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Secure an AI agent as if it were an untrusted software operator: give it a separate identity, the smallest possible permissions, isolated execution, validated tool inputs, and a human checkpoint before high-impact actions. Keep secrets out of prompts and agent configuration, minimize sensitive data at every handoff, and make every call auditable and reversible.
This checklist is for agents that can read documents, call APIs, execute code, update records, send messages, or operate across trust boundaries. It covers the controls to design before deployment and the operating procedures needed after launch.
1. Define the agent’s authority before connecting any tool
Write down what the agent is allowed to do, for whom, against which targets, and under what conditions. “The agent can use the CRM” is not an authorization policy. A useful policy names the initiating user or service identity, the action, the target resource, the data scope, and any approval requirement.
Create an authority record
- Owner: a named team or person accountable for the agent and its credentials.
- Purpose: the business task and the outcomes the agent may pursue.
- Allowed tools: exact functions or endpoints, not a broad SDK or shell.
- Allowed targets: specific projects, folders, tables, queues, or tenant IDs.
- Data scope: the fields and records required for the task.
- Action class: read, create, modify, delete, communicate, deploy, or administer.
- Approval rule: which actions need fresh human confirmation.
- Stop and recovery procedure: who can disable the agent and revoke access.
Start from deny and add exceptions. A tool that is not explicitly allowed should be unavailable, rather than merely discouraged in a prompt.
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2. Protect credentials and secrets
Never paste API keys, passwords, private certificates, database connection strings, recovery codes, or session tokens into a prompt. Microsoft warns: “Even in a private chat session, credentials in prompts are a security risk and may appear in logs.” Prompt content can be retained by application logs, tracing systems, model providers, browser history, or support exports.
Use an identity per agent
Give each deployed agent its own service identity. Do not share a human administrator account or a single credential across several agents. Separate development, test, and production identities so a test run cannot reach production systems by accident.
Issue scoped, short-lived access
Prefer tokens that are limited to the exact tool, resource, operation, and time window required for one task. Keep read-only and write-capable identities separate. If an agent only retrieves invoices, its identity should not also be able to approve payments or alter accounting records.
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Store, rotate, and revoke centrally
- Keep secrets in a managed secrets or identity system, not in prompts, chat transcripts, shell history, source control, or unrestricted agent configuration.
- Inject a credential only at the tool boundary that needs it; do not expose it to the model’s context.
- Rotate credentials on a defined schedule and immediately after suspected exposure.
- Revoke tokens and remove stale permissions when an agent is retired, replaced, or changes ownership.
- Log credential issuance, use, rotation, and revocation without recording secret values.
A model should receive a success or failure result from a tool, not the secret used to produce that result.
3. Map every data flow and classify sensitivity
Make an inventory before choosing a model or memory design. Trace data from the user’s message through chat history, memory, retrieval, model requests, tool arguments, tool responses, state changes, and logs. A secret or personal record can be copied at each hop, so a secure model endpoint does not make an insecure surrounding system safe.
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Classify what can move where
| Data location | Security questions | Useful control |
|---|---|---|
| User input and chat history | Can users submit credentials, regulated data, or another tenant’s information? | Input filtering, redaction, retention limits, and clear warnings against submitting secrets. |
| Memory and retrieval indexes | Who can retrieve a record later, and can tenant boundaries be crossed? | Per-tenant authorization, document-level access checks, encryption, and deletion workflows. |
| Model requests | Which fields leave the application, and are they needed for the answer? | Data minimization, pseudonymization, provider controls, and restricted retention. |
| Tool arguments and responses | Can a model-generated value address an unintended account, file, or endpoint? | Schema validation, allowlists, downstream authorization, and response filtering. |
| Logs and traces | Could debugging capture passwords, personal data, or confidential documents? | Field-level redaction, access controls, retention limits, and separate security logs. |
Minimize personally identifiable information, financial data, credentials, and confidential records sent to models or tools. Microsoft’s guidance says not to use real customer names, email addresses, or usage data as example inputs. Use synthetic or masked values for demonstrations and tests.
4. Treat prompts, retrieved content, and tool output as untrusted
External content can contain instructions designed to redirect the agent. This includes web pages, email bodies, uploaded files, retrieved documents, user-supplied text, tool responses, and messages from another agent. NIST describes agent hijacking as a failure to separate trusted internal instructions from untrusted external data.
