Best Practices In Technology

Think Before You Share: The Hidden Cybersecurity Risks of Social Media 🚨🔐 In an era where data is the new currency, every post, check-in, or status update can serve as an intelligence goldmine for cybercriminals. What seems like harmless sharing—your vacation photos, workplace updates, or even a "fun fact" about your first pet—can be weaponized against you. 🔥 How Oversharing Exposes You to Cyber Threats 🔹 Geo-Tagging & Real-Time Location Leaks Sharing your location makes you an easy target. Cybercriminals use this data to track routines, monitor absences, or even launch physical security threats such as home burglaries. 🔹 Social Engineering & Credential Harvesting Those "what’s your mother’s maiden name?" or "which city were you born in?" quiz posts are a hacker’s playground. Attackers scrape these responses to guess password security questions or craft highly convincing phishing emails. 🔹 Metadata & Digital Fingerprinting Every photo you upload contains EXIF metadata (including GPS coordinates and device details). Attackers can extract this information, identify locations, and even map out behavior patterns for targeted cyberattacks. 🔹 OSINT (Open-Source Intelligence) Reconnaissance Threat actors don’t need sophisticated hacking tools when your social media profile provides a full dossier on your life. They correlate job roles, connections, and public interactions to execute whaling attacks, corporate espionage, or deepfake impersonations. 🔹 Dark Web Data Correlation Your exposed social media details can be cross-referenced with breached databases. If your credentials have been compromised in past data leaks, attackers can launch credential stuffing attacks to hijack your accounts. 🔐 Cyber-Hygiene: Best Practices for Social Media Security ✅ Restrict Profile Visibility – Limit exposure by setting profiles to private and segmenting audiences for sensitive updates. ✅ Sanitize Metadata Before Uploading – Use tools to strip EXIF data from images before posting. ✅ Implement Multi-Factor Authentication (MFA) – Enforce adaptive authentication to prevent unauthorized account access. ✅ Zero-Trust Mindset – Assume any publicly shared data can be aggregated, exploited, or weaponized against you. ✅ Monitor for Breach Exposure – Regularly check if your credentials are compromised using breach notification services like Have I Been Pwned. 🔎 The Internet doesn’t forget. Every post contributes to your digital footprint—control it before someone else does. 💬 Have you ever reconsidered a social media post due to security concerns? Drop your thoughts below! 👇 #CyberSecurity #SocialMediaThreats #Infosec #PrivacyMatters #DataProtection #Phishing #CyberSecurity #ThreatIntelligence #ZeroTrust #CyberThreats #infosec #cybersecuritytips #cybersecurityawareness #informationsecurity #networking #networksecurity #cyberattacks #CyberRisk #CyberHygiene #CyberThreats #ITSecurity #InsiderThreats #informationtechnology #technicalsupport

Maybe you can WRITE SQL, but are you writing ✨GOOD SQL✨? SQL is more than just writing a query without errors… Here’s 10 query optimization tips: 1. Avoid SELECT * and instead list desired columns 2. Use INNER JOINs over LEFT JOINs when applicable 3. Use WHERE and LIMIT to filter rows 4. Filter as much as possible as early as possible (consider the order of execution) 5. Avoid ORDER BY (especially in subqueries and CTEs) 6. Avoid using DISTINCT unless necessary (especially when it’s already implied like in GROUP BY & UNION) 7. Use CTEs when you’ll have to refer to a table/ouput multiple times 8. Avoid using wildcards at the beginning of a string (‘%jess%’ vs. ‘jess%’) 9. Use EXISTS instead of COUNT and IN 10. Avoid complex logic Obviously you can’t ALWAYS avoid these, and they each have their use cases, but these are good things to think about when optimizing your queries.

