From Legalese to Logic: Translating Privacy Requirements into Verifiable Controls via...
summary
An LLM-powered pipeline that translates textual privacy requirements into implementable tasks uses a deterministic runner plus an agent network and Python guardrails to interpret intent, traverse lineage, and analyze candidate assets across billions of assets (often producing millions of candidate paths). It reduces multi-month manual effort to minutes, finds systemic patterns on first pass, self-corrects hallucinations, emits allow rules, and enforces simulation only changes with mandatory human signoff.
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chapters (13)
- 0:00Privacy requirement translation
- 1:55PrivacyWare for purpose limitation
- 3:42Manual to agentic workflows
- 4:25Agent workflow deep dive
- 6:31Banana data example
- 10:03Technical architecture and guardrails
- 12:07Autonomy versus oversight
- 13:09Risks in agent systems
- 14:57Manual workflow comparison
- 16:03Production insights
- 17:06Safe LLM deployment insights
- 17:27Takeaways and template
- 18:33Conclusion and future
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