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Insider Risk cases include relevant configured behavioral risk signals. This helps with employee behavioral risk investigation instead of the required action-centered sensitive-data event history.

eDiscovery search targets responsive content for legal review. The action is relevant to search and collect case-specific email evidence rather than action-centered sensitive-data event history.

Content explorer summarizes classified content across supported locations. This helps with inventory of content matching a sensitive type instead of the required action-centered sensitive-data event history.

Recommended labeling suggests protection based on detected content. That addresses advisory classification suggestion, whereas the current case requires policy-driven classification of matching files.

Justification requirements make classification reductions accountable. It is used for logged declassification decision; this scenario calls for policy-driven classification of matching files.

User-selectable labels enable contextual classification by authors. That addresses user-driven classification decision, whereas the current case requires policy-driven classification of matching files.

Sensitive information types match structured patterns and proximity evidence. It would support structured national-identifier detection, not the stated semantic R&D sensitive-content detection.

Exact data match reduces ambiguity by comparing to known reference values. It would support high-confidence match against approved data, not the stated semantic R&D sensitive-content detection.

Document fingerprinting identifies content resembling a supplied exemplar. Its purpose is template-specific document identification; the specified need is semantic R&D sensitive-content detection.

AI-specific Purview controls can limit processing of selected classified sources. The action is relevant to sensitive source exclusion from AI processing rather than regular retention of AI communications.

eDiscovery can search supported interaction records for legal response. This helps with discovery of relevant AI conversations instead of the required regular retention of AI communications.

Audit records capture supported AI interaction activity. This helps with AI user-action event evidence instead of the required regular retention of AI communications.

Legal hold supersedes ordinary user deletion within its scope. That addresses mandatory legal evidence preservation, whereas the current case requires deletion following mandatory preservation.

Recorded changes support disposition accountability. It is used for auditable record label changes; this scenario calls for deletion following mandatory preservation.

Disposition review governs human approval before permanent deletion. That addresses reviewed record disposal, whereas the current case requires deletion following mandatory preservation.

DLP evaluates the message content and recipient context. It would support policy-blocked sensitive outbound mail, not the stated limited document use after access.

Retention and sensitivity serve separate lifecycle and confidentiality purposes. Its purpose is independent retention and confidentiality classification; the specified need is limited document use after access.

File-level encryption can enforce authorized use beyond the SharePoint site boundary. It would support persistent protection on a shared document, not the stated limited document use after access.

Compliance Manager ties controls and improvement actions to compliance frameworks. This helps with framework-based compliance evidence instead of the required security control implementation priorities.

Classification exploration identifies where matching sensitive data resides. This helps with location of classified content instead of the required security control implementation priorities.

AI posture reporting focuses risks from generative AI usage and content exposure. The action is relevant to AI interaction and oversharing risk visibility rather than security control implementation priorities.

Label policies expose approved classification choices to eligible users. It is used for staff selection of approved document labels; this scenario calls for visibility into missing document classification.

Label-policy settings can require users to justify classification reductions. It is used for accountable reduction of file sensitivity; this scenario calls for visibility into missing document classification.

Sensitivity labels can classify and protect files using scoped encryption settings. That addresses classified encrypted finance workbook, whereas the current case requires visibility into missing document classification.

Sensitive information types detect structured patterns and corroborating evidence. It would support detection of structured payment card data, not the stated classification of a known document form.

Trainable classifiers identify content by learned characteristics beyond fixed regular expressions. Its purpose is unstructured proprietary subject classification; the specified need is classification of a known document form.

Exact data match uses hashed reference values for high-confidence sensitive data discovery. Its purpose is matching known sensitive identifiers; the specified need is classification of a known document form.

Teams DLP evaluates supported messaging content against sensitive data rules. The action is relevant to sensitive data in team conversations rather than local removable-media exfiltration control.

Exchange DLP evaluates matching content during mail transport and applies configured actions. This helps with sensitive outbound mail control instead of the required local removable-media exfiltration control.

Cloud file DLP can detect sensitive content and restrict sharing actions where supported. The action is relevant to classified cloud-file sharing enforcement rather than local removable-media exfiltration control.

DLP alerts provide matched-rule context for investigation and follow-up. It is used for actionable sensitive-data incident triage; this scenario calls for diagnosis of conflicting DLP outcomes.

Policy tips make controls visible while approved overrides can be recorded. That addresses guided exception to a DLP rule, whereas the current case requires diagnosis of conflicting DLP outcomes.

Test mode measures candidate matches without necessarily enforcing blocks. It is used for safe predeployment DLP evaluation; this scenario calls for diagnosis of conflicting DLP outcomes.