Set an AI agent’s approval limits by evaluating each action’s potential harm, reversibility, data sensitivity, scope, external visibility, and financial or administrative effect. Remove capabilities the task does not need, give every remaining tool the narrowest possible identity and permissions, require a human decision before consequential actions, and enforce authorization in the application and downstream service—not in the model. Add logging, rate limits, stop controls, named owners, and scheduled reviews so the policy continues to work as the agent, tools, and operating context change.
There is no universal dollar threshold or approval matrix that fits every organization. NIST’s AI Risk Management Framework (AI RMF) is voluntary and is being revised; its Playbook explicitly says it is not a mandatory checklist. Treat the framework below as an operational way to apply risk-based governance, then adapt it to your risk tolerance, industry obligations, and deployment.
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What an approval limit should control
An approval limit is not simply “ask a person when the amount exceeds X.” It defines which actions an agent may perform autonomously, which require a human decision, and which are prohibited. The boundary should apply to the actual operation performed by a tool, the identity used to perform it, the resources it can reach, and the consequences if the operation is wrong or abused.
For example, an assistant that summarizes mail needs read access, not send access. If it prepares a reply, drafting can be autonomous while sending requires a person to inspect and approve the actual recipient, content, and attachments. The mailbox service must enforce the read-only or send scope; the model must not be trusted to decide that it is allowed to send.
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What the agent can do
Describe each tool as a concrete operation rather than a vague capability. “Manage customer records” hides many different risks. Separate operations such as:
- Read a record or search a collection.
- Create a draft or proposed change.
- Write or update a record.
- Send an external message or publish content.
- Delete data or cancel a transaction.
- Purchase, transfer funds, or commit to a contract.
- Change permissions, credentials, configuration, or other administrative settings.
- Trigger another workflow, agent, job, or physical process.
Why model judgment is insufficient
Agent inputs and outputs can be manipulated or simply wrong. OWASP identifies direct and indirect prompt injection, tool abuse, data exfiltration, goal hijacking, excessive autonomy, approval manipulation, cascading failures, and runaway compute costs as agent risks. A message, document, web page, or database field can contain instructions that redirect the agent. A model may also misunderstand a legitimate request. The authorization decision therefore belongs in deterministic application logic and the downstream service, with the model limited to proposing an operation.
Use a lifecycle risk process, not a one-time checklist
NIST AI RMF 1.0 organizes risk work into Govern, Map, Measure, and Manage, with governance cutting across the other functions. It calls for continuous, timely risk management throughout the AI system lifecycle, procedures aligned to organizational risk tolerance, and clearly defined human-AI oversight roles.
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- Before deployment: map tools, identities, data, dependencies, affected people, and failure consequences.
- During testing: measure whether the agent stays within its tool, resource, volume, and approval boundaries under normal and adversarial inputs.
- In production: manage exceptions, monitor outcomes, rate-limit activity, and preserve an audit trail.
- After change: reassess controls when the model, prompt, tool schema, credentials, data scope, workflow, or deployment context changes.
NIST’s Playbook offers voluntary suggestions rather than steps every organization must follow. Use it to structure responsibility and evidence, not as a claim that a particular tier or numeric cap is universally required.
Inventory actions, tools, identities, and resources
Start with an action inventory that a reviewer can understand without reading the model prompt. For every operation, record what it does, whose authority it uses, what it can read or alter, and whether it can trigger further actions.
| Inventory field | Question to answer | Example |
|---|---|---|
| Operation | What exact server-side action occurs? | Send an email to an external recipient |
| Tool and schema | What parameters can the agent supply? | Recipient, subject, body, attachments |
| Identity | Which user, service account, or role is used? | Mailbox service identity with send scope |
| Resources | Which records, accounts, systems, or environments are reachable? | One shared mailbox, not every mailbox |
| Data | What sensitive information can be read or changed? | Customer contact details and contracts |
| Downstream effects | Can it start another action or workflow? | Sending creates a ticket and notifies a customer |
| Recovery | Can the result be reversed, and how quickly? | Recall may be unavailable after external delivery |
Map indirect paths as well as direct ones. A tool that edits a ticket may trigger billing, provisioning, or notifications through automation. A “read” operation may expose secrets that enable a later action. Broad extensions and broad downstream identities create unnecessary agency; narrow the extension and its functions before trying to solve the risk with more approvals.
