Data Protection Practices

Every person should have one folder in their computer that is the "In Case of Death" folder... and this is how it is structured... The folder is used to manage and pass on one's assets to successors easily... and to avoid any legal hassles. This folder is divided into 5 parts: 1/ Personal IDs - Birth Certificate - Marriage Certificate - Aadhaar and Passport - Divorce Decrees - Change of Name Affidavits 2/ Legal & Estate Management Documents - Last Registered Will with notarized copy - Succession Certificates for Movable Assets - Guardianship Documents for minor children / dependants 3/ Financial Assets and Accounts - Bank Account details and statements - Demat Account, Shares, Mutual Funds, Investment Portfolio - Insurance Policy Documents with Beneficiaries - Outstanding loan documents - Bank Locker or Safe Deposit Locations and Access details 4/ Property Documentation - Real Estate and Business - Property Deeds and Titles for Real Estate - Mortgage Documents - Rental Agreements - Business Ownership Documents and Succession Plan 5/ Digital Assets and Access Information - Email with recovery information - Social media contact and legacy contact preferences - Domain names, Cloud Storage - Digital Subscriptions of material value The best way to manage passwords here is to provide access to Password Managers where you can add an Emergency Contact who can access all passwords in case of such conditions. There are apps like 1Password, NordPass and Keeper which offer printable PDF documents for emergency access that can be kept in this folder. These apps allow a waiting period from last activity after which the emergency contacts can use the data. Also, please ensure that you submit all such documents for probate and get them drafted by a trusted lawyer. I would also hope you would tell trusted family members about where you have stored this information so that they can access it when required. Above all, wish you all a long and healthy life. #casarthakahuja

Basics of Cybersecurity: What Every Tech Professional Must Know Today In our world, cybersecurity knowledge isn't optional anymore. Let me share some actual numbers and practical insights that matter to every Tech professional: The Big Three Threats You Need to Know: 1. Phishing attacks cause 90% of all data breaches. These aren't just spam emails - they're sophisticated scams that can fool even experienced users. The fix? Strong email filters and two-factor authentication are your best defense. 2. Ransomware isn't just about paying ransom - companies lose millions in downtime alone. Regular backups and solid recovery plans are essential, not optional. 3. DDoS attacks can shut down your entire business in minutes. Cloud-based protection and load balancing aren't fancy extras - they're basic necessities. What has really worked in 2024: - End-to-end encryption for all sensitive data - Regular security training for all staff (not just IT) - Automated threat detection tools - Continuous system monitoring The Truth: Most successful attacks exploit basic security gaps. Good security isn't about complex solutions - it's about getting the fundamentals right every single day.

Having bad data is worse than not having data at all. No data → You rely on intuition. Bad data → You make confident decisions that are simply wrong. In Software Engineering, quality checks are normal. In Data, somehow they’re often skipped. Here are 10 basic checks that will save your day: 𝟭. 𝗡𝗨𝗟𝗟𝗦 Stop letting missing values break your averages. 𝟮. 𝗨𝗡𝗜𝗤𝗨𝗘𝗡𝗘𝗦𝗦 Imagine doubling the revenue by accident! 𝟯. 𝗜𝗡𝗧𝗘𝗚𝗥𝗜𝗧𝗬 (𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝗹) Every order_id must link back to a valid customer. 𝟰. 𝗔𝗖𝗖𝗘𝗣𝗧𝗘𝗗 𝗩𝗔𝗟𝗨𝗘𝗦 Don’t let “weird” statuses creep into your reports. 𝟱. 𝗙𝗨𝗡𝗖𝗧𝗜𝗢𝗡𝗔𝗟 𝗥𝗨𝗟𝗘𝗦 Check business rules that should never be broken. 𝟲. 𝗥𝗔𝗡𝗚𝗘 Catch outliers before they skew the entire quarter. 𝟳. 𝗗𝗔𝗧𝗔 𝗧𝗬𝗣𝗘 Prevent the “Text vs Integer” nightmare. 𝟴. 𝗙𝗥𝗘𝗦𝗛𝗡𝗘𝗦𝗦 No more stale dashboards. 𝟵. 𝗧𝗘𝗠𝗣𝗢𝗥𝗔𝗟 𝗖𝗢𝗡𝗦𝗜𝗦𝗧𝗘𝗡𝗖𝗬 Time should move forward, not backward! 𝟭𝟬. 𝗡𝗨𝗟𝗟 𝗦𝗣𝗜𝗞𝗘 Spot sudden drops in data quality before they bite. Good Data is a company’s most powerful asset. Bad Data is worse than no data at all. Take Data Quality seriously! I prepared the SQL implementation👇 --- ♻️ Repost if you found it useful, please Follow 👉🏻 José for more about Data, SQL, and AI!

