Privacy Considerations in Pipeline Design

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

👩⚕️ 👨⚕️ Building AI That Heals - Without Exposing Patient Data 📍Patient Data holds enormous amounts of valuable data to train AI models for better and more efficient healthcare support. However privacy issues hold back on having such data utilized in parctise to actually take advantage of what technology can offer for healthcare 🚨 The paper, “A Privacy-Preserving Ecosystem for Developing Machine Learning Algorithms Using Patient Data” (Süwer et al., 2025), provides a solution in form of a concept for a secure, collaborative framework. ✅ Developed during the TUM.ai Makeathon, the project integrates knowledge graphs, federated learning, and synthetic data generation to enable AI model training without centralising sensitive patient data. The outcome is quite promising: 👉 The developed ecosystem shows that privacy‐preserving pipelines are feasible in clinical contexts: the use of knowledge graphs and federated/synthetic methods enabled model development without full centralised raw patient data aggregation 👉 This ecosystem offers a roadmap to unlock sensitive clinical data for AI while maintaining privacy, compliance, and trustworthy governance 👉 Each participating site retains data control while still contributing to shared learning, a crucial step for rare-disease research and small clinical datasets. 🐣 Why it matters: This approach shows that privacy and innovation are not opposing forces. With the right design, structured data representation, decentralised computation, and traceable pipelines, healthcare AI can evolve within the boundaries of GDPR and ethical governance. 🎯 Bottom Line for developers, policymakers, and research institutions: this is a model for how to use patient data responsibly by privacy-preserving design. 🤓 So we do not have to compromise in Healthcare AI and move on to reap the benefits form rich data sources. This approach is a master class example for other critical domanis as well. 💡 It also is a best practise example what practice focused Hackathons can deliver. 🔗 to the paper in the comments #artificialintelligence #healthcare #GPPR #data #innovation #hachkathon

🔐 Row-Level Security in Snowflake: Real Tips from Building Healthcare Pipelines When you're dealing with healthcare data — claims, EMRs, clinical logs — row-level access isn’t just nice to have. It’s required. At UnitedHealth Group, we handled patient-level datasets across providers, payers, and care teams. That meant building secure, auditable pipelines — and Snowflake’s row access policies became a key part of the architecture. Here’s what we learned: -->Use conditional expressions in policies tied to user roles and departments — don’t hardcode logic outside the platform. -->Group access via RBAC using role hierarchies mapped to Snowflake roles, not just users. --> Leverage CURRENT_ROLE() and SESSION_USER() — they’re gold for dynamic policy control. --> Keep policies centralized and documented in Confluence + Git, version-controlled like your pipeline code. --> Audit regularly — we tracked access violations and policy mismatches using Snowflake logs + CloudWatch alerts. This wasn’t just about compliance (HIPAA, SOC2). It was about building trust across analytics teams — knowing that cardiology teams only see cardiology data, and everything else is locked down. Security isn’t a feature — it’s a design principle. #DataEngineering #Snowflake #RowLevelSecurity #HealthcareData #HIPAA #DataGovernance #BigData #RBAC #ETL #SecurityEngineering #Python #SQL #CloudComputing #UHG #Compliance #SecurePipelines #DataPlatform

𝐘𝐨𝐮𝐫 𝐑𝐀𝐆 𝐬𝐲𝐬𝐭𝐞𝐦 𝐦𝐢𝐠𝐡𝐭 𝐛𝐞 𝐥𝐞𝐚𝐤𝐢𝐧𝐠 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚 — without you even knowing it. If you're building AI apps for enterprises, PII protection isn't optional — it's critical. This guide breaks down the exact masking methods to keep your Retrieval-Augmented Generation (RAG) pipeline compliant and safe: ✅ Keyword matching ✅ Prompt engineering ✅ Context transformation ✅ Dynamic & role-based masking ✅ Chain prompting ✅ Node post-processing (LlamaIndex example) Whether you're working with legal docs, healthcare data, or multi-user platforms — this is your must-know privacy checklist. 👉 Swipe to learn how to build RAG systems that are safe, compliant, and privacy-first. ➕ Follow Naresh Edagotti for more content that makes complex AI topics feel simple