Investigate suspected misuse as a timeline, not as a single “AI log” search. Preserve the initial alert and raw records, identify the assistant and human or service identity, inspect interaction metadata, correlate cloud control-plane and data-access events with endpoint telemetry, and escalate to prompt or response content only when the question cannot be answered from metadata. Record retention, permissions, logging gaps, and every automated or investigator-initiated action so the result is defensible.
What a defensible investigation must establish
A suspicious-use investigation should answer five separate questions:
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- Who: Which user, service account, application identity, or delegated administrator initiated or enabled the activity?
- When and from where: What were the event times, time zone, tenant or project, source address, host, device, and sign-in context?
- What the AI tool touched: Which cloud resources, records, files, connectors, subscriptions, projects, or security cases were referenced?
- What happened afterward: Did the interaction coincide with data reads, permission changes, API calls, policy denials, file operations, process launches, network connections, or response actions?
- What remains unknown: Are prompt or response text, endpoint records, Data Access events, or older logs unavailable because of product coverage, configuration, permissions, exclusions, or retention?
These are different evidence layers. An audit event may prove that an account used an assistant and referenced a resource without revealing the prompt or the generated answer. A cloud control-plane record may show a role change without showing which local process requested it. Endpoint telemetry may show a browser, script, or agent but not the cloud data returned to it. Treat each record as one part of the timeline.
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1. Define and preserve the event
Start with the alert, report, or anomaly that triggered the investigation. Write down the suspected users and service accounts, the AI product or agent, affected devices, cloud tenants or projects, resources, and an initial time window. Include the time zone and whether timestamps came from a device, cloud service, or SIEM normalization layer.
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Preserve the underlying records before changing configuration, revoking access, deleting an agent, or taking a disruptive containment action. Export or place a legal hold on the relevant audit and endpoint data when your organization’s policy permits it. Keep the original timestamps and record identifiers; do not rely solely on a dashboard view that may apply a moving retention window or a changing schema.
- Capture the alert text, detection rule, severity, and first-observed time.
- Record the account, application identity, host, IP address, tenant, subscription, project, and resource identifiers shown by the alert.
- Note any immediate containment already performed and who authorized it.
- Set a search window that includes activity before and after the alert; follow-on changes can be more probative than the initial interaction.
2. Establish that an AI interaction occurred
In Microsoft environments, use Purview Audit to filter AI activity by the applicable operation name, record type, workload, and other available operation fields. Inspect the user, event time, app identity, host application, activity type, and resource references. Field availability differs by product and audit surface, so record which fields were actually present rather than treating an empty field as proof that no value existed.
Identify the exact assistant and host context. “Copilot” can describe different experiences, including cloud-management and security-operations uses. A Security Copilot event, an assistant embedded in another Microsoft workload, and an administrative operation performed through a host application should not be merged into one generic category. Record the product surface, tenant, and operation name shown in the event.
For Security Copilot, Microsoft describes Unified Audit Log coverage as administrative events and activity metadata. If the question concerns the actual prompt, response, or retained memory, that is a separate evidence question; use the content-review path described later rather than assuming the Unified Audit Log contains the text.
3. Correlate identity and cloud activity
Build a timeline around the AI event. Join the interaction to sign-ins, multifactor authentication results, token or session details, role and permission changes, API operations, data reads, policy denials, and system-driven changes. Use the same account, application identity, device, source address, and resource identifiers whenever the records expose them. Allow for clock skew and ingestion delay, and preserve the provider’s original event time alongside the SIEM’s normalized time.
Cloud logs answer different questions. A configuration change may be recorded even when the underlying data read is not, while a data-access event can exist without a corresponding configuration change. Examine both categories and document which are enabled.
Google Cloud records to inspect
| Log category | What it can show | Coverage caveat |
|---|---|---|
| Admin Activity | User-driven changes to configuration or resource metadata. | Written by default. |
| Data Access | Reads and user-driven operations on resource data. | Disabled by default for most services except BigQuery; enabling it can increase log-usage charges. |
| System Event | Provider-driven changes made by the service. | Written by default. |
| Policy Denied | Requests rejected by an organization or resource policy. | Generated by default, but exclusions can prevent storage. |
In Google Cloud, do not infer that no data was read merely because a Data Access search is empty. Verify whether Data Access logging was enabled for the relevant service, whether an exclusion removed the record, and whether the retention period still covers the event.
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4. Correlate endpoint evidence
For every implicated device, search the deployed endpoint platform for the same time window. Examine process creation, logon, file, network, and response-action telemetry. Join on the platform’s user and device identifiers, then validate each field against the current schema for that deployment. Endpoint products differ in table names, retention, sensor coverage, and whether browser, script, or agent activity is captured; there is no universal event-table name that can be safely reused across platforms.
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- Process evidence can show the browser, shell, script interpreter, connector, or security agent that was running.
- Logon evidence can distinguish an interactive session from a service account, scheduled task, remote session, or token-based operation.