I just read the McKinsey and NACD panel on the Board's role in managing AI risk. One line from Guy Gecht, Board Chair at Logitech, was impactful: “For 25 years, we protected data. Now, we need to protect judgment.” The Boards that win will protect good judgment and build more of it. Those figures are a wake-up call:   • 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘄𝗶𝘁𝗵 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗮𝗻𝗱 𝗔𝗜-𝘀𝗮𝘃𝘃𝘆 𝗕𝗼𝗮𝗿𝗱𝘀 𝗯𝗲𝗮𝘁 𝗽𝗲𝗲𝗿𝘀 by 10.9 points on return on equity (MIT, 2025)   • Only 𝟴% 𝗼𝗳 𝟯,𝟬𝟬𝟬 𝗨𝗦 𝗟𝗶𝘀𝘁𝗲𝗱 𝗙𝗶𝗿𝗺𝘀 𝗱𝗶𝘀𝗰𝗹𝗼𝘀𝗲 𝗮 𝗕𝗼𝗮𝗿𝗱-𝗹𝗲𝘃𝗲𝗹 𝗔𝗜 oversight (ISS, January 2026)   • 𝟳𝟴% 𝗼𝗳 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁 𝘁𝗵𝗲𝘆 𝗰𝗼𝘂𝗹𝗱 𝗽𝗮𝘀𝘀 𝗮𝗻 𝗶𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁 𝗔𝗜 governance audit (Grant Thornton, 2026) Better judgment is now a competitive edge, not a compliance task. Four ways to build it: ✅ 𝗕𝘂𝗶𝗹𝗱 𝘁𝗵𝗲 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗼𝗳 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁   • Invite outside experts twice a year   • Map Board skills against where the company is going, then close the gaps   • P&G, a consumer goods company (not a tech firm!) runs a Board Innovation and Technology Committee ✅ 𝗗𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗮𝘁 𝗔𝗜 𝗰𝗮𝗻 𝗷𝘂𝗱𝗴𝗲   • Define where AI acts alone, where a human decides, and who is accountable if AI fails   • Test agents and plan for when they act wrongly   • Meta's internal agent published wrong information and exposed data in March 2026 ✅ 𝗦𝗲𝗰𝘂𝗿𝗲 𝘀𝗽𝗮𝗰𝗲 𝗳𝗼𝗿 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗼𝗻 𝘁𝗵𝗲 𝗮𝗴𝗲𝗻𝗱𝗮   • Move AI out of a crowded audit agenda into a committee with the right people   • Track AI risk continuously, not once a quarter   • United Airlines runs AI risk in real time, not a quarterly look-back. Problems are addressed as they appear. ✅ 𝗦𝗵𝗮𝗿𝗽𝗲𝗻 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗯𝗲𝘁𝘁𝗲𝗿 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲   • Review how an AI decision was made, and trace what went wrong   • Ask management for the cost and the return, not soft productivity stories   • California's new law, effective Jan. 2026, removes “AI did it” as a defense. Boards must be able to explain the decision Good Boards protect judgment. Great Boards keep growing it. 💡 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗼𝗻𝗲 𝘁𝗵𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗕𝗼𝗮𝗿𝗱 𝗱𝗼𝗲𝘀 𝘁𝗼 𝗸𝗲𝗲𝗽 𝗶𝘁𝘀 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘀𝗵𝗮𝗿𝗽? #BoardDirectors #CorporateGovernance #AI #BoardEffectiveness #RiskManagement #Leadership

How To Handle Sensitive Information in your next AI Project It's crucial to handle sensitive user information with care. Whether it's personal data, financial details, or health information, understanding how to protect and manage it is essential to maintain trust and comply with privacy regulations. Here are 5 best practices to follow: 1. Identify and Classify Sensitive Data Start by identifying the types of sensitive data your application handles, such as personally identifiable information (PII), sensitive personal information (SPI), and confidential data. Understand the specific legal requirements and privacy regulations that apply, such as GDPR or the California Consumer Privacy Act. 2. Minimize Data Exposure Only share the necessary information with AI endpoints. For PII, such as names, addresses, or social security numbers, consider redacting this information before making API calls, especially if the data could be linked to sensitive applications, like healthcare or financial services. 3. Avoid Sharing Highly Sensitive Information Never pass sensitive personal information, such as credit card numbers, passwords, or bank account details, through AI endpoints. Instead, use secure, dedicated channels for handling and processing such data to avoid unintended exposure or misuse. 4. Implement Data Anonymization When dealing with confidential information, like health conditions or legal matters, ensure that the data cannot be traced back to an individual. Anonymize the data before using it with AI services to maintain user privacy and comply with legal standards. 5. Regularly Review and Update Privacy Practices Data privacy is a dynamic field with evolving laws and best practices. To ensure continued compliance and protection of user data, regularly review your data handling processes, stay updated on relevant regulations, and adjust your practices as needed. Remember, safeguarding sensitive information is not just about compliance — it's about earning and keeping the trust of your users.

