AI Privacy & Compliance Consulting
Design and implement query-time privacy frameworks for LLM applications — semantic generalization, data minimization, and GDPR-compliant AI pipelines without sacrificing utility.
Service Overview
Most "AI compliance" advice stops at policy documents. This is engineering-level privacy work: query-time semantic generalization, data minimization built into the pipeline itself, and GDPR-aligned architecture that doesn't gut your model's usefulness to hit a compliance checkbox.
What's Included
- privacy-architecture audit
- query-time generalization design
- data minimization implementation
- GDPR-alignment documentation
- and a defensible technical rationale you can hand to legal or a client's security team.
Tools & Technologies
Frequently Asked Questions
Is this the same as data anonymization?
No — traditional anonymization strips fields before storage. This addresses privacy leakage at inference time, which is where most LLM privacy failures actually happen.
Do you work with teams outside the EU on GDPR-adjacent work?
Yes — most engagements are for teams selling into EU/UK markets regardless of where they're based.
What's a realistic timeline for a privacy audit?
Initial audit and architecture recommendation typically runs 1–2 weeks; implementation scope depends on your existing pipeline.
Is this for you?
- You're shipping an LLM product handling personal or regulated data and need a defensible privacy architecture, not just a policy PDF
- You've been told "just redact the PII" and know that's not a real answer for semantic leakage
- You need an AI governance framework that survives a security or compliance review