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Control an AI agent as you would an untrusted service account: give it a unique identity, deny access by default, and grant only the data and tools needed for the current task. Enforce those decisions in your application, API gateway, policy service, operating system, or tool executor—not in a prompt. Keep credentials short-lived, start with read-only access, require independent approval for irreversible or high-impact actions, and log every decision and tool call. Treat user text, files, websites, emails, retrieval results, tool responses, and the model’s own output as untrusted input.
This design limits both ordinary mistakes and prompt-injection attacks. It also gives an operator a practical way to pause the agent, revoke credentials, investigate what happened, and roll back changes.
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An agent is controlled when an external enforcement layer decides what it may read, which tools it may call, what arguments it may use, and when a human must approve the next step. The model can propose a plan, but it cannot grant itself authority. The U.S. Department of Energy’s GEAR guidance puts the principle plainly: “An AI system cannot own a decision or accept risk.” A named person or organization must remain accountable for consequential actions.
Think in terms of a principal, not a chatbot. The principal has an identity, an owner, a delegated user or session, a set of permissions, a purpose, and an expiry time. Every request should be evaluated against all of those attributes before data is released or an operation is executed.
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- Identity: which agent instance, service account, or workload is asking?
- Delegation: which human user, ticket, workflow, or business process authorized the task?
- Scope: which tenant, records, fields, files, destinations, and operations are in bounds?
- Risk: is the request read-only, reversible, externally visible, costly, or destructive?
- Time: when does the authorization start and expire?
- Evidence: can you reconstruct the request, decision, approval, and result later?
Permission should be checked again at each boundary. A safe decision to retrieve a document does not automatically authorize emailing its contents, writing them into a database, or passing them to another agent.
Why is a system prompt not an access-control mechanism?
A prompt is an instruction to a probabilistic model, not a security boundary. The model may misunderstand it, follow a later conflicting instruction, or be manipulated by text inside a document, web page, email, or tool response. As GEAR notes, “A prompt such as ‘never delete files’ is not an access control.” If the execution layer would allow the delete, a malicious or simply confused model can eventually request it.
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Use prompts for behavior and explanation, but enforce authority in code and policy. The tool gateway should reject a call even if the model asks for it confidently. It should also reject malformed arguments, attempts to escape a tenant or path, and requests whose approval has expired. If the policy service, approval check, or audit sink is unavailable, fail closed rather than silently proceeding.
How do I inventory what an agent can reach?
Start with a complete register before changing permissions. Include components that are easy to overlook, such as retrieval indexes, cached conversation state, browser sessions, shell tools, plugins, background jobs, and downstream services that exchange tokens.
Register the technical components
- Agent applications, model endpoints, orchestrators, sub-agents, and scheduled triggers.
- Tools and operations, including read, write, delete, send, purchase, install, deploy, and administrative functions.
- Data stores, files, queues, vector indexes, secrets managers, telemetry systems, and memory stores.
- Network destinations, web proxies, DNS resolvers, cloud accounts, and third-party APIs.
- Human approval channels, identity providers, ticket systems, and break-glass procedures.
Assign accountability
Name a technical owner for each component, a data owner for each source, and a person accountable for consequential actions. Record who can change the policy and who can revoke the agent. Ownership should survive staff changes; do not leave it attached only to a developer’s personal account.
Classify both data and operations
Mark data by sensitivity and operations by reversibility. A customer’s public profile may be low sensitivity, while an authentication token, health record, payroll file, or unpublished acquisition plan is highly sensitive. Reading a record is different from exporting it; updating a draft is different from publishing it; deleting a backup is different from deleting a temporary file. These distinctions become policy conditions rather than informal guidance.
