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Do not judge an AI agent by its language ability or a vendor’s safety description. Judge the complete action path: the tools it can call, the identity and permissions those tools use, the environment and inputs it sees, the consequences of mistakes, how much autonomy it has, and whether an independent control can stop or undo an action.
Before launch, inventory every possible action, reduce the capability and privilege scope, classify high-impact or irreversible operations, require precise approval where needed, test normal and adversarial behavior, and document a go/no-go decision. Continue that process after launch because models, tools, permissions and operating conditions change.
What makes an AI agent risky?
An AI agent is more than a text generator. NIST’s National Cybersecurity Center of Excellence describes agents as “systems that have the capability for autonomous decision-making and taking action to operate with limited human supervision to achieve complex goals.” The risk therefore comes from the whole system, not from the model in isolation.
A useful risk description combines these factors:
- Capability: what functions, connectors, APIs, browser controls, computer controls, code execution paths or delegated agents are available.
- Privilege: which identity the downstream service sees and what that identity can read, change, send, delete or administer.
- Environment: whether the agent operates in a trusted internal system or can consume untrusted web pages, email, documents or user input.
- Impact: the privacy, security, financial, operational and reputational harm a mistaken action could cause.
- Autonomy: what can happen without a person, what requires review and how long an approval remains valid.
- Reliability and observability: how consistently the tool works, whether its effects can be seen, and whether activity is recorded well enough to investigate.
- Reversibility and statefulness: whether an action can be undone, compounds over time or changes persistent state.
NIST’s Consortium for Artificial Intelligence Safety Institute (CAISI) report, “Lessons Learned from the Consortium: Tool Use in Agent Systems” (August 5, 2025), treats these as complementary dimensions rather than a universal numerical score. There is no generally valid “safe risk number” that substitutes for deployment-specific judgment.
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Start with an action inventory
Write down every action the agent could take, including actions exposed indirectly through a connector or extension. Do not stop at the functions mentioned in the user interface.
Include hidden and delegated paths
- Reading files, mail, tickets, calendars, databases or source repositories.
- Creating, editing, moving or deleting records and files.
- Sending email, messages, social posts, support replies or other external communications.
- Calling internal or public APIs, placing orders, issuing refunds or moving money.
- Changing configuration, permissions, accounts, infrastructure or security settings.
- Browsing to URLs, downloading content, uploading data or following links.
- Executing code, shell commands, notebooks, scripts or database queries.
- Using saved credentials, tokens, cookies, browser sessions or delegated user access.
- Spawning sub-agents that can perform any of the actions above.
For each action, record the target system, the data it can affect, whether it reads or writes, the identity used, required parameters, possible harm, reversibility, expected reliability, available logs and the discretion the agent has to proceed without intervention.
Use a practical inventory table
| Action | State change | Identity and scope | Potential harm | Reversible? | Approval |
|---|---|---|---|---|---|
| Search internal knowledge base | Read-only | Requesting user; collection limited to assigned team | Confidentiality exposure if access checks fail | Usually yes | Normally no, but log access |
| Draft a customer reply | Creates a draft | Agent service account; no send permission | Incorrect or sensitive wording | Yes | Human review before sending |
| Send a customer reply | External communication | Named mailbox identity; recipient-specific scope | Privacy, legal and reputational harm | Usually no | Specific approval for the message and recipient |
| Delete a record | Destructive write | Deletion role limited to a defined dataset | Data loss and operational disruption | Depends on backup and retention | Human approval or separate workflow |
| Run a shell command | Potentially unrestricted | Execution identity and host privileges must be stated | Code execution, data loss or lateral movement | Often difficult | Do not expose by default; use a narrow function instead |
The inventory is a boundary document. If a function is not needed for the intended task, remove it rather than relying on a prompt telling the model not to use it.
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Reduce capability before reducing autonomy
Excess functionality creates risk even when each individual permission looks reasonable. A document connector that can read, edit, share and delete is substantially riskier than one that only retrieves specified files.
