To audit an AI agent properly, treat it as an identifiable actor rather than as an invisible feature inside an application. Record who operates it, what identity and credentials it uses, which tools and SaaS applications it can call, what data and actions those grants expose, and whether each activity can be tied back to a human or system that delegated authority. Then verify the same facts in identity-provider records, authorization controls, agent and tool logs, and the SaaS application’s own audit trail.
A defensible audit compares effective access with the agent’s actual job, tests authorization immediately before sensitive actions, examines token lifecycle and revocation, and exercises indirect-prompt-injection scenarios using untrusted documents, messages, web pages, and API responses. The workflow below is designed for security, IAM, IT, privacy, compliance, and SaaS administrators.
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What a defensible AI-agent access audit must establish
An inventory or screenshot is not enough. Your evidence should let another reviewer answer six questions for every deployed agent:
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- Who is the actor? The record identifies the agent, version, owner, operator or upstream system, runtime, and environment.
- Under whose authority does it act? You can follow the delegation from a person or service through the agent and identity provider to the principal seen by each SaaS application.
- What can it do? Effective permissions are mapped by tool, action, resource, data class, and read/write mode, including temporary and standing grants.
- How are credentials controlled? Each token, key, certificate, or delegated grant has a known issuer, audience, scope, lifetime, storage location, rotation process, and revocation path.
- Where is access enforced? Sensitive operations are checked in the execution path, outside model-generated decisions, with the exact actor, target, parameters, and current approval validated immediately before execution.
- Can activity be reconstructed? Agent-side and SaaS-side records can be joined to show the initiating request, policy decision, tool call, result, and human or system that delegated authority without putting secrets in logs.
NIST’s NCCoE concept paper on software and AI-agent identity frames these as open design questions, including how an agent identity should relate to software and organizational boundaries and how to bind an agent to a human for “on behalf of” work. It is a concept paper seeking feedback, not a final implementation standard. Read it at NIST’s agent identity and authorization concept paper.
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1. Set scope and inventory every agent
Start with a list that includes production deployments and the less visible systems that can still reach organizational accounts. Include pilots, locally run assistants, scheduled or orchestrated agents, embedded features, internal scripts that use a model, and third-party agent services. An agent belongs in scope whenever it can read, change, transmit, or authorize business data.
Collect a minimum record for each agent
| Inventory field | What to record | Why it matters |
|---|---|---|
| Identity | Name, immutable agent or run identifier, version, model/provider, environment, and runtime location | Separates one deployment from another and lets you match events after an upgrade or rollback |
| Ownership | Accountable business owner, technical owner, operator, and incident contact | Assigns responsibility for approvals, remediation, and emergency disablement |
| Purpose | Business task, expected inputs and outputs, operating schedule, and boundaries | Provides the baseline for deciding whether each permission is necessary |
| Delegation | Human requester, upstream application, workflow, or service that initiates work | Connects an automated action to the authority under which it began |
| Connections | SaaS applications, tenants, tools, API endpoints, webhooks, queues, and retrieval sources | Defines the attack and data-exposure surface |
| Credentials | OAuth grants, service accounts, API keys, certificates, workload identities, and token locations | Enables scope, lifetime, storage, and revocation checks |
| Data | Data classes, repositories, regions, retention requirements, and outbound destinations | Shows whether the agent can reach sensitive information or cross a policy boundary |
| Evidence | Identity-provider, agent, gateway, tool, approval, and SaaS audit-log locations with retention periods | Determines whether an investigation can prove what happened |
Ask each SaaS owner to identify the principal visible in that product: a service account, OAuth grant, app registration, integration identity, workload identity, or ordinary user. Record the product edition and tenant because permission granularity and event fields can vary by provider and edition. Do not assume that a catalog entry or vendor description reflects the effective access in your tenant.
Define boundaries before testing
Write down which tenants, environments, repositories, data classes, administrative functions, and external destinations are included. Note exclusions and the reason for each. A customer-support agent might be limited to one queue and approved fields, while a finance workflow could touch invoices but not payment administration. A boundary that cannot be stated in concrete resources and actions is too vague to audit.
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The question “Does my AI agent use my credentials or its own account?” has to be answered from observed transactions, not from the product’s marketing label. Run representative operations and follow the principal at every hop:
- Initiate a known task as a test operator or approved upstream service.
- Capture the request, run, workflow, or correlation ID generated by the agent.
- Inspect the identity-provider event and token claims: subject, issuer, audience, scopes, authentication method, issued-at time, expiry, and any workload or device binding.
