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Sentinel Notebooks and Advanced Enrichment

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GitHub Cloud2BR OSS - Learning Hub

Last updated: 2026-07-27


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Notebooks support advanced investigation, visualization, enrichment, and data science workflows. They are powerful but introduce code, package, credential, compute, and data-export risks that require the same governance as production automation.

Use notebooks for the right work

Need Better fit
Repeatable real-time detection Analytics rule
Deterministic response action Playbook with approval controls
One-off complex investigation Notebook
Exploratory analysis or visualization Notebook or workbook
Long-lived enrichment service Managed, reviewed application or function

Secure the notebook environment

  • Use approved compute, network paths, identities, package sources, and data handling controls.
  • Do not embed secrets in notebooks. Use managed identities and approved secret retrieval where supported.
  • Version-control notebooks, dependencies, parameters, and expected outputs.
  • Restrict who can execute, modify, export, or share notebooks with sensitive investigation data.
  • Record external enrichment calls, terms of use, privacy impact, and fallback behavior before sending telemetry outside the approved boundary.

Investigation pattern

  1. Start from an incident ID or saved Kusto Query Language (KQL) result with a defined question.
  2. Use parameters for time range, tenant, workspace, entities, and indicators.
  3. Enrich with approved sources and retain the source and confidence of each result.
  4. Export only the minimum evidence needed for the case.
  5. Save conclusions, notebook version, parameters, and output reference in the incident or ticket.

Business example

An analyst uses a versioned notebook to visualize all authentication, endpoint, and network events for a suspected compromised host. The notebook obtains data with a managed identity, queries a pre-approved threat-intelligence API, and saves only a summarized evidence report to the case. The security operations center (SOC) later promotes the validated enrichment logic into a governed playbook rather than leaving it as an analyst-specific script.