Classify actions by consequence
Use a set of consistent dimensions rather than one arbitrary amount. For each action, assess:
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- Potential impact: What happens if the operation is wrong, malicious, or repeated?
- Reversibility: Can the result be undone completely, and within what time?
- Sensitivity: Does it expose personal, confidential, regulated, credential, or commercially sensitive data?
- Scope: How many people, records, accounts, or systems can it affect in one call or sequence?
- External visibility: Does it communicate outside the organization or publish publicly?
- Financial or administrative effect: Does it spend money, alter entitlements, create a commitment, or change a control?
- Privilege expansion: Can it grant access, change a role, create credentials, or alter security settings?
- Propagation: Can it trigger more agents, jobs, messages, or transactions?
These dimensions are a practical synthesis of NIST and OWASP guidance, not a published universal classification. Your organization’s risk tolerance determines where the boundaries fall.
A workable action-class model
| Class | Typical characteristics | Possible policy |
|---|---|---|
| Low-impact assistance | Read-only, limited scope, low sensitivity, easy to check, no external effect | Autonomous operation within the task and resource scope; log the request and result |
| Controlled change | Creates or edits data, handles sensitive information, or affects an internal workflow | Permit only with scoped credentials and server-side validation; require review when impact or scope exceeds the approved task |
| Consequential action | External communication, publication, financial commitment, deletion, or broad business effect | Require a human approval before execution; show the concrete operation and affected resources |
| Restricted action | Irreversible harm, privilege change, credential handling, security-control modification, or uncontrolled propagation | Disallow for the agent, or route through a separately governed process with stronger controls |
The labels are examples, not standards. An internal edit involving regulated records may deserve the same approval treatment as an external publication. Conversely, a reversible change in a tightly isolated test environment may be lower risk than its production equivalent.
Set an autonomy boundary for every class
Write the boundary as an allow, approve, or deny rule tied to an operation and resource. Avoid policies such as “the agent may act unless it seems risky”; that leaves the model as the policy enforcement point.
Allow autonomous actions narrowly
Allow an operation without per-event approval only when its impact, scope, sensitivity, and propagation are understood and bounded. Conditions might include a read-only permission, a named project or mailbox, a fixed record set, no external recipients, and a maximum iteration or request rate. The application should reject calls that fall outside those conditions.
Require approval before execution
Use pre-execution approval for high-impact actions. OWASP specifically recommends human approval for high-impact operations, including posting social-media content. Approval should occur before the irreversible or externally visible step, not after the agent has already sent, deleted, purchased, or changed access.
Deny or isolate actions
Some operations should not be exposed to a general-purpose agent at all. Examples include changing identity-provider roles, issuing credentials, disabling security controls, deleting broad data sets, or invoking open-ended administrative extensions. If a business process genuinely needs one of these actions, isolate it behind a dedicated workflow, stronger identity, explicit change record, and separate authorization.
Make approval meaningful
A button labeled “Approve agent action” is weak if the reviewer cannot tell what will happen. Present the concrete operation before execution:
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- The exact tool and operation.
- The affected account, records, files, environment, or recipients.
- The proposed values, message, attachment, query, or change.
- The source of the data used to construct the action, where relevant.
- The expected consequence, including downstream triggers.
- Any uncertainty, policy exception, or unusual volume.
- Who requested the action and which identity will execute it.
Where possible, stage a draft or preview that is separate from the commit operation. A reviewer can edit the draft, narrow recipients, remove an attachment, or reject the action. Approval must be bound to the exact operation; do not let the agent silently alter the payload after approval or substitute a different resource.
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Store the request, displayed payload, reviewer identity, decision, timestamp, policy version, and execution result. If the action changes between review and execution, invalidate the approval and require a new decision. Define an expiry so an old approval cannot authorize a later, different context.
Enforce authorization outside the model
“Implement authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not.”
This OWASP recommendation is the core technical safeguard. The model can select a tool, but the application and downstream service must make the final authorization decision.
