Blockchain For Data Management

𝐈𝐟 𝐚 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫 𝐀𝐬𝐤𝐞𝐝 𝐓𝐨𝐦𝐨𝐫𝐫𝐨𝐰 𝐟𝐨𝐫 𝐚 𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐥𝐚𝐲 𝐨𝐟 𝐚 𝐒𝐢𝐧𝐠𝐥𝐞 𝐀𝐈 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧, 𝐂𝐨𝐮𝐥𝐝 𝐘𝐨𝐮𝐫 𝐒𝐲𝐬𝐭𝐞𝐦 𝐏𝐫𝐨𝐝𝐮𝐜𝐞 𝐈𝐭? The first time an auditor or legal team asks "why did the model do that?" you find out fast whether you actually built an audit trail. Most teams find out the hard way. 𝐖𝐡𝐚𝐭 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐜𝐚𝐩𝐭𝐮𝐫𝐞 𝐚𝐭 𝐞𝐯𝐞𝐫𝐲 𝐬𝐭𝐞𝐩? • Prompts: system prompts, user prompts, context retrievals, parameters. • Outputs: model responses, structured outputs, citations, confidence scores. • Tool Calls: tool name, inputs, outputs, status, errors, latency. • Model Metadata: model name and version, provider, run ID, timestamp, region. • Evaluations: automated eval results, safety checks, quality scores, red team results. • Human Feedback: review decisions, edits, overrides, rationale, labels. If a future investigation needs to know "why this response, on this day, for this user?" every one of these matters. 𝐇𝐨𝐰 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐬𝐭𝐨𝐫𝐞 𝐢𝐭? • Immutable WORM storage (Write Once, Read Many). • Encryption, partitioning, retention controls, indexing, search. • Tamper-evidence and time synchronization. Logs that can be edited aren't audit logs. They're notes. 𝐇𝐨𝐰 𝐥𝐨𝐧𝐠 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐤𝐞𝐞𝐩 𝐢𝐭? • Security and safety events: 7+ years. • Financial and regulated data: 5-7 years. • Customer interactions: 3-5 years. • Telemetry and debug: 30-90 days. • Model eval and test sets: 2-5 years. • Human feedback: 3+ years. "Keep everything forever" sounds safe. It's actually a compliance and cost problem. 𝐖𝐡𝐢𝐜𝐡 𝐫𝐞𝐠𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐫𝐞𝐪𝐮𝐢𝐫𝐞 𝐭𝐡𝐢𝐬? • EU AI Act: Record keeping, transparency, risk oversight. • SOC 2: Monitoring, incident response, change management. • ISO/IEC 42001: Monitoring, logging, continual improvement. • HIPAA: Audit controls and ePHI access integrity. • SEC/FINRA: Records, supervision, explainability. 𝐇𝐨𝐰 𝐬𝐡𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐮𝐬𝐞 𝐭𝐡𝐞 𝐥𝐨𝐠𝐬? • Audit and investigations: search and replay decisions. • Dashboards: KPIs and compliance reporting. • Operations: monitor health and drift. • Regulatory responses: produce records on demand. Logs you don't query are logs you don't have. AI accountability isn't a feature your model has. It's a record your system keeps. Build the trail first survive the audit later. 𝐂𝐨𝐮𝐥𝐝 𝐲𝐨𝐮𝐫 𝐬𝐲𝐬𝐭𝐞𝐦 𝐫𝐞𝐩𝐥𝐚𝐲 𝐚 𝐬𝐢𝐧𝐠𝐥𝐞 𝐀𝐈 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐭𝐨𝐝𝐚𝐲? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) Karuparti for more PS: Found this useful? Join 3,000+ AI architects and engineering leaders from Microsoft, Google, IBM, PwC and others reading my weekly newsletter 𝗗𝗶𝗮𝗿𝘆 𝗼𝗳 𝗮𝗻 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁. I break down real enterprise AI systems, agentic patterns, and what actually works in production. ✉️ Free subscription: https://lnkd.in/exc4upeq #AIGovernance #ResponsibleAI #AIArchitecture

