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Tool integrations must respect the authorization of underlying security data. This addresses prevent MCP-based privilege escalation; it does not directly satisfy connect a notebook to Sentinel MCP tools.

Tool destination trust boundaries matter for confidential incident data. That action focuses on protect sensitive data in MCP integrations rather than the specified need to connect a notebook to Sentinel MCP tools.

AI-assisted tool summaries are not substitutes for raw event provenance. This addresses ground MCP-assisted threat findings in source logs; it does not directly satisfy connect a notebook to Sentinel MCP tools.

KQL lake coverage does not retroactively include all prior historical archive data. This addresses retrieve pre-onboarding legacy archive evidence; it does not directly satisfy avoid unnecessary historical computation.

Multi-table historical query coverage is constrained by available data in each source. Its intended use is to establish exact historical search completeness, whereas this case requires the analyst to avoid unnecessary historical computation.

KQL jobs can correlate supported data lake records with rich KQL operators. This addresses query recent lake records across tables; it does not directly satisfy avoid unnecessary historical computation.

Notebook artifacts require the same confidentiality controls as raw incident logs. This addresses secure notebook incident artifacts; it does not directly satisfy ensure notebook analysis reproducibility.

Statistical outliers are not automatically hostile actors. Its intended use is to adjudicate machine-learning hunting outliers, whereas this case requires the analyst to ensure notebook analysis reproducibility.

Chart provenance is necessary to interpret partial historical evidence correctly. That action focuses on make notebook visualizations reproducible rather than the specified need to ensure notebook analysis reproducibility.

Detection rules directly process eligible arriving events with lower latency. Its intended use is to choose active detection for immediate threats, whereas this case requires the analyst to retrieve detailed historical records.

Asynchronous KQL jobs are suited to broad historical multi-source correlations. That action focuses on run a one-time long-range correlation rather than the specified need to retrieve detailed historical records.

Summary rules are designed for frequent aggregate materialization. This addresses sustain recurring short-interval metrics; it does not directly satisfy retrieve detailed historical records.

Search jobs can query applicable historical archived records outside later lake onboarding coverage. This addresses access pre-onboarding archived events; it does not directly satisfy distinguish search jobs from active analytic rules.

KQL jobs support multi-table joins and unions for eligible lake data. That action focuses on execute complex multi-table historical correlation rather than the specified need to distinguish search jobs from active analytic rules.

Search jobs can return large single-table result sets into a queryable investigation table. This addresses materialize one-table forensic search results; it does not directly satisfy distinguish search jobs from active analytic rules.

Historical scan cost should match the needed cadence and lookback. This addresses right-size costly historical job schedules; it does not directly satisfy prevent lake job results landing in a test table.

Stale promoted output can mask failed background query jobs. Its intended use is to repair a failed scheduled lake enrichment, whereas this case requires the analyst to prevent lake job results landing in a test table.

Scheduled jobs can continue running until their schedule is explicitly retired. This addresses decommission outdated lake jobs; it does not directly satisfy prevent lake job results landing in a test table.

Jobs can materialize valid results in an unintended location if output bindings are wrong. This addresses prevent lake job results landing in a test table; it does not directly satisfy repair a failed scheduled lake enrichment.

Historical scan cost should match the needed cadence and lookback. Its intended use is to right-size costly historical job schedules, whereas this case requires the analyst to repair a failed scheduled lake enrichment.

Scheduled jobs can continue running until their schedule is explicitly retired. That action focuses on decommission outdated lake jobs rather than the specified need to repair a failed scheduled lake enrichment.

Hunting bookmarks preserve selected query evidence for continued triage. Its intended use is to preserve a noteworthy hunting result, whereas this case requires the analyst to restore an outdated saved hunt.

Hunting queries are investigator-initiated, while analytics rules provide recurring detection. That action focuses on convert a confirmed hunt into active monitoring rather than the specified need to restore an outdated saved hunt.

Saved hunting queries preserve repeatable investigations without immediately creating incidents. This addresses reuse a vetted threat-hunting hypothesis; it does not directly satisfy restore an outdated saved hunt.

Successful computation alone does not guarantee a separate dashboard consumed its output. This addresses connect scheduled notebook output to reporting; it does not directly satisfy schedule notebooks through the correct interface.

Notebook execution depends on compatible runtime libraries and environment state. That action focuses on restore notebook job dependencies rather than the specified need to schedule notebooks through the correct interface.

Notebook jobs can execute approved notebooks on a schedule where supported. This addresses automate recurring notebook analysis; it does not directly satisfy schedule notebooks through the correct interface.

Changes to rollup dimensions can alter the meaning of existing detections. This addresses protect dependent analytics from summary schema drift; it does not directly satisfy minimize sensitive summary output.

Central rollups provide consistent precomputed measures across consumers. Its intended use is to share stable SOC summary metrics, whereas this case requires the analyst to minimize sensitive summary output.

Successfully aggregating an empty dataset does not prove telemetry health. This addresses identify empty summaries caused by missing input; it does not directly satisfy minimize sensitive summary output.

Selective inputs reduce join expansion and execution cost. This addresses optimize expensive lake joins; it does not directly satisfy prefer available summary signals to repeated scans.

Expired or moved source data cannot be recovered by query syntax alone. Its intended use is to assess retention before blaming KQL logic, whereas this case requires the analyst to prefer available summary signals to repeated scans.

Narrow source and time boundaries reduce unnecessary scans. That action focuses on reduce hunting query resource use rather than the specified need to prefer available summary signals to repeated scans.