- Inspect the tool gateway or API proxy to see which principal and policy decision it forwarded.
- Inspect the SaaS audit event and note whether it names the agent, the operator, a service account, an integration, or only a generic user.
- Join the records with timestamps and correlation identifiers, then document any break in the chain.
A shared personal login, generic service account, or token copied between agents makes attribution ambiguous. NIST explains that shared credentials create accountability gaps and that a bearer token or static API key does not, by itself, prove which person or service used it. Its August 27, 2026 article, “Back to the Future: Why Agentic AI Needs a Strong Identity Foundation,” describes unique agent identifiers, credentials, and entitlements bound to the identity of the operating user or system as the direction organizations need for trustworthy transactions.
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Document each link in the chain
| Link | Evidence to capture | Typical gap |
|---|---|---|
| Human or upstream system to agent | Requester, approval or workflow ID, task parameters, and start time | Only a free-text prompt is retained, with no durable requester identity |
| Agent to runtime | Agent ID, deployment, version, model/provider, and run ID | Runs from multiple versions appear under one name |
| Runtime to identity provider | Subject, client or workload identity, token type, scopes, audience, and expiry | A long-lived key is embedded in a configuration file |
| Identity provider to tool gateway | Policy decision, requested operation, resource, and authorization result | The gateway trusts a model-produced “approved” flag |
| Gateway to SaaS application | Forwarded principal, API call, SaaS request ID, and response status | The SaaS sees only a shared integration account |
If the SaaS log cannot distinguish the agent from a user or shared integration, state that limitation explicitly. It is an attribution gap, not proof that the agent acted alone or that a human performed the action.
3. Compare effective permissions with the job
Build a permission matrix for each agent and application. Use the permissions that are actually effective after group membership, inherited roles, OAuth scopes, resource policies, network controls, and conditional access are evaluated. Separate what the agent can read from what it can change, and separate standing access from grants issued only for a particular task.
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| Agent | SaaS/tool | Action | Resource or data scope | Mode | Business need and owner |
|---|---|---|---|---|---|
| Customer-support agent | Ticket API | Read conversation | Assigned queue only | Standing read | Required to draft replies; support systems owner |
| Customer-support agent | Ticket API | Send reply | Assigned queue; approved template | Human-approved write | Requires current reviewer approval |
| Finance workflow agent | Document store | Export file | Invoices for one legal entity | Temporary read/export | Required for a scheduled reconciliation; finance owner |
| Finance workflow agent | Payment system | Approve payment | Not granted | Denied | Outside the agent’s purpose; treasury owner |
For every grant, answer whether it can invite users, send messages, export files, alter configuration, approve transactions, administer an account, or call another tool. Mark wildcard, tenant-wide, or cross-environment access separately from a named project, folder, record type, or API operation.
Remove or explain excess access
Look for permissions that are unused, undocumented, inherited from a broad group, or unrelated to the stated task. Assign an owner and a removal or mitigation date. If a grant cannot yet be removed, record the compensating control, expiry date, monitoring requirement, and decision-maker. CISA’s May 1, 2026 joint-guidance announcement advises against broad or unrestricted agent access, particularly to sensitive data and critical systems; see CISA and partners’ guidance on adopting agentic AI services.
Use NIST SP 800-210, General Access Control Guidance for Cloud Systems, as a foundation for reviewing IaaS, PaaS, and SaaS controls. It was finalized July 31, 2020 and is not agent-specific, so validate each control against the particular SaaS provider’s implementation.
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4. Examine credentials and token lifecycles
For each connection, create a credential register. Include credentials held by the agent, its runtime, a gateway, an integration platform, and the SaaS application itself. A protocol choice does not remove the need to control scope and lifecycle.
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| Check | Questions | Evidence |
|---|---|---|
| Issuer and owner | Which identity system issued it, and who is accountable? | Application registration, service-account record, or workload-identity binding |
| Privilege scope | Which tools, operations, resources, and data classes are allowed? | OAuth scopes, role assignments, API policy, and resource ACLs |
| Audience | Can the credential be replayed at another API or tenant? | Audience claim, endpoint restriction, sender-constraining or equivalent control |
| Lifetime | When does it expire, and is access temporary or standing? | Token TTL, grant expiry, renewal rule, and task lease |
| Storage | Where is it stored, and who or what can read it? | Secret-store policy, runtime configuration, source scan, and log review |
| Rotation | How is it replaced without an outage? | Rotation schedule, automation, and last successful rotation |
| Revocation | How quickly can access be stopped for an agent, operator, or SaaS grant? | Revocation procedure and an observed test result |
| Monitoring | What detects unusual issuance, use, replay, or scope changes? | Identity-provider alerts, gateway telemetry, and SaaS events |
Check source repositories, local agent configuration, prompt templates, memory stores, crash dumps, and logs for embedded secrets. Confirm that offboarding an agent or operator revokes grants and does not leave an orphaned OAuth consent, service account, webhook, or API key.
