Remediating Systemic Privacy Incidents
summary
Built a system that clusters privacy incidents by root-cause using a weighted-graph plus community-detection and LLM scoring, runs scheduled llama inference calls to match new reports, and then prioritizes clusters with a simple risk score (data sensitivity, user impact, regulation). Analysis produces concrete root patterns, tracked workstreams assign owners and target dates, and automatic regression alerting reopens fixes if new incidents show up. Team reports the LLM tagging and regression monitoring meaningfully reduce repeat incidents.
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chapters (14)
- 0:00Systemic privacy incidents
- 1:54Privacy incident lifecycle
- 3:38Post-mortems fail to prevent
- 5:02Remediate systemic incidents
- 5:43Clusters of privacy incidents
- 7:50LLMs match incidents to clusters
- 8:34Prioritize clusters for analysis
- 9:16Root cause patterns in clusters
- 10:18Remediation workstreams
- 11:01Monitoring after remediation
- 12:04Key takeaways
- 13:06Future prevention with AI
- 13:50Scaling tooling for teams
- 14:11Q&A
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