AI Privacy Engineering

AI privacy engineering is the discipline of making AI systems handle personal data the way users, regulators, and your own privacy policy say they will — and proving it. It sits at the intersection of data protection law (GDPR, CCPA/CPRA, HIPAA, COPPA, the EU AI Act), classical security engineering (encryption, key management, access control, audit), formal privacy science (k-anonymity, differential privacy, membership inference, federated learning, MPC, HE, TEEs), and the operational machinery that actually runs in production (consent platforms, DSAR pipelines, retention engines, breach response, ROPA, transfer impact assessments). Over the last few years it has stopped being a back-office compliance function and has become an operating commitment for any organisation deploying AI at scale: privacy reviews block launches, DPIAs gate model releases, regulators issue multi-million-euro fines, customer RFPs require evidence, and incident clocks run in hours not weeks.

This track is written for the practitioners doing this work day to day: privacy engineers, ML engineers integrating privacy controls into pipelines, security engineers extending their remit, DPOs / privacy counsel collaborating with engineering, platform leads building privacy primitives, incident-response commanders running breach playbooks, and product managers shipping AI features that touch personal data. Every topic explains the underlying privacy-engineering discipline (drawing on the GDPR, CCPA/CPRA, HIPAA, the EU AI Act, ISO/IEC 27701 and 27018, NIST PE Framework, and the privacy-tech literature), the practical artefacts and rituals that operationalise it (DPIAs, threat models, ROPAs, runbooks, evaluations, dashboards), and the failure modes where privacy engineering quietly breaks down in practice. The aim is that a reader can stand up a credible AI privacy-engineering function, integrate it with engineering and governance, and defend it to boards, regulators, customers, and users.