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To see what AI agents are doing and which information they touch, build an auditable chain from an accountable owner and a distinct identity to every tool call, data reference, decision, approval and outcome. Pair that visibility with least-privilege permissions, data-loss prevention, approval gates and a tested disablement process. Monitoring without those controls only produces a detailed record of an incident after it has happened.
What AI-agent monitoring must answer
An AI agent is software that can retrieve information, make decisions and take actions across business systems. Unlike a conventional application that follows a fixed path, an agent may choose a tool, call another service, request more data or repeat an operation. That delegated authority makes a simple application log inadequate.
A useful monitoring program should let an investigator answer these questions for any execution:
- Which business owner and technical owner are accountable for the agent?
- Which agent, version and model or provider ran?
- Which user, service principal or managed identity initiated the work?
- What task or prompt context was provided, subject to your privacy policy?
- Which tools and functions were called, with what parameters after appropriate redaction?
- Which files, records, sites or other resources were read or changed?
- Which policy checks, approvals and authorization decisions occurred?
- What result, error or side effect followed, and can it be correlated with events in other systems?
Microsoft’s Cloud Adoption Framework summarizes the objective as: “Every agent must be observable, governed, and secure.” Treat that as a design requirement rather than a slogan.
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Start with an inventory and accountable ownership
You cannot interpret an event from an unknown agent. Create a registry before production activation and include development, test, internal, SaaS and third-party agents that can reach organizational data.
Minimum registry fields
- Business owner and technical owner
- Environment and deployment location
- Model and provider
- Agent version, deployment date and lifecycle state
- Tools, APIs, plug-ins and destinations
- Data sources, sensitivity classes and geographic boundaries
- Identity, service accounts, delegated permissions and credential expiry
- Risk tier, approval status, exception records and planned retirement date
Require an owner, a documented purpose and an initial permission review before activation. Microsoft recommends a centralized governance layer with continuous visibility into ownership, identity, lifecycle, access patterns and policy compliance. Keep the registry versioned so a later investigation can distinguish an approved configuration from a permission change made last week.
Give every agent a traceable identity
Use a distinct managed identity or service principal for an autonomous workload where possible. If an agent acts for a person, pass the user identity securely so downstream systems enforce that user’s permissions; do not hide the action behind a shared account. Short-lived, narrowly scoped credentials reduce the damage from theft or misconfiguration. NIST identifies identification, authorization, auditing and non-repudiation as central issues for software agents.
Record the relationship between the initiating user and the runtime identity. An event that says only “agent-service-account-17 read a file” cannot establish who requested the work. Conversely, a user name without the agent identity cannot show which version or tool made the call.
Design an execution trace, not just a text log
Capture an immutable or tamper-evident event envelope for each meaningful stage of an execution. You may split it into several events, but retain a shared session or trace identifier so an analyst can reconstruct the sequence.
| Event element | What it establishes |
|---|---|
| Event ID and timestamp | When the event occurred and which record is being investigated |
| Tenant or workspace | Which organizational boundary produced the event |
| Initiating user or service principal | Who requested or authorized the work |
| Agent ID and version | Which deployed agent configuration ran |
| Session, correlation and trace IDs | How to join planning, tool, data and outcome events across systems |
| Model and provider | Which model service generated or evaluated a step, when available |
| Task or prompt reference | The request context, stored according to privacy and retention policy |
| Tool or function name and parameters | What capability was invoked and the inputs supplied after redaction |
| Data sources and object identifiers | Which files, records, sites or other resources were referenced |
| Policy decisions and approvals | Whether a request was allowed, denied, escalated or approved by a person |
| Action result and error | What happened, including a failure reason or partial completion |
Microsoft recommends logging plans, tool calls, decisions and outcomes. Its Purview audit records for supported AI interactions can include users, times, the Microsoft 365 service and references to files, sites or other resources. The exact fields and supported applications vary by tenant, product version and region, so verify the current documentation for your environment.
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Redact secrets while preserving forensic value
Log a parameter’s type, destination, object identifier or a one-way fingerprint when the raw value is unnecessary. Never store access tokens, session tokens, private keys or full credentials in an event. Scope sessions, rotate credentials and keep log readers on a need-to-know basis. A prompt can contain personal, confidential or regulated information; classify it and apply the same handling rules used for the source data.