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- Keep system and developer instructions under application control; do not let retrieved text replace them.
- Label external content as data and pass it through a separate context field when the framework supports that distinction.
- Assume a document can say “ignore previous rules,” request a secret, or propose an unsafe tool call.
- Require the application to enforce authorization; a model’s statement that an action is permitted is not proof of permission.
Microsoft Agent Safety guidance puts the implementation rule plainly: “Treat LLM-provided arguments as untrusted input, similar to user input in a web API.” Validate type, length, format, range, encoding, and allowed values before invoking a tool.
Defend retrieval and browsing paths
Apply access checks before retrieval, not only after the model receives a result. Filter documents by the initiating user and tenant, strip active content where practical, and prevent retrieved instructions from being copied into privileged system messages. For browsing agents, restrict destinations and inspect redirects, downloads, and form submissions.
5. Apply least privilege to every tool call
Tool access is an authorization problem, not a prompting problem. Expose narrow functions instead of a general-purpose shell, unrestricted database connection, or broad cloud role.
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Design narrow tool contracts
- Allow a specific operation such as
get_invoice_status, rather than arbitrary SQL. - Constrain paths to an approved directory and reject traversal, symlinks, and unexpected file types.
- Allowlist network destinations, methods, ports, and content types.
- Limit query rows, result size, batch size, and execution time.
- Return only the fields needed for the next decision.
Authorization should bind together the initiating user or agent, requested action, target, and scope. Re-check authorization downstream even when the request came through an agent gateway. A read-only extension must not inherit UPDATE, INSERT, or DELETE rights.
Use a preflight decision
Before execution, a policy layer should normalize the arguments, resolve the target resource, check the identity’s permissions, classify the action’s risk, and decide whether approval is required. If any check fails, return a safe denial and do not offer the model an alternate privileged path.
6. Gate consequential actions with fresh approval
Require a human confirmation immediately before sending messages, making purchases or payments, deleting data, deploying code, changing permissions, performing bulk updates, or taking another irreversible or high-impact action. Approval should describe the actual operation, not a vague “continue?” prompt.
What a useful approval screen shows
- The initiating user and agent identity.
- The exact tool and operation.
- The target account, record set, environment, or destination.
- The material arguments and affected data volume.
- What will happen, what cannot be undone, and any financial or external impact.
- The time limit for approval and the policy that triggered it.
Use stronger review when an action crosses a trust boundary, accesses sensitive data, affects many records, or changes security settings. Do not treat a previous conversation message as standing approval for a later action; obtain fresh confirmation after the final arguments are known.
7. Sandbox execution and limit the blast radius
If an agent can run code, process files, or execute queries, isolate that work from the host and from production. A sandbox should have a restricted filesystem, minimal operating-system privileges, controlled network egress, and a defined resource budget.
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Set operational limits
- Cap the number of model steps and tool calls per task.
- Set timeouts for tools, loops, subprocesses, and network requests.
- Limit query rows, downloaded bytes, file size, concurrency, and spend.
- Disable outbound network access by default; allow only required destinations.
- Separate temporary workspaces and destroy them after the task.
- Use independent production identities and prohibit test credentials from production environments.
Resource limits are security controls as well as reliability controls: they reduce the damage from a prompt-injection loop, runaway automation, compromised dependency, or mistaken bulk operation.
8. Build complete auditability
Log enough to reconstruct what happened without storing the secrets and sensitive payloads you are trying to protect. At minimum, record each tool call, the validated arguments or a safely redacted representation, the authorization decision, approval identity and time, state changes, result status, latency, and failure reason.
Make logs useful to an investigator
- Use a consistent task and trace identifier across model turns and tools.
- Record policy version, agent version, identity, tenant, and environment.
- Store hashes or references for large documents instead of copying their full contents into logs.
- Protect logs with separate access controls and retention rules.
- Alert on unusual destinations, denied-call spikes, privilege changes, bulk activity, and repeated approval failures.
Logging every argument verbatim can create a new data leak. Redact credentials, tokens, personal data, and confidential content while preserving the metadata needed to investigate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Test the failure paths, not only the happy path
Security testing should attempt to make the agent violate its policy. Use synthetic secrets and non-production accounts to test whether they appear in prompts, model responses, traces, memory, or logs.