Prefer narrow, task-specific functions
- Expose “create a draft invoice” instead of an arbitrary database write.
- Expose “look up shipment status for this order” instead of unrestricted API access.
- Expose a vetted calculation function instead of unrestricted shell or code execution.
- Expose an allow-listed URL fetcher with size, protocol and domain limits instead of arbitrary URL retrieval.
- Separate reading from writing, and drafting from sending.
Remove unused tools, disabled features and broad fallback connectors from the agent’s runtime. A tool that is technically available can become reachable through an unexpected plan, a prompt injection or a future model update.
Separate proposal from execution
For consequential work, use two components: an agent that proposes an action and an independent executor that validates authorization and performs it. The model should not be the final authority on whether its own proposed action is permitted. The executor or downstream service must enforce authorization, validate parameters and reject requests outside policy.
Inspect identity and permissions at the downstream service
Ask what the receiving system actually sees, not what the agent claims it intends to do. A natural-language instruction such as “only update this project” does not constrain a token that can update every project.
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Apply least privilege and separation
- Use the requesting user’s security context when that is appropriate and technically enforceable.
- Grant only the scopes required for the defined task.
- Separate read, draft, approve and execute roles.
- Use separate identities for unrelated systems and environments.
- Keep production credentials separate from development and test credentials.
- Remove unused scopes and rotate or revoke credentials that are no longer needed.
- Prefer short-lived, action-scoped authorization over broad, long-lived tokens.
Check authorization again at the API, database, mail system or other downstream boundary. Prompt instructions, system messages and model-generated reasoning are not access controls.
Bind approval to the exact action
An approval should identify the actor, tool, target, parameters, approver, timestamp and expiry. “Approved to send messages” is too broad. “Approved for this agent to send this exact message to these recipients from this mailbox until 15:00 UTC” is materially more restrictive. The executor should reject any change to those fields and require a new approval.
Classify impact, reversibility and statefulness
Not all writes deserve the same treatment. Rate each action by plausible consequence, not by how easy it is to describe.
High-impact categories
- Deleting or overwriting data, especially when backups or retention cannot restore it quickly.
- Financial transactions, purchases, refunds, transfers or changes to billing.
- Administrative changes to accounts, permissions, infrastructure or security controls.
- External communications that create legal, contractual, customer or reputational commitments.
- Actions involving sensitive personal, health, financial, confidential or regulated information.
- Changes that can trigger many downstream jobs or compound over time.
OWASP’s “LLM06:2025 Excessive Agency” guidance recommends additional integrity controls for destructive, financial, administrative and externally visible actions. Treat an action as higher risk when it is difficult to undo, affects many records, leaves the organization’s boundary or can be repeated rapidly.
Ask four consequence questions
- What is the worst plausible outcome if the agent is wrong or manipulated?
- How many people, records, systems or dollars could be affected in one run?
- Can the effect be reversed completely, and how long would recovery take?
- Could a small error compound through retries, loops, scheduled runs or delegated agents?
Mark actions with severe, irreversible or compounding effects for stronger authorization, narrower limits and human review.
Account for untrusted inputs and prompt injection
An agent can encounter instructions in email, web pages, documents, tickets or retrieved content. Those instructions may be malicious or simply irrelevant to the approved task. OWASP describes an indirect prompt-injection scenario in which a malicious email manipulates an agent that has excessive mail permissions.
Test the trust boundary
- Label which sources are trusted policy and which are untrusted content.
- Prevent retrieved text from silently changing the agent’s authority or approval state.
- Constrain where links can lead and what data can be uploaded.
- Require confirmation before an untrusted source causes an external or destructive action.
- Test plausible multi-step deviations, not only obvious attack strings.
Examples include a document telling the agent to upload confidential files, a web page instructing it to ignore its task, or an email asking it to forward a mailbox archive. The test should verify that independent authorization rejects the action even if the model follows the embedded instruction.
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Design human oversight that actually constrains action
Human-in-the-loop is meaningful only when the reviewer can see what will happen and the system prevents execution until the decision is recorded.