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

Blockchain: The Infrastructure that Banks and Investors Should Not Ignore Recent fraud allegations that MFS (U.K.) and Tri-Color double pledged collateral across multiple lenders is nothing short of alarming. This is not a new risk. It is a structural flaw tied to fragmented systems, delayed verification, paper and e-mail trail with a reliance on representations rather than real time fact-based truth. Every loan could carry a single, immutable record of origination, ownership, lien status, and payment history. Title, servicing activity, and collateral pledges would be visible to authorized participants in real time. A loan cannot be pledged twice if the system of record enforces uniqueness at the asset level. Blockchain eliminates this vulnerability entirely. This is not theoretical; the technology exists today. Every origination event, title transfer, lien, warehouse pledge, repo, securitization and payment is recorded as an immutable, timestamped hash on a public or permissioned ledger. The record cannot be altered. Every counterparty sees it in real time. Double pledging becomes structurally impossible when a single authoritative registry marks each asset as encumbered at the moment of pledge. Warehouse lines reflect the precise collateral position at all times. Principal, interest and tax payments are logged instantaneously on the ledger. If the loan moves to repo or securitization, that event is captured sequentially, in chronological order, with zero latency and no paperwork. Investor can examine the chain of ownership and evaluate the complete payment history. The same process extends beyond loans as it is as easily applied to the securities and futures markets. Every securities transaction, repo, sale and purchase agreement benefit from instantaneous, immutable settlement as does home loans, auto loans, CRE loans, corporate loans. Jamie Dimon wrote in his April 2026 shareholders letter that JPMorgan needs to roll out its own blockchain, as Tricolor and MFS blew up from the exact fraud blockchain would have prevented. Given recent events, the cost of inaction is becoming clearer; Jamie knows this and so do the regulators. In 2026, the age of technological change, the question is not whether blockchain belongs in the credit infrastructure; the question is why it has not been widely adopted. As often is the case, Jamie is spot on in his belief.

On Protecting the Data Privacy of Large Language Models (LLMs): A Survey From the research paper: In this paper, we extensively investigate data privacy concerns within Large LLMs, specifically examining potential privacy threats from two folds: Privacy leakage and privacy attacks, and the pivotal technologies for privacy protection during various stages of LLM privacy inference, including federated learning, differential privacy, knowledge unlearning, and hardware-assisted privacy protection. Some key aspects from the paper: 1)Challenges: Given the intricate complexity involved in training LLMs, privacy protection research tends to dissect various phases of LLM development and deployment, including pre-training, prompt tuning, and inference 2) Future Directions: Protecting the privacy of LLMs throughout their creation process is paramount and requires a multifaceted approach. (i) Firstly, during data collection, minimizing the collection of sensitive information and obtaining informed consent from users are critical steps. Data should be anonymized or pseudonymized to mitigate re-identification risks. (ii) Secondly, in data preprocessing and model training, techniques such as federated learning, secure multiparty computation, and differential privacy can be employed to train LLMs on decentralized data sources while preserving individual privacy. (iii) Additionally, conducting privacy impact assessments and adversarial testing during model evaluation ensures potential privacy risks are identified and addressed before deployment. (iv)In the deployment phase, privacy-preserving APIs and access controls can limit access to LLMs, while transparency and accountability measures foster trust with users by providing insight into data handling practices. (v)Ongoing monitoring and maintenance, including continuous monitoring for privacy breaches and regular privacy audits, are essential to ensure compliance with privacy regulations and the effectiveness of privacy safeguards. By implementing these measures comprehensively throughout the LLM creation process, developers can mitigate privacy risks and build trust with users, thereby leveraging the capabilities of LLMs while safeguarding individual privacy. #privacy #llm #llmprivacy #mitigationstrategies #riskmanagement #artificialintelligence #ai #languagelearningmodels #security #risks

Personal data is highly sensitive information we entrust to internet companies, and strong regulations require these companies to handle it safely and reliably to meet security, privacy, and compliance standards. In this tech blog, Airbnb’s data science team shares how they built a data classification workflow to establish a unified strategy for identifying and classifying data across all data stores. The workflow is built on three pillars: Catalog, Detection, and Reconciliation. The Catalog pillar focuses on creating a dynamic and accurate system to identify where data resides and organize it into a comprehensive inventory. Detection addresses the question: what data might be considered personal? This step involves a detection engine structured as a pipeline to scan, validate, and control thresholds for surfacing detected results. Finally, Reconciliation ensures accurate classification by involving data owners in a human-in-the-loop process to confirm or refine detected classifications. Given the complexity of the system, the team developed metrics to assess its quality. These metrics—recall, precision, and speed—evaluate how effectively, accurately, and efficiently the classification system operates, ensuring it safeguards personal data over the long term. Additionally, the team shares strategies for governing data classification early in the process, along with best practices for improving workflows. These insights provide a clear understanding of not only the metrics but also actionable ways to enhance classification systems. Highly recommended reading for anyone interested in data governance and security. #datascience #personal #data #governance #classification #metrics – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gqxuQ29E