Constrain the tool layer
- Expose separate functions for read, draft, write, send, delete, purchase, and administrative operations.
- Use narrow parameter schemas with validated types, enumerations, length limits, and destination constraints.
- Reject unknown fields and unexpected URLs, recipients, file paths, or resource identifiers.
- Prevent a tool from accepting arbitrary code, unrestricted queries, or open-ended extension names when a fixed operation is sufficient.
- Require explicit linkage between a tool call and the user task or approval record.
Use scoped identities
Execute extensions in the user’s authorization context where feasible, and give downstream services the minimum permissions needed for the task. Scope by resource, operation, environment, and duration. A summarization agent should receive a read-only scope for the mailbox or repository it summarizes, not a general account token. Separate production from test credentials, and avoid sharing one broad service identity across unrelated agents.
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Check policy at the server
At the downstream boundary, validate the caller, resource, operation, approval state, data classification, rate, and current business rules. Re-check authorization immediately before the side effect. A client-side warning or model instruction is not a security control because a manipulated prompt, compromised application, or alternate caller can bypass it.
Bound volume and detect misuse
Least privilege limits what an agent can do; volume controls limit how much damage a permitted action can cause. OWASP describes monitoring and rate limits as damage-limiting strategies, not substitutes for prevention.
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Log the action path
Capture enough information to reconstruct an event without unnecessarily copying sensitive content:
- Agent, application, user, and downstream identity.
- Model and policy versions.
- Tool name, operation, resource, and approval outcome.
- Request and execution timestamps.
- Result status, error, retry count, and downstream transaction identifier.
- Rate, spend, iteration, token, or resource counters relevant to the workflow.
- Policy exceptions, denials, and emergency stops.
Protect logs from alteration and restrict access because they may contain sensitive requests or destinations. Define retention according to your security, privacy, and legal requirements.
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Use limits on requests per time period, total spend, number of iterations, records affected, recipients, concurrent jobs, or execution duration as appropriate. A cap should be tied to a known failure mode. For example, a mail agent might limit recipients and messages per run; a data-cleanup agent might limit records and require a preview; a purchasing workflow might stop after one approved transaction rather than allowing an autonomous loop.
Add stop and recovery controls
- Provide an operator-accessible kill switch that blocks new side effects.
- Make queued actions cancellable before dispatch.
- Use idempotency keys to prevent duplicate transactions during retries.
- Keep backups, version history, or compensating actions for changes that can be recovered.
- Escalate repeated denials, unusual destinations, rapid retries, or policy exceptions to a human.
Threats that approval limits must address
| Threat or failure mode | What can happen | Controls to apply |
|---|---|---|
| Indirect prompt injection | An email, document, web page, or record instructs the agent to ignore its task or exfiltrate data | Separate untrusted content from control instructions; minimize tools; enforce downstream authorization; require approval for external or sensitive actions |
| Tool abuse | A valid tool is called with an unexpected resource, parameter, or sequence | Narrow schemas, server-side validation, resource scoping, and detailed logs |
| Approval manipulation | The agent presents an incomplete or misleading summary to obtain consent | Show the exact payload, recipients, resources, identity, and consequence; bind approval to an immutable request |
| Excessive autonomy | The agent can perform actions unrelated to its task or continue without a stopping condition | Remove unused extensions, split operations, cap iterations and volume, and deny administrative functions |
| Cascading failure | One action triggers workflows, agents, or notifications that multiply the impact | Map downstream effects, add propagation limits, use idempotency, and require approval at the consequential boundary |
| Runaway cost or compute | Retries or loops consume unexpected resources | Set time, iteration, spend, and concurrency limits; alert on abnormal rates; stop on repeated failures |
Worked example: a mailbox summarization assistant
Suppose an assistant’s stated purpose is to summarize incoming mail and help prepare replies.
Remove unnecessary capability
For summarization, expose a read operation restricted to the designated mailbox and folders. Do not grant send, delete, forwarding, rule-management, or attachment-upload permissions merely because the same mailbox API supports them.