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OWASP gives publishing a social-media post as an example of an action that should receive human approval. Apply the same principle to destructive, financial, administrative, sensitive-data and externally visible operations. A low-impact draft may proceed automatically; sending that draft may not.
Show the reviewer the complete action
- The requesting user and execution identity.
- The tool or API that will be called.
- The exact target, recipients and parameters.
- The data to be created, changed, disclosed or deleted.
- Expected side effects, limits and expiry time.
- What will happen if the tool fails or retries.
Do not ask a person to approve a vague plan while allowing the agent to fill in material details afterward. Any change to the approved actor, tool, target or parameters should invalidate the approval.
Assign ownership and escalation
Name the person or team responsible for each approval queue, define a response-time target, and specify who can suspend the agent. If no qualified reviewer is available, the default should be to hold the action, not to proceed automatically.
Make activity observable and limit its scale
Logs should let an investigator reconstruct what the agent attempted and what the system actually did. Record, where feasible:
- Agent and model version, session and requesting user.
- Tool name, target system, parameters and relevant input references.
- Identity and authorization scopes used.
- Approval decision, approver, timestamp and expiry.
- Tool response, downstream outcome, retries and errors.
- Data affected and links to resulting records.
Use alerts for unusual destinations, privilege changes, repeated failures, high-volume operations and actions outside normal hours. Add rate limits, quotas, transaction ceilings, recipient limits and concurrency limits. OWASP notes that logging, monitoring and rate limits can limit damage and reveal undesirable activity, but they do not replace preventive authorization.
Follow a go/no-go evaluation sequence
- Define the task and boundary. State the intended outcome, users, affected parties, systems, prohibited actions and risk tolerance. Identify who accepts residual risk.
- Inventory tools and actions. Include read and write operations, external communication, code execution, credentials, browser control and delegated agents. Mark persistent-state and out-of-environment effects.
- Map access and identity. Document the downstream identity, exact scopes, environment and separation options. Remove unused or overly broad functions.
- Rate consequence and reversibility. Assess privacy, security, financial, operational and reputational harm. Flag destructive, externally visible, irreversible and compounding actions.
- Set independent controls. Enforce authorization in the executor or downstream service. Add specific approval, parameter validation, short-lived authorization, quotas and rate limits where appropriate.
- Test before launch. Cover expected use, malformed inputs, tool failures, retries, adversarial content, prompt injection, unauthorized targets and plausible multi-step deviations.
- Operate with monitoring. Review logs, outcomes, near misses, approval overrides and rejected actions. Reassess after model, tool, permission, data or deployment changes.
- Document the decision. Record evidence, remaining risks, mitigations, owners, escalation contacts, rollback steps and conditions for expanding or reducing autonomy.
NIST AI RMF 1.0 (2023) organizes this work as Govern, Map, Measure and Manage. Governance sets responsibilities and risk tolerance; mapping documents purpose, users, context, benefits and harms; measurement evaluates trustworthiness with qualitative or quantitative methods; management prioritizes risks, monitors them and adjusts controls. The framework calls for testing before deployment and regularly during operation.
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Use a consistent comparison framework
When comparing two agents, permission designs or deployment configurations, score neither by marketing claims nor by a single aggregate number. Compare the same dimensions for each option.
| Dimension | Questions to ask | Lower-risk signals |
|---|---|---|
| Task capability | What can it call, and which functions are unnecessary? | Narrow, purpose-built functions; unused tools removed |
| Read/write and external access | Can it alter persistent state or leave the environment? | Read-only by default; separate draft and send paths |
| Privilege and identity | Which downstream identity and scopes are used? | Least privilege, user-context enforcement, short-lived scopes |
| Action severity | Could an error cause financial, privacy, operational or reputational harm? | High-impact actions isolated behind stronger controls |
| Reversibility and statefulness | Can effects be undone, and can they compound? | Tested rollback, backups, transaction limits and idempotency |
| Tool reliability | How often does the tool fail or produce ambiguous results? | Validated responses, bounded retries and safe failure behavior |
| Observability | Can you reconstruct who did what, where and why? | Complete event and downstream records with alerting |
| Autonomy | What happens without a person? | Explicit approval gates for consequential actions |
| Recovery and approval | Can execution be stopped and is approval action-specific? | Independent executor, expiry, revocation and tested rollback |
NIST’s CAISI report presents these dimensions as a candidate taxonomy, not a final universal standard. Use them to expose trade-offs and missing controls, not to imply a precise probability of harm.