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{'event_id':'8f2…','timestamp':'2026-10-18T14:22:09Z','agent_id':'claims-assistant','agent_version':'4.2.1','initiating_user':'user-identifier','session_id':'session-identifier','tool':'retrieve_case_file','object_id':'file-identifier','policy_decision':'allowed','outcome':'returned_metadata'}The identifiers in this example are placeholders for your own values; the important property is that each event can be joined to an identity, a resource and an outcome without exposing a secret.
Make data access visible across systems
Activity monitoring and access control are inseparable. For each read, write, share, export or deletion, record the resource identifier and its sensitivity label when the platform exposes one. Correlate the agent event with the data system’s native audit event rather than trusting the agent to describe its own access.
Microsoft 365 and Purview scenarios
For supported AI interactions, Microsoft Purview documentation describes prompts and responses appearing in the unified audit log, with the Microsoft 365 service and references to files accessed. Use those records with the agent’s session and identity fields to answer: which agent, for whom, accessed which resource, at what time and for what task? Audit search and export, retention, eDiscovery and DLP workflows can then support investigations and legal holds. Availability, retention periods and fields depend on the tenant, licensing and product version.
Cross-system correlation
Join agent telemetry to identity-provider, API gateway, file-store, database and network events using correlation or trace IDs. If a tool calls a downstream service that generates its own request ID, retain both IDs. Normalize timestamps to a documented standard and account for clock skew. Record denials as well as successful calls: repeated policy-denied requests can reveal prompt injection, a broken workflow or attempted privilege escalation.
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Control what an agent is allowed to do
Apply least privilege and permission inheritance
Enumerate every tool, data source and permission during registration, then remove unused access. Prefer user-permission inheritance when an agent is acting on a user’s behalf. For autonomous jobs, use a dedicated identity with only the scopes required for its stated purpose. Conditional access can add device, network, risk or location requirements before a sensitive operation.
Separate data classes and apply DLP
Define which data classes an agent may read, summarize, transform or send externally. Keep confidential sources separate from public-facing agents. Apply DLP to both inputs and outputs: a prompt can exfiltrate sensitive material even when the final response looks harmless, and an agent can copy protected data into an external ticket, message or file.
Use approval gates for high-impact actions
Require a human approval or a second control for destructive operations, external sharing, financial transactions, identity or permission changes, and unusually large or high-volume actions. Store the approver, decision, time and policy version in the trace. Set transaction and rate thresholds so an otherwise valid identity cannot perform an unlimited sequence of changes.
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Define an emergency stop
Document who can disable an agent, revoke its credentials, block its tools and preserve evidence. Test the procedure, including the case where the agent is running asynchronously or has queued work. A kill switch that exists only in a design document is not a response capability.
Send security telemetry to detection and response systems
Operational dashboards show whether an agent is healthy; a security monitoring pipeline shows whether it is behaving safely. Forward security-relevant events to the SOC or SIEM and retain the original event identifiers so analysts can pivot back to detailed records. Microsoft names Azure Monitor, Application Insights, Defender and Sentinel as examples of a layered observability and security approach.
Alert conditions worth defining
- A new agent, version or tool appears without an approved registry change.
- A permission, service principal, model provider or destination changes.
- Access volume, tool sequence or destination deviates from the agent’s baseline.
- A policy-denied call repeats or is followed by attempts through another tool.
- Prompts or tool parameters contain indicators of prompt injection or instruction override.
- The agent accesses a sensitivity class outside its declared purpose.
- Failures repeat, execution loops, or action volume spikes.
- Activity occurs from an anomalous geography or device, or outside the owner’s change window.
Baseline normal behavior by agent and workload, not by the organization as a whole. A document summarizer and a provisioning agent have different legitimate sequences and volumes. Tune thresholds with the SOC so alerts remain actionable and false positives do not train analysts to ignore them.
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Investigate with a repeatable sequence
- Start with the alert’s event, session and trace IDs.
- Confirm the initiating user, runtime identity, agent version and current registry approval.
- Reconstruct the plan, tool calls, data references, policy decisions and outcomes in time order.
- Compare permissions and behavior with the agent’s baseline and recent configuration changes.
- Contain the activity by disabling the agent, revoking credentials or blocking a tool when warranted.
- Preserve correlated identity, data-access and network records under the applicable retention or legal-hold process.
- Document the root cause, remediate permissions or prompts, and record the review decision before reactivation.
Protect the monitoring data itself
Agent logs may contain the same confidential information the agent can access. Treat them as a sensitive data store, not as harmless diagnostics.