I'm sharing a breakdown of SQL commands that I find essential for anyone working with databases. Let's dive deep into each category: 1. Data Definition Language (DDL) These are your building blocks for database structure: - CREATE: For new tables, views, or databases - ALTER: Modify existing structures - DROP: Remove objects completely - TRUNCATE: Clear data while keeping structure 2. Data Manipulation Language (DML) Your daily drivers for handling data: - INSERT: Add new records - UPDATE: Modify existing data - DELETE: Remove specific records 3. Transaction Control Language (TCL) Critical for maintaining data integrity: - SAVEPOINT: Create checkpoints in transactions - COMMIT: Make changes permanent - ROLLBACK: Undo changes to the last savepoint 4. Data Control Language (DCL) Essential for security management: - GRANT: Assign permissions - REVOKE: Remove access rights 5. Data Query Language (DQL) The powerhouse of data retrieval: - SELECT: The foundation of all queries Advanced Features: Aggregate Functions: - COUNT: Record counting - SUM: Total calculations - AVG: Mean values - MAX/MIN: Range Identification - GROUP BY: Data grouping - HAVING: Filter grouped results - ORDER BY: Result sorting Filtering Techniques: - WHERE: Basic conditions - AND/OR: Complex logic - BETWEEN: Range filtering - LIKE: Pattern matching - IN: Multiple value checks - IS NULL/IS NOT NULL: Null handling Data Combination Methods: - INNER JOIN: Matching records - LEFT JOIN: All records from left+ matches - RIGHT JOIN: All records from right + matches - FULL JOIN: All records from both tables - SELF JOIN: Table to itself - UNION: Combine result sets 💡 Pro Tips: 1. Always start with proper indexing for performance 2. Use transactions for data integrity 3. Master JOIN operations for complex queries 4. Understand query execution plans 5. Practice defensive programming with appropriate error handling These commands form the foundation of efficient database management, whether building enterprise applications, analyzing big data, or managing small databases. What's your most challenging SQL scenario?

Robot safety isn't optional. ⚠️ The person in this video walked away. The Reality: - 41 robot-related deaths in US workplaces over 26 years (1992-2017) - 77 serious injuries reported to OSHA (2015-2022) - Most fatalities happen during maintenance - unjamming, cleaning, troubleshooting The numbers are low. But anything above zero is unacceptable. Best Practices to Prevent This: 1. Physical Barriers 🚧 Light curtains, safety fences, and guards. If a human enters the zone, the robot stops. 2. Lockout/Tagout 🔒 Power down and lock the robot during maintenance. Most deaths happen when someone thinks "I'll just quickly fix this." 3. Speed & Force Limiting ⚡ Collaborative robots should operate at reduced speed around humans. Impact force limits matter. 4. Training 👷 Every person near a robot needs to understand the danger zones and emergency stops. 5. Risk Assessment 📋 Map every scenario where human-robot interaction occurs. Design safety systems accordingly. The Bottom Line: That 99.998% uptime means nothing if someone gets injured or dies.

Day 5 – Building Your #AI Governance Team (You Need More Than Just Ethicists) Most companies think AI #governance = hire a Chief Ethics Officer and call it a good day. Nah. You don't need philosophers. You need operators. Sorry Reid, no shade. After working on #tech governance at Meta, Microsoft, and DHS, here's who you actually need on your governance squad: 1/ I start with a Technical Lead who speaks both tech and business 👩💻 Not just someone who can code ↳ 📔 Someone who can translate "bias detected" into "this costs us $2M in discrimination risk" ⚒️ They're your BS detector for vendor claims 2/ I get a Legal/Compliance partner who actually gets technology 📃 Not your general corporate counsel who thinks AI is magic (no shade to my lawyer friends, you know how much I love you 💕 🎰 Someone who understands how algorithms actually work ✔️ They turn vague regulations into actionable checklists 3/ I find a Product Owner who has skin in the game 💰 Someone whose bonus depends on the AI systems working 👨🏫 Not just an ethics committee observer 💨 They ensure governance enables innovation instead of killing it 4/ I bring in Risk/Audit people who play offense (my people) ↳ Someone who asks "what could go wrong?" not "does this check the box?" ↳ They stress-test your systems before the real world does ↳ They find the edge cases your team missed 5/ I secure Executive Air Cover who can make hard calls ↳ Someone who can say "shut it down" when things go sideways, not an indecisive wimp ↳ Not a committee that needs three meetings to decide anything ↳ They have authority to stop revenue-generating systems The Magic Formula Technical lead identifies the problem Legal partner defines the requirements Business owner weighs the trade-offs Risk function stress-tests the solution Executive makes the final call But Here's What Most Companies Screw Up... They build governance as a separate function that "reviews" AI projects. Wrong!!! Your governance team should be embedded in development. Not external reviewers, not implementers, not doers. Internal partners. The One Role You DON'T Need (Yet) A dedicated ethicist. I'm so SORRY!!! Controversial, I know. But ethics without implementation is just expensive philosophy. Start with operators who can build working systems. Add the ethicist later when you need philosophical guidance on complex edge cases. Your governance team isn't about having the smartest people in the room. It's about having the right people who can actually ship stuff, and be empowered to make decisions and not too scared to make mistakes because they're the fall person. I wouldn't take a job where I'm set up for failure like that, though I do have a price 😜 💰 Tomorrow: Your first 30 days (what to build when you're starting from zero). What's your biggest team gap right now? TLDR; Governance problems are usually people problems. #responsibleai #aigovernance #algorithmsarepersonal #teambuilding