- File evidence can reveal downloads, staging, archive creation, or access to sensitive paths.
- Network evidence can connect a device to cloud endpoints, data stores, management APIs, or unusual destinations.
- Response-action evidence can show isolation, remediation, deletion, quarantine, or other changes made by an operator or automated policy.
Do not publish or rely on platform-specific query syntax until you have verified the reader’s product version, schema, sensor policy, and data retention. A syntactically valid query against the wrong schema can produce a convincing but incomplete result.
5. Use investigation and SIEM tools as pivots, not as the evidence itself
Investigation assistants can accelerate triage, but their output is not a substitute for original telemetry. AWS CloudWatch investigations can analyze telemetry such as CloudTrail change events and CloudWatch Logs Insights queries. AWS documents that every action taken by a CloudWatch investigation is logged in CloudTrail. Review those investigator actions alongside the suspected incident, especially if the investigation added data sources, shared results, or executed a suggested runbook remediation.
CloudWatch investigations use the signed-in user’s permissions to analyze resources and provide suggestions. Configured investigation groups may periodically scan resources to map topology. AWS also notes that some services can invoke KMS decrypt API calls on CloudWatch’s behalf for describe or list operations. Understand these permissions and resulting events before classifying them as attacker activity or dismissing them as background noise.
Microsoft’s Azure-focused playbook includes Sentinel query examples for Azure AI and Foundry alert types, including credential-theft and jailbreak-related detections. Treat alert names and table schemas as version-dependent: verify them against the current product before reusing a query, then correlate the alert with Azure activity such as deployment or key-listing operations.
6. Escalate to content review only when metadata cannot answer the question
Audit records often identify the user, time, application, activity, and referenced resources without storing the actual prompt or response. Microsoft’s playbook states: “Unified Audit Log (UAL) events are metadata-first.” If you must determine what instruction was given, what answer was generated, or what memory was retained, use an authorized content-review surface such as Purview eDiscovery or DSPM for AI when it is enabled and configured.
Before collecting content, confirm the required product setup, role permissions, retention, legal basis, and internal authorization. Prompt and response text can contain personal data, secrets, regulated information, or privileged communications. Limit collection to the approved scope, record who accessed it, and preserve the chain of custody. If the content surface was not enabled at the time, state that limitation; do not reconstruct a prompt from an audit event that never stored it.
7. Test competing explanations
Compare the activity with the user’s duties, approved tool access, change windows, known automation, and ordinary endpoint and cloud behavior. Look for a combination of signals rather than a single unusual event:
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- A resource scope that exceeds the account’s normal responsibilities.
- Permission, role, key, deployment, or policy changes near the AI interaction.
- Policy denials followed by successful access through another identity or path.
- Endpoint downloads, archive creation, scripts, or outbound connections after the interaction.
- Use of an assistant’s remediation capability that changed production resources.
Separate observed facts from generated explanations. AWS describes CloudWatch investigation output as AI-generated observations, suggestions, and hypotheses. Validate every hypothesis against the original audit, identity, cloud, and endpoint records. A plausible narrative is not evidence until the underlying events support it.
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8. Document scope and evidence gaps
Close the investigation record with an explicit scope statement. Include the time range, accounts, tenants, projects, subscriptions, devices, resources, queries and filters, permissions used, data sources consulted, collection status, retention boundaries, and records that were unavailable. State whether timestamps were converted and how.
Check logging defaults, exclusions, routing, and retention before interpreting a missing event. An empty search can mean that the service does not emit that event, Data Access logging was disabled, an exclusion dropped it, the sensor was offline, the record was routed elsewhere, or retention expired. “No record found” is not the same as “no activity occurred.”
What each evidence source can and cannot prove
| Evidence source | Strongest answers | Typical limitations | Prerequisites to verify |
|---|---|---|---|
| AI audit and interaction metadata | Who interacted, when, which application or host was involved, and which resources were referenced when those fields are available. | May not contain prompt, response, or memory content; fields vary by product and audit surface. | Correct operation filters, workload and record type, tenant configuration, permissions, and retention. |
| Cloud control-plane logs | Configuration, role, deployment, key, policy, and other administrative changes. | May not show reads of resource data or activity performed by a different identity. | Provider audit service, complete account or project scope, and reliable time correlation. |
| Cloud data-access logs | Reads and user-driven operations on resource data where the service records them. | Coverage can be disabled, excluded, chargeable, or service-dependent. | Data Access enabled for the relevant service, routing and retention confirmed. |
| Endpoint telemetry | Processes, logons, files, network connections, and response actions on enrolled devices. | Sensor gaps, offline periods, schema differences, and short retention can leave blind spots. | Healthy sensor, correct device scope, current schema, and preserved raw events. |
| SIEM or investigation-assistant output | Cross-source pivots, summaries, suggested searches, and possible relationships. | Generated observations and hypotheses can be wrong; actions taken by the tool may alter the environment. | Source connectors, permissions, query history, and review of every automated action. |
| Content-review systems | Prompt, response, or memory material when the product captured and retained it. | Requires additional setup and authorization; content may never have been recorded. | Configured Purview eDiscovery or DSPM for AI capability, role access, retention, and approved purpose. |
Provider-specific details that change the investigation
Microsoft audit and retention
Microsoft documents that AI audit records can capture interaction timing and resource references, but the available content depends on the product and audit surface. For the specified pay-as-you-go audit logs covering non-Microsoft AI applications, Microsoft documents 180 days of retention. That figure applies to that logging model; it is not a universal retention period for Microsoft applications, Security Copilot, or every AI service. Verify the tenant’s licensing, audit mode, retention policy, and collection date.
