
- Every tool, operation, connector, and indirect access path is listed.
- Each operation has a named resource scope and side-effect description.
- Read, constrained-write, and write permissions are separated.
- Authorization is checked in the backend or tool executor for every call.
- Parameters, destinations, tenant boundaries, and returned data are validated and filtered.
- High-impact or hard-to-reverse actions pause for approval of the exact operation.
- Untrusted content cannot grant permissions or select arbitrary tools and destinations.
- Denied calls fail closed without an alternative escalation path.
- Positive, negative, injection, approval-expiry, and audit tests have passed.
- Logs support investigation while minimizing unnecessary sensitive data.
- The permission inventory has an owner and is reviewed after material system changes.
Sources and scope of this guidance
This process reflects NIST’s “Lessons Learned from the Consortium: Tool Use in Agent Systems” (August 5, 2025), OWASP’s “LLM06:2025 Excessive Agency,” the OWASP “AI Agent Security Cheat Sheet,” the OWASP AI Security and Privacy Guide’s “General Controls,” NIST CAISI’s “Strengthening AI Agent Hijacking Evaluations” (January 17, 2025), and OpenAI’s “Safety in building agents.” These materials provide security design guidance, not a compliance determination for a particular industry or jurisdiction. They also do not establish that a vendor implements every control by default; verify the behavior and current documentation of the tools you deploy.
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Should an agent ever receive unrestricted write access?
Only when the operation, resources, environment, impact, and recovery path have been deliberately assessed and the resulting risk is acceptable. Most workflows can reduce exposure by replacing unrestricted writes with constrained functions or approval-gated actions.
How should emergency automation be handled?
Define the emergency scope, identity, allowed operations, duration, approval authority, and audit requirements before an incident occurs. A temporary exception should expire automatically and be reviewed after use.
What should an approval record contain?
It should identify the execution run, initiating identity, exact operation, normalized parameters, target resources, destination, material side effects, approver, decision time, and whether any parameter changed before execution.
Does using a trusted internal data source remove prompt-injection risk?
No. Internal documents, tickets, and messages may still contain user-controlled or compromised content. Trust the source only as far as its access and integrity controls justify, and keep authorization independent of the text the agent reads.
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