None will ever be able to stop Shadow AI! When people find an AI tool that helps them work faster, think better, or save time, they use it. That is what good people do. They look for leverage. Banning this is not a strategy. It is just an invitation to hide it, which is worse. So, what can we do? In my discussion with Stephen Schmidt, Chief Security Officer at Amazon, one message came through very clearly: 👉 The role of security is no longer to stop Shadow AI—because it is impossible. 👉 The role of security is to make AI safe, visible, and controlled. 👉 To know what is being used, where it is installed, what it can access, and where the data goes. That is the real shift. Because the biggest danger with AI is often not the intelligence. It is the permission. The moment an agent gets broad access to your files, systems, or sensitive data, your risk changes completely. So the question is not: “How do we stop Shadow AI?” The question is: “How do we make sure AI does not operate in the shadows?” That means four things: 1️⃣ Visibility: Create an inventory of the AI tools and agents people are actually using. 2️⃣ Boundaries: Run agents in isolated environments, not freely on laptops or production systems. 3️⃣ Permissions: Give agents only the minimum access they need, nothing more. 4️⃣ Traceability: Log actions so you know what the agent did, what data it touched, and who triggered it. This is where many leaders get it wrong. They think control means restriction. It does not. Real control means creating an environment where AI can be used fast, safely, and in the open. The companies that try to ban AI will lose visibility. The companies that learn to govern it will gain trust, speed, and advantage. You cannot stop Shadow AI. But you can stop unmanaged AI. 💥 Curious to learn more: https://lnkd.in/eubr-VpH How is your organization dealing with this today? #AWSAmbassador #AI #AgenticAI #Cybersecurity #Leadership #FutureOfWork

The EU Data Act becomes applicable from today across all EU countries. This marks a major step forward in building a fair and innovative European data economy. The Act is a key pillar of the European data strategy. It addresses the challenges and unleashes the opportunities presented by data in the EU, emphasising fair access and user rights while ensuring the protection of personal data. What changes in practice: ➡️ Connected devices on the EU market must be designed to allow data sharing ➡️ Consumers can choose more services without being locked into the manufacturer of the device ➡️ Business users in industries such as manufacturing or agriculture gain access to data on the performance of industrial equipment, boosting efficiency and innovation ➡️ Consumers can easily transfer their data and switch between cloud providers ➡️ Unfair contract terms that restrict data sharing are prohibited And more https://europa.eu/!pqXD47