🚀 Delta Sharing: The Open Protocol for Secure Data Exchange Traditionally, data sharing involved providing static CSV/Parquet file dumps based on ad-hoc requests, requiring data engineers to create extracts or build complex ETL pipelines. By the time data reached recipients, it was often outdated. Additionally, moving data across organizational boundaries increased security risks and required manual auditing as well. Delta Sharing, an open protocol, solves these challenges by enabling direct, real-time data exchange while ensuring security and governance. 🔍 What is Delta Sharing? Delta Sharing is an open-source protocol that allows data providers to securely share live data from their data lake or lakehouse with any recipient, regardless of the computing platform they use. It is designed to work with Delta Lake, but it also supports other formats like Apache Parquet. 🔧 What Problems Does Delta Sharing Solve? ✅ Eliminates Data Copies – Consumers can query shared data without duplicating or exporting it into another system. ✅ Interoperability – Enables cross-platform sharing across different cloud and analytics services, including Databricks, Apache Spark, Pandas, and others. ✅ Real-time & Secure Access – Uses fine-grained access control to ensure only authorized users can access the latest version of shared data. ✅ Simplified Data Collaboration – Reduces the need for custom APIs, FTP transfers, or complex ETL workflows when sharing data with external partners. 🛠 Key Components in a Delta Sharing Scenario - Provider (Data Owner) – The entity sharing the data. - Delta Sharing Server – Handles authentication and access control. - Recipient (Data Consumer) – The entity accessing the shared data, which can be a data warehouse, a machine learning model, or a BI tool. - Storage Backend – Typically an object store (AWS S3, Azure Blob, Google Cloud Storage, MinIO) where the data resides. 📌 Common Use Cases for Delta Sharing 💡 Inter-company Data Exchange – Share supply chain, financial, or operational data with partners securely. 📊 Federated Analytics – Analysts can query live shared datasets without moving them into their own data warehouse. 🤖 Machine Learning & AI – Data scientists can directly access fresh, live data for model training without worrying about outdated extracts. ⚡ Data Monetization – Organizations can offer secure access to valuable datasets as a service without needing data pipelines. Delta Sharing + Unity Catalog Delta Sharing and Unity Catalog work together to enable secure, scalable, and governed data sharing across organizations. While Delta Sharing provides the protocol for sharing live data with external consumers, Unity Catalog acts as the central governance layer, ensuring fine-grained access control, auditing, and security compliance. I will write about this integration in the future. #deltasharing #datagovernance #datasharing

Generative AI is reshaping industries, but as Large Language Models (LLMs) continue to evolve, they bring a critical challenge: how do we teach them to forget? Forget what? Our sensitive data. In their default state, LLMs are designed to retain patterns from training data, enabling them to generate remarkable outputs. However, this capability raises privacy and security concerns. Why Forgetting Matters? Compliance with Privacy Laws: Regulations like GDPR and CCPA mandate the right to be forgotten. Training LLMs to erase specific data aligns with these legal requirements. Minimizing Data Exposure: Retaining unnecessary or sensitive information increases risks in case of breaches. Forgetting protects users and organizations alike. Building User Trust: Transparent mechanisms to delete user data foster confidence in AI solutions. Techniques to Enable Forgetting 🔹 Selective Fine-Tuning: Retraining models to exclude specific data sets without degrading performance. 🔹 Differential Privacy: Ensuring individual data points are obscured during training to prevent memorization. 🔹 Memory Augmentation: Using external memory modules where specific records can be updated or deleted without affecting the core model. 🔹 Data Tokenization: Encapsulating sensitive information in reversible tokens that can be erased independently. Balancing forgetfulness with functionality is complex. LLMs must retain enough context for accuracy while ensuring sensitive information isn’t permanently embedded. By prioritizing privacy, we can shape a future in which AI doesn’t just work for us—it works with our values. How are you addressing privacy concerns in your AI initiatives? Let’s discuss! #GenerativeAI #AIPrivacy #LLM #DataSecurity #EthicalAI Successive Digital

They tried to delete the vault. Azure said no. They tried to shorten the retention. Azure said no again. The answer is still no, for the next 99 years. In March 2026, a team discovered their Azure Recovery Services Vault had been locked with immutable WORM retention set to 99 years at setup. Apparently, a misconfiguration, a value entered or left unchecked during initial provisioning. The cost problem is precise: immutable storage in Azure Backup continues to bill you even after you stop using the vault. It bills at the immutable-storage rate for the full retention period. They tried every obvious fix. Stop protection with data deletion? Blocked. Reduce the retention policy? Blocked. Delete the vault? Blocked. Delete the resource group? The vault persists. Delete the subscription? Still no. Microsoft Support cannot override it. The vault will sit there, accumulating charges, until 2125. This is not a bug. It is working exactly as designed. WORM immutability protects backup data from ransomware or insider deletion. That protection is unconditional. The platform cannot distinguish intent from mistake. The only path forward: stop new backups, strip out linked resources, and spin up a correctly configured vault alongside the one you cannot touch. Azure Backup's immutability is one of the few cloud features where there is genuinely no recovery path from a misconfiguration. Most mistakes in Azure are expensive but reversible. This one is not. Before you enable immutability, know what retention value you are committing to. Then treat that number like a contract. Because that is exactly what it is. Link to the source in the comments. #azure #finops #cloudarchitecture #cloud

