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Review dispositions preserve a decision trail for policy violations. That addresses accountable escalation of flagged messages, whereas the current case requires conduct-related Teams message review.

Role-controlled review limits access to sensitive conversation content. It is used for protected access to communications investigations; this scenario calls for conduct-related Teams message review.

Narrower detection criteria can improve review precision. It is used for tuned employee communication policy; this scenario calls for conduct-related Teams message review.

AI activity telemetry includes supported rule-match events and metadata. Its purpose is Copilot DLP trigger timeline; the specified need is temporal label-usage evidence.

Data access governance focuses site access exposure rather than classification actions. It would support sites creating broad sharing links, not the stated temporal label-usage evidence.

Case-based discovery locates responsive items and preserves review controls. Its purpose is formal litigation content collection; the specified need is temporal label-usage evidence.

Assessments identify and prioritize risky AI processing conditions. The action is relevant to prioritized sensitive prompt exposure response rather than unsanctioned AI exposure insight.

DLP provides enforcement evidence for supported AI content flows. This helps with AI-specific data loss prevention instead of the required unsanctioned AI exposure insight.

Activity detail offers contextual evidence subject to permission and capture rules. The action is relevant to specific risky AI interaction evidence rather than unsanctioned AI exposure insight.

Case segregation controls evidence scope and reviewer authority. It is used for distinct legal matter boundaries; this scenario calls for targeted email discovery.

Hold preserves covered content for legal collection. It is used for litigation preservation before review; this scenario calls for targeted email discovery.

AI interactions can be subject to discovery using supported item types. That addresses litigation search across AI conversations, whereas the current case requires targeted email discovery.

Audit events can expose rule modifications and the acting principal. It would support historical mailbox-rule mutation evidence, not the stated actor for an access-permission change.

Monitoring converts historical event data into actionable detection. Its purpose is timely reaction to concerning activity; the specified need is actor for an access-permission change.

AI interaction audit records provide relevant usage evidence. Its purpose is time evidence of Copilot activity; the specified need is actor for an access-permission change.

Disposition review inserts accountable review into content deletion. The action is relevant to approved records destruction rather than owner-driven oversharing remediation.

Alert casework supports remediation beyond a simple block. This helps with governed data incident follow-up instead of the required owner-driven oversharing remediation.

Assessment tasks track required control maturity and remediation. The action is relevant to recurring regulatory assessment rather than owner-driven oversharing remediation.

AI posture reporting combines supported usage patterns with sensitive data findings. It is used for AI adoption-associated data risk context; this scenario calls for sensitive prompt leakage prevention.

Auditing provides user and time attribution for supported AI interactions. That addresses traceable sensitive Copilot activity, whereas the current case requires sensitive prompt leakage prevention.

AI grounding respects effective source permissions; oversharing is a source governance fault. It is used for prevention of unauthorized AI source discovery; this scenario calls for sensitive prompt leakage prevention.

Hold requirements prevent loss of evidence despite ordinary deletion schedules. Its purpose is hold-compliant preservation; the specified need is file-sharing policy incident evidence.

Reviewers assess policy context and document their disposition. Its purpose is conduct violation assessment; the specified need is file-sharing policy incident evidence.

Insider risk contextualizes configured user behavior for triage. It would support suspicious worker file-exfiltration investigation, not the stated file-sharing policy incident evidence.

EDM identifies values known to belong to the protected dataset. This helps with precise matching of known customer IDs instead of the required discovery of structured financial identifiers.

Document fingerprints match against a reference form structure. The action is relevant to recognition of standard legal forms rather than discovery of structured financial identifiers.

Semantic classification can identify document themes beyond fixed patterns. The action is relevant to discovery of unstructured trade secret content rather than discovery of structured financial identifiers.

RAC adds a site-level group boundary above ordinary link permissions. It is used for hard restriction of a high-risk site; this scenario calls for broad link-exposure risk identification.

Retention policies are controls rather than just reports. That addresses mandatory preservation of AI interaction data, whereas the current case requires broad link-exposure risk identification.

Aggregated risk reporting describes activity patterns without changing access. It is used for trend monitoring of insider-risk alerts; this scenario calls for broad link-exposure risk identification.

DLP incidents show sensitive data matches and policy enforcement. Its purpose is investigation of sensitive document transfer; the specified need is review of harmful employee messages.

Insider-risk evidence combines configured user-activity risk signals. It would support insider-driven exfiltration case, not the stated review of harmful employee messages.

eDiscovery organizes litigation-related content finding and holding. Its purpose is formal legal evidence collection; the specified need is review of harmful employee messages.