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- Encrypt records in transit and at rest.
- Restrict search, export and deletion rights to defined roles and document who can export a case.
- Classify logs and apply DLP, eDiscovery and legal-hold workflows where required.
- Redact secrets and unnecessary personal data at collection time.
- Use tamper-evident storage and monitor administrative access to the logging system.
- Document regional or data-residency requirements before selecting a telemetry destination.
OWASP guidance emphasizes centralized logging, session scoping, traceability and need-to-know access. Those controls apply even when a log is created by an internal agent rather than a public application.
Choose tools by capability, not by dashboard appearance
Whether you assemble a platform from existing services or buy an agent-observability product, evaluate the same control surface.
| Evaluation axis | Questions to ask |
|---|---|
| Identity fidelity | Can every action be tied to a user-delegated identity or a distinct agent identity, including downstream calls? |
| Event depth | Does it capture tasks, plans, prompts or references, tool calls, decisions, approvals, errors and outcomes? |
| Data context | Can events include file or resource IDs, sensitivity labels, DLP decisions and cross-system correlation? |
| Response integration | Can it alert the SOC, invoke approval gates, revoke credentials and disable an agent quickly? |
| Retention and privacy | Are retention, export, redaction, access control and regional storage configurable? |
| Coverage | Does it cover SaaS agents, custom applications, cloud workloads, on-premises systems and third-party agents? |
| Operational cost | What ingestion volume, licensing, storage, analyst effort and false-positive rate will the design create? |
A product that records model latency but cannot identify the files read is not sufficient for a data-access question. Conversely, a complete audit trail with no alerting or disablement path leaves the organization slow to respond.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Implement the program in a controlled sequence
- Register the population. Find production, development and third-party agents; assign owners, identities, risk tiers and lifecycle states.
- Map authority. List tools, data sources, permissions, destinations and approval requirements; remove unused access.
- Version the telemetry contract. Define required event fields, correlation IDs, redaction rules and failure behavior when telemetry is unavailable.
- Connect data sources. Join agent events to identity, file, database, API and network audit records.
- Enforce policy. Configure DLP, sensitivity boundaries, conditional access, rate limits and human approvals.
- Build detection. Establish baselines and alerts for new agents, privilege changes, unusual access, denied calls and anomalous actions.
- Test response. Exercise approval, disablement, credential revocation, evidence preservation and restoration procedures.
- Review continuously. On a scheduled cadence, reassess the inventory, permissions, telemetry quality, exceptions, retention and alert performance.
Make telemetry failure a visible condition. If an agent continues operating when its required audit event cannot be written, you may create an unmonitored execution path. Decide in advance whether the action should be blocked, degraded to read-only or allowed with an incident raised.
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Problem: The response does not reveal which plan, tool or file produced it. Fix: Capture the execution stages and retain shared trace IDs, including denied and failed calls.
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Problem: Investigators cannot attribute actions and a compromise affects every workload. Fix: Use distinct managed identities or securely propagated user identity, with scoped credentials.
Prompts are copied into a general log bucket
Problem: Sensitive business or personal data becomes broadly searchable. Fix: classify and minimize prompt content, redact secrets, restrict access and set a documented retention period.
Monitoring is disconnected from data audits
Problem: You can see that a tool ran but not which record or file it touched. Fix: capture resource identifiers and correlate with the source system’s native audit events.
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Problem: legitimate batch jobs generate noise while a low-volume privilege change is missed. Fix: baseline sequences, destinations, sensitivity classes, identity changes and policy decisions as well as counts.
There is no tested stop procedure
Problem: Teams debate ownership during an incident while the agent continues acting. Fix: assign responders, document credential revocation and tool blocking, and test the kill switch.
What current guidance says about risk
NIST’s concept paper released February 5, 2026 states: “However, realizing these benefits requires understanding the potential risks from giving AI agents access to diverse data sets, tools, and applications, and applying appropriate identification and authorization controls to mitigate these risks.” The paper is emerging guidance, not a mandatory standard, but it reinforces why identity, authorization and auditability belong in the architecture from the start.
Microsoft’s governance guidance likewise calls for a centralized, enforceable baseline covering ownership, identity, lifecycle, observability, data governance and security. Implement those controls together: an inventory without telemetry is a list, telemetry without authorization is surveillance after the fact, and permissions without an owner will drift.
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