As a Lead Test Automation Engineer, I was tired of writing repetitive POJO classes with endless getters, setters, constructors, equals(), hashCode(), and toString() methods. My data classes were bloated with boilerplate code that added zero business value. **TASK:** I needed to refactor my test data models to be more maintainable and reduce code complexity while keeping the same functionality. **Steps Taken:** I migrated from traditional POJOs to Java 16 Records. Here's the transformation: ❌ BEFORE (Traditional POJO - 30+ lines): ✅ AFTER (Java Record - 1 line): **RESULT:** -> 90% less boilerplate code -> Immutable by default (thread-safe) -> Built-in equals(), hashCode(), toString() -> Cleaner, more readable codebase -> Faster development cycles 💡 **What are Java Records?** Records are a special kind of class introduced in Java 14 (stable in 16) that act as transparent carriers for immutable data. They automatically generate: • Constructor with all fields • Getter methods (no "get" prefix) • equals() and hashCode() • toString() method Perfect for DTOs, test data objects, and value classes! #Java #JavaRecords #CleanCode #TestAutomation #SoftwareDevelopment #Programming #TechTips #CodeRefactoring

𝗛𝗼𝘄 𝗮 𝗺𝗼𝗱𝗲𝗿𝗻 𝗔𝘇𝘂𝗿𝗲 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗼𝗺𝗲𝘀 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿 (𝗲𝗻𝗱 𝘁𝗼 𝗲𝗻𝗱) When people talk about 𝗺𝗼𝗱𝗲𝗿𝗻 𝗱𝗮𝘁𝗮 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀, they usually focus on tools. But platforms don’t fail because of tools. They fail because those tools 𝗱𝗼𝗻’𝘁 𝘄𝗼𝗿𝗸 𝘄𝗲𝗹𝗹 𝘁𝗼𝗴𝗲𝘁𝗵𝗲𝗿. This is a simple, battle-tested way a modern Azure data platform moves data from 𝘀𝗼𝘂𝗿𝗰𝗲 → 𝗶𝗻𝘀𝗶𝗴𝗵𝘁. 1️⃣ 𝗦𝗲𝗰𝘂𝗿𝗲 𝗶𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 𝗰𝗼𝗺𝗲𝘀 𝗳𝗶𝗿𝘀𝘁 Data starts in SQL Server and is orchestrated using Azure Data Factory. Secrets, credentials, and keys are centrally managed with Azure Key Vault. Security isn’t an afterthought. It’s built into the architecture from day one. 2️⃣ 𝗗𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗮𝘀 𝘁𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝗼𝗳 𝗿𝗲𝗰𝗼𝗿𝗱 All data lands in Azure Data Lake Gen2, structured using the Bronze–Silver–Gold pattern: • Bronze → raw, unchanged data • Silver → cleaned and validated • Gold → curated, business-ready Each layer has a clear purpose and ownership. 3️⃣ 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻𝘀 𝘄𝗶𝘁𝗵 𝗦𝗽𝗮𝗿𝗸 Azure Databricks handles transformations: • Bronze → Silver (quality & standardization) • Silver → Gold (business logic) Compute stays flexible. Logic stays reusable. 4️⃣ 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 & 𝗰𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿 Gold data is exposed through Azure Synapse Analytics and Power BI for: • Fast SQL analytics • Enterprise reporting • Interactive dashboards This is where data finally becomes decisions. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 • Clear separation of concerns • Independent scaling across layers • Secure, auditable, and debuggable • Easy to reprocess and evolve • Supports BI today and advanced use cases tomorrow This isn’t just an Azure stack. It’s a 𝗿𝗲𝗽𝗲𝗮𝘁𝗮𝗯𝗹𝗲 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻. If you’re building or modernizing a data platform: focus less on tools — and more on how they connect. ♻️ 𝗥𝗲𝗽𝗼𝘀𝘁 𝗶𝗳 𝘁𝗵𝗶𝘀 𝘄𝗮𝘀 𝗵𝗲𝗹𝗽𝗳𝘂𝗹. 🔔 Follow Aishwarya Pani for more practical Data Engineering interview insights.