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Risk changes when a model is updated, a connector gains a new function, permissions expand, data sources change or the agent moves from an internal test environment to the public internet. Re-run the evaluation when any of those conditions changes.
Monitor leading indicators
- Unexpected tool calls or destinations.
- Repeated approval requests for similar actions.
- High rejection, retry or timeout rates.
- Attempts to access data outside the declared task.
- Changes in action volume, recipients or transaction size.
- Near misses, user overrides and rollback events.
Prepare a stop and recovery procedure
- Revoke or disable the agent’s credentials and execution route.
- Stop queued jobs, scheduled runs and delegated agents.
- Preserve logs, approvals, prompts, tool responses and affected-record identifiers.
- Assess what changed and which identities or systems were touched.
- Restore data or reverse transactions using a tested procedure.
- Notify affected owners and update the boundary, permissions or approval policy before re-enabling.
A rate limit can reduce the number of affected records, but only a tested recovery plan tells you whether the underlying harm can be repaired.
Common failure modes and corrections
“The prompt says not to do that”
Failure: The model is trusted to enforce its own permission boundary.
Correction: Enforce authorization in the tool executor and downstream service; reject unauthorized targets regardless of the model’s explanation.
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Failure: The connector combines read, write, delete and send operations.
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Correction: Split capabilities and remove functions not required for the task.
“A human is in the loop”
Failure: The reviewer sees a general plan while the agent can change recipients, amounts or targets afterward.
Correction: Bind approval to exact parameters, identity, timestamp and expiry, then invalidate it when any material field changes.
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“We can investigate from logs later”
Failure: Monitoring is used as a substitute for prevention.
Correction: Add least privilege, validation, approval gates and limits; use logs and alerts to detect residual risk and support recovery.
“Testing worked in the sandbox”
Failure: Tests omit untrusted inputs, tool failures, retries and production identities.
Correction: Test the real trust boundary, adversarial content, unauthorized requests and plausible multi-step deviations before enabling production actions.
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- Every tool, extension, API, browser control, code path and delegated agent is inventoried.
- Unneeded functions and broad fallback access are removed.
- Downstream identities and exact scopes are documented.
- Read, draft, approve and execute operations are separated where practical.
- Destructive, financial, administrative, sensitive-data and external actions are classified as high impact.
- Independent authorization rejects actions outside policy.
- Human approval is required where consequence warrants it and is bound to exact action details.
- Untrusted content and indirect prompt injection are included in testing.
- Logs capture actor, tool, target, parameters, authorization, approval and outcome.
- Rate limits, quotas, transaction ceilings and retry limits are configured.
- Rollback, credential revocation and incident ownership are documented and tested.
- A named owner records the go/no-go decision and residual-risk acceptance.
- Reassessment triggers are defined for model, tool, permission, data and context changes.
What a defensible decision looks like
Approve autonomy only when the agent’s capability is narrower than the task requires, its downstream privileges are minimal, high-impact actions are independently authorized, untrusted inputs cannot silently expand its authority, activity is observable, and recovery has been tested. If any of those conditions is missing, reduce the scope: make the agent read-only, require drafts instead of sends, add a human gate, isolate the environment, lower transaction limits or postpone launch.
The NIST CAISI workshop held in January 2025 included approximately 140 experts. That figure describes workshop participation; it is not a prevalence rate, safety guarantee or estimate of agent failures. The practical conclusion is more modest and more useful: evaluate the deployment conditions and controls around an agent, then keep measuring and managing risk as those conditions evolve.
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