AI security/securing the use of AI is going to kill me. I use Claude Code almost daily. It's a problem.... Here's what I have to change AGAIN this week. Security researcher Ari Marzuk disclosed 30+ vulnerabilities across AI coding tools. Cursor. GitHub Copilot. Windsurf. Claude Code. All of them. He called it IDEsaster. The attack chain includes prompt injection, hijacking LLM context, and auto-approved tool calls executing without permission. Then, legitimate IDE features are weaponized for data exfiltration and RCE. Your .env files. Your API keys. Your source code. Accessible through features you thought were safe. Most studies I read claim that around 85% of developers now use AI coding tools daily. Most have no idea their IDE treats its own features as inherently trusted. 𝗦𝗼... 𝗮𝗳𝘁𝗲𝗿 𝗿𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗔𝗿𝗶'𝘀 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵, 𝗵𝗲𝗿𝗲'𝘀 𝗜 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗶𝗻𝗴... Be warned: All this is SO much easier said than done! Audit every MCP server connection. Checked for tool poisoning vectors where legitimate tools might parse attacker-controlled input from GitHub PRs or web content. Removed servers I couldn't verify. Disabled auto-approve for file writes. The attack chains weaponize configuration files and project instructions like .claude/settings.json and CLAUDE.md. One malicious write to these files can alter agent behavior or achieve code execution without additional user interaction. Move all credentials to a secrets manager. No .gitignored .env files in agent-accessible directories. API keys live in 1Password CLI. Environment variables inject at runtime through a wrapper script the LLM never sees. Start running Claude Code in isolated containers. Mounted volumes limited to specific project directories. No access to ~/.ssh, ~/.aws, or ~/.config. If the agent gets compromised, blast radius stays contained. Enable all security warnings. Claude Code added explicit warnings for JSON schema exfiltration and settings file modifications. These exist because Anthropic knows the attack surface. Add pre-commit hooks for hidden characters. Prompt injections hide in pasted URLs, READMEs, and file names using invisible Unicode. Flag non-ASCII characters in any file the agent might ingest. The fix isn't to stop using AI coding tools. The fix is to stop trusting them implicitly. What controls do you have for AI tools with write access to your codebase? 👉 Follow for more AI and cybersecurity insights with the occasional rant #AISecurity #DevSecOps

When a dashboard crashes, the finger-pointing starts. Is it the Engineer? The Analyst? The Steward? Think of data governance like building a bridge. One engineer can design brilliant steel beams, but if the foundation team uses weak concrete and the inspection team skips safety checks—the bridge collapses. You can't blame the steel. 🎬 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 = 𝗔 𝗦𝘆𝗺𝗽𝗵𝗼𝗻𝘆, 𝗡𝗼𝘁 𝗮 𝗦𝗼𝗹𝗼 🔧 Engineers: Build the pipelines (the stage) 📊 Analysts: Define the metrics (the script) 🔮 Scientists: Extract insights (direct the plot) 📜 Stewards: Own data quality (manage backstage) 📈 Business: Drive decisions (deliver the finale) Miss one cue? The entire show derails. 💥 What Happens Without Governance? ❌ Wrong data flows into dashboards → Bad decisions made with confidence ❌ Silos form between teams → Duplicate work, conflicting sources ❌ Finger-pointing replaces fixing → Problems fester, trust erodes ❌ Reactive patches, not root fixes → Same fires, different day The Damage: When Governance Fails → $12.9M lost per organization annually (as per Gartner's research) Wasted spend. Bad decisions. Endless rework. Missed opportunities. → 15-25% revenue leakage Decisions made on incomplete data. Inconsistent sources. Duplicate records. → $4.5M average breach cost (2025) U.S. and U.K.? Often $9-10M per incident. Security isn't optional anymore. → $3.1T drained from U.S. economy Failed initiatives. Wasted effort. Poor quality compounds across industries. 𝘕𝘰𝘵 𝘢 𝘵𝘦𝘤𝘩 𝘱𝘳𝘰𝘣𝘭𝘦𝘮. 𝘈 𝘵𝘦𝘢𝘮𝘸𝘰𝘳𝘬 𝘱𝘳𝘰𝘣𝘭𝘦𝘮. 💡 The Fix: Build Systems, Not Silos ✅ Automate quality checks → Catch issues before analysts do ✅ Track lineage & metadata → Know where data comes from, where it goes ✅ Design for observability → Monitor pipelines like you monitor apps ✅ Embed compliance early → Privacy isn't a checkbox—it's architecture ✅ Break down role barriers → Engineers, analysts, stewards—one team 🎯 The Bottom Line Governance isn't bureaucracy. It's the blueprint for data systems that don't crumble under pressure. 𝘎𝘳𝘦𝘢𝘵 𝘥𝘢𝘵𝘢 𝘪𝘴𝘯'𝘵 𝘣𝘶𝘪𝘭𝘵 𝘪𝘯 𝘪𝘴𝘰𝘭𝘢𝘵𝘪𝘰𝘯—𝘪𝘵'𝘴 𝘰𝘳𝘤𝘩𝘦𝘴𝘵𝘳𝘢𝘵𝘦𝘥 𝘵𝘰𝘨𝘦𝘵𝘩𝘦𝘳. What's your biggest data governance challenge? Drop it below. 👇

