How to Apply Privacy by Design Principles

Privacy isn’t a policy layer in AI. It’s a design constraint. The new EDPB guidance on LLMs doesn’t just outline risks. It gives builders, buyers, and decision-makers a usable blueprint for engineering privacy - not just documenting it. The key shift? → Yesterday: Protect inputs → Today: Audit the entire pipeline → Tomorrow: Design for privacy observability at runtime The real risk isn’t malicious intent. It’s silent propagation through opaque systems. In most LLM systems, sensitive data leaks not because someone intended harm but because no one mapped the flows, tested outputs, or scoped where memory could resurface prior inputs. This guidance helps close that gap. And here’s how to apply it: For Developers: • Map how personal data enters, transforms, and persists • Identify points of memorization, retention, or leakage • Use the framework to embed mitigation into each phase: pretraining, fine-tuning, inference, RAG, feedback For Users & Deployers: • Don’t treat LLMs as black boxes. Ask if data is stored, recalled, or used to retrain • Evaluate vendor claims with structured questions from the report • Build internal governance that tracks model behaviors over time For Decision-Makers & Risk Owners: • Use this to complement your DPIAs with LLM-specific threat modeling • Shift privacy thinking from legal compliance to architectural accountability • Set organizational standards for “commercial-safe” LLM usage This isn’t about slowing innovation. It’s about future-proofing it. Because the next phase of AI scale won’t just be powered by better models. It will be constrained and enabled by how seriously we engineer for trust. Thanks European Data Protection Board, Isabel Barberá H/T Peter Slattery, PhD

Does the meeting below sound familiar? Where everyone's excited about a new product launch and suddenly someone whispers "...but what about privacy?" 😅 Recently on the She Said Privacy/He Said Security podcast, I (and with my awesome co-host Justin Daniels) had an incredible conversation with Christin McMeley (Comcast's Chief Privacy & Data Strategy Officer) about something game-changing: privacy tabletops. Every day I see companies struggling with: - Engineering teams racing to innovate - Privacy teams trying to keep up - Legal teams worried about compliance - Business teams just wanting to move forward Instead of privacy being an afterthought, privacy tabletops bring everyone together BEFORE the problems start. What does this actually look like? Picture this: You're building a new app with AI features. Now ask: - Who's our audience? - What data are we collecting? - How are we handling age verification? - Where is this data actually going? - What could possibly go wrong? - Are we surprised by any of the answers? But here's the real question - when should you do this? BEFORE: - Writing that first line of code - Collecting that first piece of data - Making that first AI model - Launching that new feature - Starting that marketing campaign And with AI regulation moving fast (EU AI Act, Colorado Privacy Act, FTC guidelines... anyone else need coffee? ☕), we can't wait for perfect clarity. Just this week, I worked with a company implementing a new AI chatbot. Instead of the usual back-and-forth of privacy reviews, we ran a privacy tabletop. The result? - Engineering caught potential issues early - Privacy wasn't the "Department of No" - Legal felt confident in the approach - The business could move forward faster Remember: A privacy challenge doesn't have to derail your day or your project. Sometimes it just needs the right conversation starters and the right people in the room. Listen to the full podcast to learn more - https://lnkd.in/enEA6aWr What creative approaches have you used to make privacy more collaborative in your organization? Would love to hear your experiences! #PrivacyByDesign #DataPrivacy #Leadership #Innovation #PrivacyEngineering #AIRegulation

Michelle Finneran Dennedy and I had a great week at IAPP DC catching up with old friends and making some new ones. One of the questions we got a lot was “What does Privatus do?” Here’s one answer 😎 ✈️🔐 Retro‑fitting Privacy‑by‑Design—200+ apps, one year, one playbook When a major U.S. airline realized its entire application portfolio needed to align with privacy by design requirements, they called us in. Here’s how we turned a privacy roadblock into a privacy accelerator ⬇️ The Challenge 200+ internal & customer‑facing apps already live—irregular application of privacy controls, limited documentation, and a ticking compliance clock. Our Flight Plan 1️⃣ Prioritize: scored every app for data sensitivity & business impact → focused on the top 35. 2️⃣ Map Reality: rebuilt data‑flow diagrams w/ product, security & legal owners. 3️⃣ Threat Model: applied Solove taxonomy at every access, flow & retention point. 4️⃣ Design Strategize: matched threats to Hoepman strategies (Minimize, Hide, Separate, …). 5️⃣ Align & Action: linked each mitigation to new cyber controls and wrote hundreds of backlog‑ready user stories. 6️⃣ Institutionalize: delivered a repeatable playbook + traceability matrix format for all future builds. The Results 🌟 ✅ 35 critical apps assessed in 9 months ✅ 200 + control points documented & traceable ✅ Privacy review cycle slashed from months → weeks ✅ Managing Director: “Your team’s foundational work really helped us accelerate our privacy transformation.” Why it Matters Privacy Engineering doesn't need to wait for the next release cycle. With the right framework and the right people, you can retrofit and future‑proof—without grounding productivity. And yes, this is what we do at Privatus 😁