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Admin Activity and System Event records are written by default. Data Access records are disabled by default for most services other than BigQuery, and enabling them can create additional log-usage charges. Policy Denied entries are generated by default, but exclusions can keep them from being stored. Record these settings in the case file because they determine whether an apparent gap is meaningful.
AWS CloudWatch investigations
CloudWatch investigations can use signed-in-user permissions, add data sources, share results with appropriately permissioned users, and execute suggested runbook remediations. Every action taken by the investigation is logged in CloudTrail. Investigators should review those records, understand configured investigation-group scans, and account for service-initiated KMS decrypt calls that may appear during describe or list operations.
Azure AI alerting
Azure Sentinel examples cover AI and Foundry alert types such as credential theft and jailbreak detections. Alert names, tables, and fields can change. Verify the current schema, then correlate the alert with related Azure activity, including deployments and key-listing operations, instead of treating the alert label as a conclusion.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Common failure modes and corrective actions
The search starts with a generic “AI” keyword
Why it fails: Different assistants and host applications use different operation names and workloads, while a generic search can mix unrelated activity.
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Correction: Identify the product surface first, then filter by the documented operation, record type, workload, tenant, and time range. Preserve the exact event fields returned.
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An empty audit result is treated as proof of no use
Why it fails: The event may have expired, been excluded, routed elsewhere, or never been emitted by that product.
Correction: Check retention, logging mode, exclusions, connector health, and permissions. Search identity, cloud, endpoint, and SIEM records for corroborating activity.
Metadata is mistaken for prompt or response content
Why it fails: Audit metadata can show who did what, when, and with which resources without storing the text.
Correction: Use an authorized content-review surface if content is necessary, and state plainly when content was not captured or cannot be collected.
Cloud control-plane coverage is assumed to include data reads
Why it fails: Administrative changes and resource-data access are separate log categories.
Correction: Inspect Data Access coverage for the specific Google Cloud service or the equivalent data-access records in the relevant provider. Verify enablement and cost implications.
An AI-generated investigation narrative is accepted without validation
Why it fails: Suggestions and hypotheses can be incomplete or incorrect, and the investigation tool itself may have generated additional events.
Correction: Reconcile each statement with raw audit, identity, cloud, and endpoint records. Review CloudTrail or equivalent action logs for investigator-initiated changes.
Endpoint queries are copied across products
Why it fails: Event-table names, fields, retention, and sensor coverage are platform-specific.
Correction: Validate the current schema and deployment policy locally before running or publishing a query. Record offline periods and unenrolled devices as explicit gaps.
A practical investigation record
Use one timeline row per material event and keep the provider’s original record identifier. The following fields make the result reproducible:
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|---|---|
| Time | Original provider timestamp, normalized timestamp, time zone, and any known ingestion delay. |
| Identity | User, service account, application identity, delegated administrator, and authentication context. |
| AI context | Product, host application, operation, record type, workload, and interaction or session identifier. |
| Location | Tenant, subscription, project, account, device, source address, and region when available. |
| Resource | Referenced files, cases, datasets, storage locations, keys, deployments, policies, or other resource identifiers. |
| Action | Read, change, denial, download, process launch, network connection, remediation, or other observed operation. |
| Evidence status | Raw record preserved, derived summary, generated hypothesis, unavailable data, or pending content review. |
| Confidence and explanation | Observed facts, competing explanations considered, and the record that supports or weakens each explanation. |
Keep generated summaries separate from raw events. If an investigator or automated runbook changed a resource, mark that action as part of the timeline rather than blending it into the suspected user’s activity.
When to conclude, contain, or escalate
You can usually conclude that an AI interaction occurred when an attributable audit event is corroborated by identity context and the product surface is unambiguous. You can conclude that the interaction was followed by a cloud or endpoint action only when the related records match on identity, resource, device, and time closely enough to exclude ordinary automation or another authorized operator.
Escalate to incident response, privacy, legal, or an internal review authority when the evidence indicates credential theft, unauthorized data access, sensitive prompt or response content, production changes, policy bypass, or an investigation tool that executed remediation. Preserve records before containment where policy allows, and document the authority, time, and scope of each containment step.
The strongest final report distinguishes three statements: what the logs directly show, what they reasonably support when correlated, and what cannot be determined because a required evidence layer was missing. That separation prevents both overclaiming and the false reassurance of an empty search.
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