NIST’s August 2026 identity article discusses OAuth 2.0, SPIFFE, JSON Web Tokens, X.509 credentials, and sender-constraining mechanisms as building blocks. It also emphasizes that dynamic, tightly scoped, audience-restricted credentials are prerequisites rather than automatic solutions. For implementation guidance on protecting identity tokens, access tokens, and assertions through key management, verification, lifecycle controls, and monitoring, consult NIST IR 8587, Protecting Tokens and Assertions from Forgery, Theft, and Misuse, finalized September 15, 2026. IR 8587 is general token-protection guidance, not an AI-agent-specific checklist.
5. Verify authorization at execution time
Inspect the component that actually invokes the SaaS API or other high-impact tool. The model’s plan, a prompt instruction, or a field saying “user confirmed” must not be the final authority. The execution component should evaluate the current actor, exact operation, target resource, parameters, policy context, and approval immediately before sending the request.
Test an approval-controlled operation
- Create a test task that requires approval, such as sending an external message, exporting a file, changing a permission, or initiating a financial action.
- Capture the proposed tool call, including actor, target, parameters, and a unique approval or nonce value.
- Approve the exact call through the designated control and record approver identity, approval time, expiry, and permitted use count.
- Change one material element, such as the recipient, file, amount, tenant, or operation, and replay the call.
- Attempt a second use of the original approval, an expired approval, an approval from the wrong actor, and a request when the approval service is unavailable.
- Confirm that only the original call succeeds, that approval is checked and consumed atomically, and that every denial is logged.
OWASP’s AI Agent Security Cheat Sheet recommends enforcing authorization in the execution component outside the agent context. Its guidance says a confirmation flag is insufficient unless the component verifies that approval belongs to the current actor and exact tool call, remains valid, has not been consumed, and is checked and consumed atomically before execution.
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Check for privilege self-expansion
Try instructions and tool responses that ask the agent to grant itself a role, create a more privileged token, disable logging, alter its policy, or call an administrative endpoint. The request should fail because the execution layer—not the model—controls authorization. Check that a retrieved document or API response cannot change the policy used for the next call.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Reconstruct what the agent did
Select representative read, write, administrative, and denied operations. For each, produce a timeline that starts with the initiating request and ends with the SaaS result. Include reads as well as mutations when the data is sensitive; an agent can expose confidential information without changing a record.
| Stage | Evidence fields |
|---|---|
| Initiation | Requester or upstream system, task ID, purpose, timestamp, and approved scope |
| Agent run | Agent and run IDs, version, model/provider, environment, and relevant retrieval configuration |
| Decision | Policy evaluated, requested action and resource, allow/deny result, approval ID, and policy version |
| Tool call | Tool name, endpoint, parameters or a protected hash of sensitive parameters, credential or principal reference, and correlation ID |
| SaaS event | Provider request ID, visible user or service principal, object, operation, status, source address or workload where available, and provider timestamp |
| Outcome | Response status, records affected, data destination, error, retry, and follow-up action |
Protect logs from alteration, restrict who can read them, define retention by risk and regulatory need, and ensure incident responders can retrieve them. Do not log passwords, access tokens, private keys, or unnecessary document contents in plaintext. Where sensitive values are needed to correlate events, use controlled references or cryptographic hashes and preserve the mapping separately.
The NIST concept paper asks how action and intent logs can be tamper-proof and verifiable and how actions can be bound back to human authorization. OWASP recommends retaining validation evidence such as agent version, model provider, tool policy, retrieval configuration, abuse cases, observed approvals and denials, and accepted residual risk. Treat the ability to join agent and SaaS records as a requirement to verify, not an assumption.