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Give each production agent or workflow a dedicated service or bot identity. Never let it inherit a developer’s broad personal credential. Bind “on behalf of” activity to the initiating user or session so an audit record can distinguish the human request from the agent execution.
| Identity model | Useful for | Controls to require | Main risk |
|---|---|---|---|
| Dedicated agent identity | Scheduled jobs and bounded service workflows | Separate credentials, narrow roles, owner, expiry, and revocation test | Shared identity can hide which user initiated a change |
| Delegated user identity | Interactive tasks that must honor a user’s existing data rights | Explicit delegation, audience restriction, session binding, and short token lifetime | The agent may inherit more of the user’s access than the task needs |
| Workload identity | Containerized or cloud-native agents | Attested workload, namespace or tenant binding, and policy checks at each service | A compromised workload can use every downstream permission unless scopes are reduced |
Use short-lived, task-scoped credentials issued just before execution. Make revocation observable and test it: invalidating a token should stop new calls, not merely mark an incident in a dashboard. Review effective permissions in downstream systems, because a narrow role in the agent framework may expand into administrator rights after token exchange or group membership.
How do I apply least privilege to an AI agent?
Least privilege must cover more than a list of tools. The OWASP AI Agent Security Cheat Sheet advises, “Grant agents the minimum tools required for their specific task.” In practice, scope the data, operation, resource, field, tenant, network, rate, cost, retries, recursion, delegation depth, memory, and lifetime.
| Dimension | Practical restriction | Example decision |
|---|---|---|
| Tools | Allowlist exact functions; disable unused plugins and shell access | Permit ticket lookup, not arbitrary HTTP requests |
| Operations | Separate read, create, update, publish, send, and delete privileges | Allow draft creation but require approval to publish |
| Resources | Bind access to specific projects, folders, records, or tenants | Read one customer case, not the entire customer database |
| Fields | Mask or omit secrets and unnecessary personal data | Return account status without returning an access token |
| Network | Route egress through an allowlisted proxy or sandbox | Permit the payment API, deny arbitrary domains |
| Rate and cost | Set request, spend, retry, and token budgets | Stop after a bounded number of retries or a cost ceiling |
| Memory | Isolate sessions and tenants; set retention and write permissions | Prevent one customer’s conversation from entering another’s context |
| Time | Issue credentials and approvals that expire | Access ends when the ticket is closed or the session expires |
Keep secrets out of prompts, long-term memory, logs, shell history, and environment files. Use a secret manager or broker that returns a narrowly scoped, short-lived value to the tool that needs it. A model should receive a success or failure result, not the underlying credential, unless the task genuinely requires the secret and the exposure is explicitly accepted.
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Start read-only, then add one capability at a time
- Run the workflow against synthetic or redacted data.
- Grant read-only access to the smallest useful resource set.
- Observe actual calls and remove unused permissions.
- Add one write operation with validation and an approval gate.
- Test revocation, expiry, and failure behavior before expanding scope.
Do not treat a broad role with an instruction to “use only these files” as equivalent to a narrow role. The role must make the forbidden action impossible at the enforcement point.
Where should authorization be enforced?
Put a policy decision point between the model and every tool or data source. This can be an API gateway, service mesh, application service, tool proxy, operating-system sandbox, or a combination. The model proposes a typed call; the gateway authenticates the caller, normalizes arguments, evaluates policy, and either executes the call or returns a denial.
Evaluate the complete request
A useful decision includes the agent identity, delegated user, tenant, audience, resource, operation, normalized parameters, risk class, approval state, time, and current session. Normalize paths, URLs, identifiers, and quantities before comparison so that alternate encodings cannot bypass an allowlist. Reject unknown fields instead of passing them through to a downstream API.
Separate planning from execution
The agent may produce a plan containing proposed calls. A separate executor validates each call immediately before execution. This separation supports independent checks, makes approvals bindable to exact actions, and limits the damage if the model’s plan is altered after approval. OWASP summarizes the pattern as, “Separate decision-making from execution.”
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If policy lookup, identity verification, approval validation, or audit recording fails, deny the action. Return a structured reason such as missing scope, expired approval, disallowed destination, or malformed argument. Do not reveal secrets or internal policy details in a denial that will be fed back into an untrusted model.
Which actions require human approval?
Require a human decision before an agent deletes or overwrites data, sends an external message, makes a purchase, changes permissions, installs software, deploys to production, resets a system, or accesses especially sensitive records. The Canadian Centre for Cyber Security warns that an erroneous or unexpected agent decision could cause significant damage, including deletion of important data.
