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Container labels govern supported site and group configuration settings. It is used for site-level classification policy; this scenario calls for file-level confidentiality.

Default labeling provides a starting classification for eligible new files. That addresses baseline classification of new files, whereas the current case requires file-level confidentiality.

Publishing configuration controls available labels and user presentation. It is used for published label catalog for users; this scenario calls for file-level confidentiality.

DLP telemetry records policy match and enforcement. Its purpose is source of a sensitive mail protection action; the specified need is file deletion activity evidence.

eDiscovery supports case-based identification and collection of evidence. Its purpose is litigation search across supported content; the specified need is file deletion activity evidence.

Holds preserve relevant items during a legal matter. It would support preservation of responsive mailbox data, not the stated file deletion activity evidence.

Insider Risk cases include relevant configured behavioral risk signals. This helps with employee behavioral risk investigation instead of the required inventory of content matching a sensitive type.

eDiscovery search targets responsive content for legal review. The action is relevant to search and collect case-specific email evidence rather than inventory of content matching a sensitive type.

Activity explorer tracks classified-content activities over time. The action is relevant to action-centered sensitive-data event history rather than inventory of content matching a sensitive type.

Auto-labeling can apply a matching classification without per-file manual selection. It is used for policy-driven classification of matching files; this scenario calls for user-driven classification decision.

Recommended labeling suggests protection based on detected content. That addresses advisory classification suggestion, whereas the current case requires user-driven classification decision.

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

Classifier models identify topic and content patterns not tied to one exact token. Its purpose is semantic R&D sensitive-content detection; the specified need is structured national-identifier detection.

Exact data match reduces ambiguity by comparing to known reference values. It would support high-confidence match against approved data, not the stated structured national-identifier detection.

Document fingerprinting identifies content resembling a supplied exemplar. Its purpose is template-specific document identification; the specified need is structured national-identifier 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 discovery of relevant AI conversations.

Supported retention policies govern preservation and expiration of interaction records. The action is relevant to regular retention of AI communications rather than discovery of relevant AI conversations.

Audit records capture supported AI interaction activity. This helps with AI user-action event evidence instead of the required discovery of relevant AI conversations.

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

Recorded changes support disposition accountability. It is used for auditable record label changes; this scenario calls for reviewed record disposal.

Retention preserves records for the prescribed period before deletion. It is used for deletion following mandatory preservation; this scenario calls for reviewed record disposal.

Rights management can constrain permitted operations independent of file retrieval. Its purpose is limited document use after access; the specified need is persistent protection on a shared document.

DLP evaluates the message content and recipient context. It would support policy-blocked sensitive outbound mail, not the stated persistent protection on a shared document.

Retention and sensitivity serve separate lifecycle and confidentiality purposes. Its purpose is independent retention and confidentiality classification; the specified need is persistent protection on a shared document.

Security score recommendations emphasize security posture rather than audit controls. The action is relevant to security control implementation priorities rather than framework-based compliance evidence.

Classification exploration identifies where matching sensitive data resides. This helps with location of classified content instead of the required framework-based compliance evidence.

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 framework-based compliance evidence.

Coverage reporting identifies labeled versus unlabeled content. That addresses visibility into missing document classification, whereas the current case requires staff selection of approved document labels.

Label-policy settings can require users to justify classification reductions. It is used for accountable reduction of file sensitivity; this scenario calls for staff selection of approved document labels.

Sensitivity labels can classify and protect files using scoped encryption settings. That addresses classified encrypted finance workbook, whereas the current case requires staff selection of approved document labels.

Sensitive information types detect structured patterns and corroborating evidence. It would support detection of structured payment card data, not the stated unstructured proprietary subject classification.

Fingerprints match document structures against an approved exemplar. It would support classification of a known document form, not the stated unstructured proprietary subject classification.

Exact data match uses hashed reference values for high-confidence sensitive data discovery. Its purpose is matching known sensitive identifiers; the specified need is unstructured proprietary subject classification.