Scaling Privacy in Engineering Teams

Google has published a whitepaper on privacy in AI, proposing a practical framework for integrating Privacy Enhancing Technologies (PETs) across the entire AI lifecycle — from data collection to training, personalization, and deployment. The paper reframes privacy from “regulatory obligation” to “product design.” PETs shouldn’t be bolted on at the end just to manage compliance risk; they should be part of the system architecture from the start. The approach is: map where personal data enters the model at each stage, identify the specific privacy risks in each of those stages, and then apply targeted protections in data handling, training, and production. The framework is built around a three-way decision: privacy, utility, and cost. Teams are expected to intentionally choose the combination of PETs that offers protection without breaking product value or user experience. The whitepaper also categorizes PETs by phase: 📃Data layer: PII removal, deduplication, anonymization, synthetic data with differential privacy. ⚙️Training: differential privacy during optimization, federated learning, MPC, trusted execution environments to reduce memorization and internal exposure. 🚀Deployment: input/output filtering, secure runtime environments, on-device processing, and computation over encrypted data to protect prompts and responses in production. Finally, the document introduces the idea of creating “well-lit paths”: reusable engineering and governance patterns that make privacy part of the core infrastructure instead of something manually reinvented by each team. It’s a useful read for anyone looking to understand, in practical terms, how to apply PETs when assessing and deploying AI models.

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

🔐 Ever feel like privacy in AI is all theory, no tools? Just finished reading Privacy-Enhancing and Privacy-Preserving Technologies in AI by Centre for Information Policy Leadership (CIPL) —and honestly, I wish more resources were written like this. 📚 It’s practical. 📋 It’s structured. And it turns complex tech into clear, usable guidance. 💡 Why it matters? AI governance often gets stuck in high-level debates while real teams ask: What do we actually do on Monday? This guide answers that—breaking down PETs like synthetic data, differential privacy, homomorphic encryption, federated learning, and secure multi-party computation into digestible, real-world applications across the AI lifecycle. What I loved most: – Use-case driven structure (training, validation, deployment) – Real case studies from companies like Meta, Google, and Mastercard – Clear explanation of trade-offs (privacy vs utility) – Honest look at implementation challenges It’s not hype. It’s not vague. It’s a toolkit for people actually doing the work. If you’re serious about operationalizing Privacy by Design in AI, this is a must-read. Which PET are you most curious to try in practice? #PrivacyEngineering #PETs #DataGovernance #AICompliance #AIGovernance __________________________________ Did you like this post? Connect or Follow 🎯 Jakub Szarmach, AIGP, CIPM Want to see all my posts? Ring that 🔔

Engineers love to build for scale, but ignore privacy until legal comes knocking. This costs MILLIONS. When engineers design data systems, privacy is often an afterthought. I don’t blame them. We aren’t taught privacy in engineering schools. We learn about performance, scalability, and reliability - but rarely about handling consent, compliance, or privacy by design. This creates a fundamental problem: We build data systems as horizontal solutions meant to store and process any data without considering the special requirements of CUSTOMER data. As a result, privacy becomes a bolt-on feature. This approach simply DOES NOT WORK for customer data. With customer data, privacy needs to be a first-class citizen in your architecture. You need to: 1. Track consent alongside every piece of customer data throughout the entire lifecycle 2. Build identity resolution with privacy in mind 3. Design data retention policies from day one 4. Implement access controls at a granular level When privacy is an afterthought, you'll always have leaks. And in today's regulatory environment, those leaks can cost millions. The solution isn't complicated, but it requires a shift in mindset. Start by recognizing that customer data isn't like other data. It has unique requirements that must be addressed in your core architecture. Then, design your systems with privacy, consent, and compliance as fundamental requirements, not nice-to-haves.

The best privacy programs get stronger when legal and engineering work from the same view of reality. That matters because compliance risk is rarely created by policy alone. It is created when legal obligations are defined, but no one can clearly see how personal data is actually moving through systems. Legal teams ask the right questions: What data is collected? Where is it shared? What controls are in place? Engineering holds the real answers: Which service uses the data? Which API sends it out? Which logs, vendors, or models touch it after deployment? That is the gap privacy teams need to close. A few practical truths: • Data maps go stale fast • Policies do not control data flows. Code does • Reviews before launch are not enough • Evidence matters more than assumptions When legal and engineering are aligned, privacy becomes easier to operationalize, monitor, and prove. That is where mature compliance is heading. How is your team improving visibility between legal requirements and engineering systems? #legal #engineer #privacy #risk

Today is World Data Privacy Day. While today is often marked by discussions about compliance checklists and regulatory hurdles, I want to pivot the conversation toward data enginering and architecture, which is *my* world. In the rush to become "data-driven," many organizations still treat data privacy as a final gate—something applied only when a user tries to access or query data. The prevailing thought is often, "If we lock down the BI tool, the API or the warehouse, we’re safe." This is a dangerous misconception. If you are waiting until data is ready for consumption to think about privacy, it’s already too late. You cannot effectively govern what you didn't properly understand the moment it entered your world. True data leadership, I sincerely believe, requires adopting a "Privacy by Design" mindset that starts at the very point of ingestion. That's why the "Ingestor" is the most important part your data platform. We must build streams that classify, tag, and assess data sensitivity the second it appears. Is this PII? What is the lineage? What are the retention policies associated with this specific stream? If we don't address these questions at ingestion, we end up with data swamps where sensitive information is effectively hidden in plain sight, making robust downstream controls nearly impossible to automate. You can't apply dynamic masking or precise RBAC at scale if your foundational metadata is missing. Privacy isn't just a legal obligation; it’s the architectural foundation of a sustainable data strategy. Stop treating it as a final hurdle and start designing it as the bedrock of your ingestion framework. How are you "shifting left" on privacy in your data platforms? #WorldDataPrivacyDay #DataPrivacy #PrivacyByDesign #DataGovernance #DataEngineering #CISO #CDO

🔁 If you want privacy risk assessments to matter, stop treating them like paperwork and start treating them like part of how you build. Here’s what I’ve seen actually work: ⚙️ Tie the assessment trigger into product or project intake. Use a checkbox, intake form, or ticket. It doesn’t have to be fancy. 🧠 Legal and privacy shouldn’t be the only ones spotting risk. PMs, engineers, and data science leads need to know what qualifies as “high risk” and when to raise it. 📄 Risk assessments work best when they're short, specific, and reviewed alongside design or architecture; not tacked on after a decision’s already made. 📣 If you’re doing real assessments, share them internally. When others can see how a decision was made (and how a risk was handled), the org learns faster. Don't bolt privacy on at the end. Build the questions into how your team ships. You’ll get better decisions and fewer regrets. #PrivacyByDesign #RiskAssessment #ProductDevelopment #CPRA