Bind approval to the exact action
An approval such as “let this agent continue” is too broad. Create a short-lived approval artifact containing:
- the agent identity and delegated user;
- the normalized operation and all parameters;
- the exact target, tenant, destination, quantity, and affected records;
- the approver’s identity and authentication event;
- creation time, expiry time, and a unique nonce;
- the policy version and risk classification.
At execution, recompute a canonical representation of the proposed action and compare it with the approved artifact. Reject changes to the target, amount, destination, or arguments. Use replay protection and idempotency keys where possible so a repeated message cannot repeat a payment or deletion.
Use step-up authentication for critical decisions
For high-risk actions, require phishing-resistant authentication in addition to the normal session. A FIDO2 security key uses public-key cryptography to authenticate the approver without sharing a reusable password. It strengthens the human approval step, but it does not decide whether the agent is authorized; the backend policy engine, allowlists, and audit trail still have to permit the action.
Show a reviewer what will really happen
The approval screen should display the normalized operation, target, records or recipients, data that will leave the system, estimated cost, expiration, and the reason the agent requested it. Avoid displaying only the model’s natural-language summary, which may omit a parameter hidden in a structured call.
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Can prompt injection make an agent access private files?
Prompt injection can persuade an agent to request actions it should not perform, especially when external content is mixed with trusted instructions. A document may contain text telling the agent to reveal secrets; a web page may ask it to upload internal files; a tool response may include a fake system message. These are trust-boundary failures, not merely bad wording.
Treat every content source as untrusted
- User messages, uploaded documents, web pages, emails, and retrieved passages are data, not policy.
- Tool responses and tool descriptions can be compromised or misleading.
- Sub-agent messages and model-generated plans are untrusted until validated.
- Never let content redefine the allowed tools, identity, tenant, or approval requirement.
Use structural separation and typed interfaces
Keep trusted policy in a separate channel or data structure from retrieved content. Pass model output through a strict schema that permits only known operation names and typed arguments. Use parameterized queries, safe rendering, destination-specific validation, and output filters at downstream sinks. A string that looks like an SQL fragment, shell command, URL, or HTML should never be concatenated into an executable instruction without validation.
Test direct and indirect attacks
Red-team with harmless test data and instrumented tools. Try instructions in user text, documents, images with extracted text, web pages, emails, API responses, and sub-agent outputs. Confirm that the gateway denies unauthorized calls, that secrets are not returned in errors, and that the event is visible in the audit stream. Test both a direct request such as “send this file” and an indirect request hidden in a retrieved document.
How do I stop an agent from seeing secrets?
Minimize the context before it reaches the model. Retrieve only the records and fields required for the current step, redact personal data where possible, and tokenize identifiers that do not need to be human-readable. Keep tenant and session boundaries explicit in retrieval filters and memory keys.
Protect data in context and transit
- Use field-level filtering before retrieval results enter the prompt.
- Return references or masked values instead of raw credentials and payment details.
- Encrypt service-to-service traffic and restrict which workloads can call the retrieval layer.
- Set retention limits for prompts, completions, caches, embeddings, and traces.
- Prevent model providers, plugins, and observability tools from receiving data they do not need.
Protect memory
Separate short-term task state from durable memory. A task should not automatically write customer data into a long-lived profile or shared vector index. Require an explicit, policy-checked operation to persist information, record its owner and retention period, and provide deletion and correction workflows.
Protect logs and telemetry
Redact or tokenize credentials and personal data before logging. Keep a structured, tamper-evident workflow log outside the agent’s control. Restrict who can read traces, because a fully verbose trace can become a copy of every secret the agent handled.
What should an AI-agent audit log contain?
Log enough to reconstruct the workflow without copying sensitive payloads indiscriminately. At minimum record:
- agent and workload identity;
- delegated user, session, tenant, and correlation ID;
- request and policy versions;
- normalized tool name, operation, target, and arguments or a protected hash;
- policy decision, denial reason, and risk classification;
- approval artifact ID, approver, authentication method, and expiry;
- execution result, downstream request ID, latency, and error class;
- credential issuance, rotation, revocation, and kill-switch events.