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Any document, email, ticket, web page, retrieved record, or API response that the agent reads may contain instructions crafted by someone other than the operator. Test whether that content can cause a permitted tool to be used for an unintended purpose.
| Abuse case | Test setup | Expected control |
|---|---|---|
| Document exfiltration | Place an instruction in a shared document telling the agent to attach unrelated files to an external address | Data scope and destination policy block the export; a high-risk action requires fresh approval |
| Ticket-to-message pivot | Put a request in an inbound ticket to send a message to a new recipient | The agent treats ticket text as untrusted data and cannot bypass recipient or template controls |
| API-response tool abuse | Return a malicious instruction in a tool response that asks for a second privileged call | Each call is independently authorized; response text cannot alter policy |
| Cross-tenant retrieval | Use a poisoned record that references another customer or workspace | Resource-level filtering denies the reference and logs the attempted access |
| Approval manipulation | Include text claiming that a manager already approved a destructive operation | Only a verifiable, current approval bound to the exact call is accepted |
| Memory poisoning | Insert a persistent instruction into agent memory or a retrieval index | Memory writes are scoped, reviewable, attributable, and removable |
Review input boundaries, sanitization, tool scopes, approval rules, memory handling, and sensitive-data logging. Run these tests under an authorized security program and record the exact input, model and agent version, tools exposed, policy result, and observed side effects. OWASP identifies direct and indirect prompt injection, tool abuse, privilege escalation, data exfiltration, excessive autonomy, and high-impact action abuse as agent risks; its guidance is technical advice, not a regulation or certification.
8. Verify SaaS-side controls and coverage
For each application, ask its administrator or provider documentation to confirm which events are available in the edition and region you use. At minimum, determine whether logs cover authentication, token issuance and consent, reads, searches, downloads, exports, writes, sharing changes, administrative changes, failed attempts, and policy denials. Establish retention, export format, clock source, API access, and alerting before an incident occurs.
Compare the principal shown by the SaaS log with the identity-provider and agent records from the same test. Check whether the product exposes an agent, integration, client ID, delegated user, service account, IP or workload identity, request ID, and object identifier. If the SaaS records only a generic integration account, preserve the gateway evidence that supplies the missing attribution and document the residual uncertainty.
Questions for every SaaS owner
- Which app registration, OAuth grant, service account, or integration is used by the agent?
- Can scopes be restricted to named resources and read-only operations?
- Can exports, external sharing, administrative changes, and irreversible actions require separate approval?
- Which events are delayed, sampled, or unavailable through the standard audit interface?
- Can grants be revoked without disabling unrelated integrations?
- How are provider timestamps normalized with identity-provider and agent clocks?
- What product or edition limitations prevent agent-level attribution?
9. Prioritize findings and corrective actions
Rate each finding by data sensitivity, action impact, breadth of access, ease of exploitation, attribution quality, and time to contain. Fix identity and enforcement failures before optimizing low-risk permissions: an agent that cannot be distinguished or stopped is harder to contain even when its current scope appears small.
| Finding | Immediate containment | Durable correction |
|---|---|---|
| Shared personal credential | Disable or rotate the credential and review recent use | Issue a distinct workload or agent identity bound to the delegating user or system |
| Wildcard or tenant-wide write scope | Remove write access or place the agent behind a deny policy | Constrain tools, resources, operations, and read/write modes; add expiry for exceptional grants |
| Long-lived bearer token in configuration | Revoke, rotate, and scan copies in logs and source | Use short-lived, audience-restricted credentials from a protected secret or workload-identity flow |
| Approval flag trusted by the model | Block the sensitive tool until an external check exists | Bind single-use approval to actor, exact parameters, target, freshness, and atomic consumption |
| Unjoinable SaaS events | Increase gateway logging and restrict high-impact actions | Propagate durable correlation IDs and preserve agent-to-SaaS mapping |
| Prompt-injection path to export or write | Disable the affected tool or require manual execution | Treat external content as untrusted, isolate retrieval, narrow destinations, and add abuse-case tests |
Record an owner, due date, verification method, and accepted residual risk for every item. Re-test after a permission, model, tool, provider, workflow, or logging change; a security control that worked for one agent version may not hold after the execution path changes.
10. Make the audit repeatable
Run a full review before production launch and after material changes to the agent, model provider, tools, SaaS permissions, identity configuration, or data sources. Use lighter checks on a scheduled cadence for high-impact agents and trigger an immediate review after an incident, unexplained token use, failed approval, data export, or provider-side change.
Keep a versioned evidence package containing the inventory record, permission matrix, credential register, delegation trace, execution-time test results, prompt-injection cases, joined event timelines, unresolved gaps, remediation decisions, and approver names. Store the package where auditors and incident responders can retrieve it without granting them access to the underlying business data. This turns an ad hoc investigation into a repeatable control that can show what changed, who accepted the risk, and whether the agent still has only the access its job requires.
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