Separate drafting from sending
A draft operation can produce a reply in a review workspace without delivering it. The send operation should be separate and require a person to inspect the final recipient list, message body, and attachments. Approval must precede the downstream send request.
Defend against hostile content
An incoming email may contain an indirect prompt injection telling the assistant to forward confidential messages or reveal its instructions. Treat message content as untrusted data. The mail service should still reject forwarding or sending when the connection lacks that permission, regardless of what the model proposes.
Monitor and recover
Log the mailbox, message identifiers, draft and send calls, reviewer, decision, and delivery result. Set sensible message and recipient limits for a run, detect repeated failures, and provide an operator stop control. Review the policy if the assistant gains access to additional mailboxes, new automation, or a different model.
Assign responsibility and review triggers
Document who owns the policy, who approves high-impact actions, who monitors exceptions, and who can stop the system. Avoid assigning “human oversight” to an undefined group; name a role and an escalation path.
Minimum responsibility map
- Business owner: defines the task, acceptable outcomes, affected resources, and risk tolerance.
- Security or platform owner: implements tool, identity, policy, logging, and rate-limit controls.
- Approver: reviews consequential operations and can reject or modify them.
- Operator: watches alerts, handles incidents, and exercises the stop and recovery procedures.
- Change owner: triggers reassessment when models, tools, permissions, data, or context change.
Review after meaningful change
Reassess controls after a model or prompt change, a new tool or extension, a broader resource scope, a credential or identity change, an incident, repeated policy exceptions, a new integration, or a change from internal to external deployment. Also review periodically even when nothing obvious changed, because usage patterns and downstream systems evolve.
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Organizations commonly choose between broad autonomous operation, approval-heavy operation, and a tiered design. Compare them on the same risk and usability dimensions rather than selecting by convenience alone.
| Decision axis | Questions to ask |
|---|---|
| Potential harm and reversibility | What is the worst credible outcome, and can it be undone? |
| Scope and sensitivity | How many resources and people are affected, and what data is involved? |
| External or financial effect | Does the action publish, contact outsiders, spend money, create a commitment, or alter entitlements? |
| Tool and identity scope | Could the same task work with fewer functions, resources, or privileges? |
| Approval quality | Does the reviewer see the exact operation and approve before execution? |
| Auditability and recovery | Can you reconstruct, stop, reverse, or compensate for a bad action? |
| Operational friction | Will approval volume overwhelm reviewers and encourage unsafe workarounds? |
| Risk tolerance | What level of residual risk is acceptable for this organization and context? |
A tiered policy often balances speed and control: permit bounded read-only work, require review for changes and external communication, and exclude irreversible or privilege-changing operations. That is an organizational choice, not an NIST or OWASP-mandated scheme.
How to stop an AI agent from taking an unauthorized action
- Disable the side-effecting tool or revoke its downstream credential. This blocks execution even if the model continues to propose the action.
- Stop queued jobs and revoke pending approvals. Treat an approval as invalid when the policy, payload, identity, or context has changed.
- Preserve logs and transaction identifiers. Record what was requested, what executed, and which identity performed it.
- Contain downstream effects. Disable triggered workflows, rotate exposed credentials, isolate affected resources, and notify the responsible operator.
- Recover or compensate. Use version history, backups, cancellation, recalls, or manual correction where available.
- Investigate the boundary failure. Determine whether the cause was excess permission, an unvalidated parameter, prompt injection, missing approval, excessive volume, or an operational gap.
- Reassess before re-enabling. Narrow the tool or identity, add the missing server-side check, test adversarial inputs, and document the revised owner and review trigger.
A concise policy record you can maintain
For each agent and operation, keep a current record with these fields:
- Purpose and task scope.
- Allowed tools and explicitly prohibited tools.
- Resources, data classes, environments, and downstream identities.
- Autonomous, approval-required, and denied action classes.
- Approval payload, approver role, expiry, and audit fields.
- Rate, spend, iteration, concurrency, and affected-resource limits.
- Logging, alerting, stop, rollback, and incident contacts.
- Policy version, owner, last review, and reassessment triggers.
Keep the record close to the enforcement code and deployment configuration so a policy change cannot exist only in a document that the running system ignores.
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