Store logs in an external system with append-only or tamper-evident controls. Alert when an agent tries to alter or delete its own records. Correlation IDs should follow the request across gateways and downstream services so investigators can distinguish a model proposal from an executed operation.
How do I pause, revoke, and recover an agent?
Stopping an agent must be an operational capability, not a promise in a runbook. Provide a kill switch that prevents new tool calls, invalidate active tokens, disable scheduled triggers, and quarantine queued work. Test the sequence regularly under realistic load.
Use layered stop controls
- Pause: stop the orchestrator from issuing new steps and mark in-flight work for review.
- Revoke: invalidate task credentials, delegated tokens, refresh tokens, and session grants.
- Contain: block network egress, disable risky tools, and isolate the workload if compromise is suspected.
- Assess: use the correlation ID and external log to identify data reads, writes, messages, and approvals.
- Recover: rotate exposed credentials, restore known-good data, cancel queued external actions, and resume only after policy review.
Design reversible operations where possible. Use drafts, soft deletes, staging environments, transaction boundaries, and idempotency keys. For an irreversible action, require a separate approval and retain enough evidence to determine exactly what occurred.
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How should I monitor an agent for risky behavior?
Detection should focus on behavior relative to the approved task, not just model confidence. Alert on scope expansion, unusual tool choices, repeated denials, unexpected destinations, anomalous data volume, excessive retries or cost, growing delegation chains, and attempts to modify logs or policy.
Useful signals
- A read-only agent suddenly requests a write or administrative tool.
- Calls move from the approved tenant or project to another one.
- Retries, recursion depth, token use, or spend exceed the task budget.
- Large amounts of data are retrieved or sent externally.
- The agent repeatedly reformulates a denied request to find an allowed path.
- A sub-agent asks for broader credentials than its parent task received.
Combine automated alerts with periodic human review of sampled traces. A denial is not necessarily an attack, but repeated denials can reveal a prompt-injection attempt, a broken workflow, or an overly broad plan. Tune policies from observed need rather than granting broad access to silence alerts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is a safe end-to-end execution flow?
The following sequence keeps authority outside the model and creates a checkable record at every step.
- The user or scheduler creates a task with a purpose, tenant, resource scope, and deadline.
- The orchestrator authenticates the requester and obtains a short-lived agent credential bound to that task.
- The model proposes a plan using only the tools exposed for the task.
- The executor validates the plan against a typed schema and normalizes every argument.
- The policy service evaluates identity, delegation, resource, operation, parameters, risk, network destination, budgets, and approval state.
- If approval is required, the system presents the canonical action to an authenticated reviewer and issues a single-use, expiring approval artifact.
- The tool gateway rechecks policy and approval immediately before execution.
- The gateway executes with a scoped credential, captures the result, and records a structured event.
- The orchestrator receives only the result permitted by the data-minimization policy and proposes the next step.
- At task completion or expiry, credentials and temporary memory are revoked or deleted, and the workflow is closed.
This flow also works when a model is replaced, a tool is updated, or a new retrieval source is added, because the enforcement point does not depend on the model’s wording.
How can teams compare different control designs?
Compare implementations on the properties that determine whether a control is real and enforceable.
| Comparison axis | Questions to ask |
|---|---|
| Enforcement location | Is the decision made in a gateway, service mesh, application, tool proxy, operating-system sandbox, or several layers? |
| Identity model | Is there a dedicated agent, delegated user, or workload identity, and can the initiating user be traced? |
| Permission granularity | Can policy limit a tool, operation, resource, field, tenant, destination, and quantity separately? |
| Credential lifetime | Are credentials static, rotating, or issued just in time, and has revocation been tested? |
| Approval binding | Does approval cover a generic prompt or the exact canonical action, target, arguments, expiry, and nonce? |
| Isolation | Are process, container, virtual machine, filesystem, memory, and network boundaries independent of the model? |
| Audit quality | Are events structured, correlated, protected from alteration, and stored outside the agent’s control? |
| Recovery | Can operators pause, revoke, rotate, roll back, prevent replay, and prove what happened? |
A sandbox is valuable but not sufficient. It can limit local files or processes while an authorized API call still exports sensitive data. Conversely, a precise API policy cannot protect against a vulnerable host that lets the agent escape its process. Layered controls should fail independently.
