Inspiration
Sensitive-data risks become more serious when they spread through lineage. We wanted to turn DataHub context into clear, actionable security decisions.
What it does
SentinelGraph discovers DataHub metadata and lineage, identifies security and governance risks, calculates blast radius, generates remediation, and produces reports, dashboard insights, and AI summaries.
How we built it
Built in Python with coordinated agents for metadata discovery, security assessment, lineage analysis, risk correlation, recommendations, reporting, and optional DataHub publishing.
Challenges we ran into
Balancing meaningful risk analysis with a reproducible demo that does not require credentials, a database, or a hosted DataHub instance.
Accomplishments that we're proud of
A complete offline POC with critical-risk scenarios, executive reports, dashboard output, AI Q&A, simulated publishing, and automated DataHub integration tests.
What we learned
Lineage gives metadata issues business context: one ungoverned dataset can affect multiple downstream reports, pipelines, and consumers.
What's next for sentinel-graph
Add multi-dataset domain scans, historical risk trends, interactive lineage visualization, and deeper DataHub MCP integration.
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