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- A retrieved document tells the agent to ignore its system instructions.
- A tool response asks for a credential or suggests a new destination.
- A user requests access to another tenant’s record.
- Arguments contain path traversal, an invalid identifier, an oversized batch, or an unexpected URL scheme.
- A tool times out halfway through a write operation.
- The approval is denied, expires, or is revoked while the task is running.
- The agent retries a failed payment, deletion, or deployment.
- A credential is exposed and must be rotated while active tasks are still running.
Verify that the safe result is denial, containment, or a clearly bounded retry—not a fallback to a more powerful tool.
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10. Prepare revocation and incident response
Document how to disable the agent quickly. The runbook should identify the owner, on-call contact, identity provider, secret location, dependent tools, affected tenants, and evidence sources.
- Disable new tasks and pause queued work.
- Revoke the agent’s tokens and rotate any credential that may have been exposed.
- Remove stale permissions and block suspicious destinations or tool methods.
- Preserve relevant audit records while restricting access to them.
- Determine which data was read, changed, transmitted, or exposed.
- Restore or reverse changes using approved recovery procedures.
- Re-enable the agent only after a policy, code, and permission review.
Review permissions and logs regularly, not only after an incident. Ownership changes, new tools, model upgrades, and data-source changes can silently expand the agent’s effective authority.
11. Compare agent designs using security evidence
When selecting an architecture or platform, ask for demonstrable controls rather than relying on a “secure agent” label. Compare designs on these axes:
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|---|---|
| Credential isolation and revocation | Are secrets hidden from model context, scoped per agent, short-lived, rotatable, and revocable? |
| Per-tool and per-action authorization | Can policy bind identity, action, target, and scope, with downstream enforcement? |
| Data minimization and tenant isolation | Can fields, documents, memory, logs, and retrieval results be limited by tenant and purpose? |
| Prompt-injection resistance | Are external instructions separated from trusted control messages and tested adversarially? |
| Sandbox and egress controls | Can code, files, subprocesses, network destinations, and resource budgets be restricted? |
| Approval experience | Does a reviewer see exact targets, arguments, impact, and expiry before a high-risk action? |
| Observability | Can an investigator reconstruct calls, decisions, approvals, state changes, and failures without exposing secrets? |
| Operational cost and latency | What security checks, human gates, and logging overhead are incurred for each task? |
A design that cannot answer these questions should not receive production access to sensitive data or write-capable tools.
12. A practical pre-launch checklist
- Identity: The agent has a distinct owner and service identity; development and production are separated.
- Secrets: No credentials are in prompts, source code, shell history, logs, or unrestricted configuration.
- Data: Inputs, memory, retrieval, model requests, tool traffic, and logs are inventoried and minimized.
- Trust: External content is untrusted; system instructions remain application-controlled.
- Tools: Exact tools, commands, paths, data stores, and destinations are allowlisted.
- Authorization: Every call is checked for identity, action, target, scope, and tenant.
- Approval: High-impact or irreversible actions require fresh, informative human confirmation.
- Isolation: Code execution is sandboxed; egress and resource use are capped.
- Audit: Calls, decisions, approvals, changes, and failures are traceable with safe redaction.
- Recovery: Disable, revoke, rotate, investigate, and restore procedures have been exercised.
Frequently Asked Questions
Should an AI agent ever receive an API key in its prompt?
No. Keep credentials outside prompts and model context. Inject scoped access at the tool boundary, and rotate or revoke it through a managed identity or secrets system.
Is prompt-injection protection enough to secure an agent?
No. Treat prompt-injection defenses as one layer. Enforce authorization, validate arguments, restrict tools and network access, sandbox execution, require approval for high-impact actions, and maintain audit logs.
What should a human approve?
Require fresh confirmation for sending messages, purchases, payments, deletion, deployment, permission changes, bulk operations, and other irreversible or high-impact actions. Show the exact target, arguments, impact, and expiry.
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How often should agent permissions be reviewed?
Review them regularly and whenever ownership, tools, models, environments, or data sources change. Remove permissions that are no longer required and verify that revocation works.
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