What are the most common implementation mistakes?
Relying on “do not” instructions
Failure: a system prompt says not to delete, send, or reveal secrets, but the tool accepts those operations anyway.
Fix: deny the operation in the gateway and require an exact approval artifact for any exception.
Giving the agent a human administrator account
Failure: one credential combines broad permissions with no reliable attribution.
Fix: create a dedicated identity, bind delegated user context, and reduce downstream roles to the task’s effective need.
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Allowing arbitrary URLs or shell commands
Failure: a prompt injection turns a general-purpose tool into a data-exfiltration path.
Fix: expose narrow, typed operations and route network access through an allowlisted proxy or sandbox.
Approving a plan but executing a different call
Failure: a reviewer sees a friendly summary while a later step changes the recipient, file, amount, or destination.
Fix: canonicalize arguments, bind approval to the exact action, and revalidate immediately before execution.
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Logging everything in plaintext
Failure: traces become a second secret store accessible to analysts, plugins, or the model.
Fix: redact before logging, limit retention, protect access, and store tamper-evident records externally.
Never testing revocation
Failure: a kill switch changes a dashboard flag while existing tokens continue to work.
Fix: run a revocation drill that proves active calls stop, queued work is contained, and credentials are rotated.
How do I audit and review permissions over time?
Perform a permission review whenever the model, tool, data source, workflow, network, or deployment environment changes. Recompute effective access from the identity provider through every downstream service; do not review only the role displayed in the agent framework.
- Remove tools and scopes that were not used or are no longer required.
- Compare approved actions with executed actions and investigate drift.
- Review denials, unusual egress, retries, costs, and delegation depth.
- Verify that data retention, memory writes, and log redaction still match policy.
- Exercise the pause, revoke, rollback, and credential-rotation procedures.
- Re-run prompt-injection tests after changes to retrieval, tool descriptions, or model versions.
Keep a change record that identifies the new model or tool, the affected policy, the reviewer, the test evidence, and the rollback plan. A permission that was safe for one workflow may be excessive after the workflow is reused in another tenant or connected to a more sensitive data source.
What is the practical baseline for a new agent?
For a first production deployment, use a dedicated identity; read-only, tenant-scoped access; a short credential lifetime; an allowlist of typed tools; a policy gateway that fails closed; isolated memory; redacted, tamper-evident logs; and a tested kill switch. Add human approval before any delete, overwrite, external send, purchase, permission change, software installation, production deployment, system reset, or access to specially sensitive records. Expand capability only after observing the narrow workflow and proving revocation and recovery.
This baseline does not make the model trustworthy. It makes trust unnecessary at the point where harm could occur. The model can remain useful for planning and interpretation while the surrounding system decides what is permitted, records why, and stops safely when conditions change.
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Can I let an AI agent use my own login session?
Avoid sharing a personal session cookie or administrator token. Use explicit delegation that records the user, audience, tenant, purpose, and expiry, and issue a separate credential whose permissions are narrower than the full interactive session.
How often should an agent’s permissions be reviewed?
Review them after every model, tool, data-source, workflow, or environment change, and after any security incident. Between changes, use log-driven reviews to remove unused scopes and investigate denials, unusual destinations, or budget overruns.
Does running an agent in a sandbox prevent data exfiltration?
No. A sandbox can restrict local files and processes, but an agent may still send data through an authorized network or API. Pair isolation with egress allowlists, field-level filtering, and policy checks on the destination and payload.
Is read-only access always safe for an AI agent?
Read-only access can still expose credentials, personal data, trade secrets, or enough information to enable fraud. Limit the records and fields returned, isolate tenants and memory, and monitor unusual volume and external-transfer attempts.
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