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DatabricksDaniel Zoccali, Jack Yallop6 min readintermediate

How energy teams turn theft detection into governed action with Genie and AI business processes

Read on databricks.com

Summary

Databricks shows how to turn energy‑theft ML scores into a governed, end‑to‑end workflow using a Databricks App, Lakebase for live case state, Unity Catalog for data governance, and Genie One for natural‑language executive reporting. The pattern lets utilities act on alerts faster while staying compliant, and can be reused for other fraud‑type use cases.

  • A Databricks App writes flagged accounts and recovery totals to Lakebase, providing low‑latency, live case state for investigators.
  • Unity Catalog and Unity Gateway enforce data lineage, PII labeling, and model guardrails to satisfy UK regulator OFGEM requirements.
  • Genie One enables executives to ask natural‑language questions about recovery metrics, and Agent Bricks can auto‑generate board‑pack reports.
  • The lakehouse‑centric pattern (Lakeflow → model serving → Lakebase → app) is reusable for fraud, predictive maintenance, churn, etc.

Utility revenue‑protection teams and ML‑ops engineers need a regulated, low‑latency pipeline to convert fraud alerts into field actions and measurable recovery.

6/10
machine learningsecurityarchitecturedata engineeringml infra

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