🚨 AI Privacy Risks & Mitigations Large Language Models (LLMs), by Isabel Barberá, is the 107-page report about AI & Privacy you were waiting for! [Bookmark & share below]. Topics covered: - Background "This section introduces Large Language Models, how they work, and their common applications. It also discusses performance evaluation measures, helping readers understand the foundational aspects of LLM systems." - Data Flow and Associated Privacy Risks in LLM Systems "Here, we explore how privacy risks emerge across different LLM service models, emphasizing the importance of understanding data flows throughout the AI lifecycle. This section also identifies risks and mitigations and examines roles and responsibilities under the AI Act and the GDPR." - Data Protection and Privacy Risk Assessment: Risk Identification "This section outlines criteria for identifying risks and provides examples of privacy risks specific to LLM systems. Developers and users can use this section as a starting point for identifying risks in their own systems." - Data Protection and Privacy Risk Assessment: Risk Estimation & Evaluation "Guidance on how to analyse, classify and assess privacy risks is provided here, with criteria for evaluating both the probability and severity of risks. This section explains how to derive a final risk evaluation to prioritize mitigation efforts effectively." - Data Protection and Privacy Risk Control "This section details risk treatment strategies, offering practical mitigation measures for common privacy risks in LLM systems. It also discusses residual risk acceptance and the iterative nature of risk management in AI systems." - Residual Risk Evaluation "Evaluating residual risks after mitigation is essential to ensure risks fall within acceptable thresholds and do not require further action. This section outlines how residual risks are evaluated to determine whether additional mitigation is needed or if the model or LLM system is ready for deployment." - Review & Monitor "This section covers the importance of reviewing risk management activities and maintaining a risk register. It also highlights the importance of continuous monitoring to detect emerging risks, assess real-world impact, and refine mitigation strategies." - Examples of LLM Systems’ Risk Assessments "Three detailed use cases are provided to demonstrate the application of the risk management framework in real-world scenarios. These examples illustrate how risks can be identified, assessed, and mitigated across various contexts." - Reference to Tools, Methodologies, Benchmarks, and Guidance "The final section compiles tools, evaluation metrics, benchmarks, methodologies, and standards to support developers and users in managing risks and evaluating the performance of LLM systems." 👉 Download it below. 👉 NEVER MISS my AI governance updates: join my newsletter's 58,500+ subscribers (below). #AI #AIGovernance #Privacy #DataProtection #AIRegulation #EDPB

Read Cyber Secured India's comment published in The Times Of India on a new ZIP file scam targeting businesses. Cybercriminals are constantly evolving. Their latest tactic combines social engineering, malware, and impersonation to trick employees into authorising fraudulent financial transactions. In the recent cases reported by The Times of India, two companies collectively lost ₹3.48 crore after employees received a malicious ZIP file. Once opened, the attackers reportedly gained control of the victim's mobile device, manipulated contacts, impersonated senior management on WhatsApp, and convinced finance teams to transfer funds. I'm pleased to have shared my insights with The Times of India on this emerging threat. As I mentioned in the article: While these attacks targeted mobile and computer devices, similar malicious files can also compromise desktops and laptops, potentially causing significant damage to an entire organisation. Every organisation should immediately implement: ✅ Verify all payment requests through an independent communication channel. ✅ Never open ZIP files or attachments received from unknown or unverified sources. ✅ Deploy Endpoint Detection & Response (EDR) and Mobile Device Management (MDM) solutions. ✅ Conduct regular cybersecurity awareness training for all employees—especially finance and HR teams. ✅ Establish a culture where employees are encouraged to verify unusual requests, regardless of the sender's designation. Cybersecurity is no longer just an IT responsibility, it's a business risk that every employee must understand. 📰 Read the full article here: https://lnkd.in/eDDQHSdg #CyberSecurity #CyberCrime #CyberAwareness #BusinessSecurity #WhatsAppScam #Malware #SocialEngineering #DigitalForensics #CyberSecuredIndia #SafeSkillz #InformationSecurity #FraudPrevention #CyberHygiene #CyberThreats