𝗪𝗵𝘆 𝗱𝗼 𝘀𝗼 𝗺𝗮𝗻𝘆 𝗘𝗥𝗣 𝗺𝗶𝗴𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗮𝗶𝗹? 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝘁𝗿𝗲𝗮𝘁 𝗶𝘁 𝗹𝗶𝗸𝗲 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗽𝗮𝘁𝗰𝗵, not the business transformation it truly is. Listening to my network, there seems to be a rush to complete ERP migrations, as fast as possible, with SAP S/4HANA plans driving most of it. But an ERP system is more than just an IT upgrade. It’s a chance to redesign how your business operates and build a solution architecture that supports agility and innovation. While necessary, these migrations often become redundant without proper alignment to business goals. Something, I've seen happen! Here some get rights to consider: ◉ 𝗔𝗹𝗶𝗴𝗻 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝘁𝗲𝗰𝗵 𝗴𝗼𝗮𝗹𝘀 Ensure that IT and business leaders are on the same page. ERP systems serve broader business objectives, such as innovation, improving procurement strategies, and enhancing supplier relationships. ◉ 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗼𝘂𝘁𝗰𝗼𝗺𝗲𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗼𝗼𝗹𝘀. Instead of getting caught up in the technology itself, be clear about the business benefits you'd like to achieve. New ERP functionality can be of support to achieve goals like efficiency, cost reduction, and agility. ◉ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗻𝗱 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 𝗲𝗻𝗱-𝘁𝗼-𝗲𝗻𝗱 Don't just migrate complex, outdated processes but streamline them end-to-end. Reevaluate processes for efficiency and desired outcomes. ◉ 𝗜𝗻𝘃𝗲𝘀𝘁 𝗶𝗻 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 - 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗶𝗻 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 ERP migrations often fail due to poor user adoption. Beyond training, invest in communication & ongoing support showing the value and relevance of the system to users. ◉ 𝗜𝗻𝘃𝗼𝗹𝘃𝗲 𝗰𝗿𝗼𝘀𝘀-𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗲𝗮𝗺𝘀 ERP impacts every area of the business, so cross-team collaboration is essential. Involve stakeholders from finance, procurement, IT, and operations ensures the system meets everyone’s needs. ◉ 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 - 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗰𝗼𝗺𝗽𝗿𝗼𝗺𝗶𝘀𝗲 An ERP system is only as good as the data it processes. Ensure that data is clean, consistent, and reliable before migration. Dirty or incomplete data is one of the biggest challenges post-go-live. ◉ 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘀𝗲 𝗦𝘆𝘀𝘁𝗲𝗺 𝗳𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗼𝘀𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Choose an architecture which allows for future-proofing and integration of new features, scalability and integration. Business models evolve, and your ERP must evolve with them." ◉ 𝗦𝗲𝘁 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝘁𝗶𝗺𝗲𝗹𝗶𝗻𝗲𝘀 - 𝗶𝘁'𝘀 𝗻𝗼𝘁 𝗴𝗼𝗶𝗻𝗴 𝘁𝗼 𝗯𝗲 𝗾𝘂𝗶𝗰𝗸 𝗶𝗳 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲 Don’t rush an implementation. ERP migrations are complex and require time to integrate properly. A phased approach allows for troubleshooting and mitigates a risk for failure. ❓Any other "get rights" i missed and you would add from your experience. #erp #businesstransformation #migration #sap4hana

Inflation isn’t just an economic challenge—it’s a test of agility for businesses. As costs rise and purchasing power shifts, companies that rely on gut instinct risk falling behind. The real winners? Those who use data-driven insights to navigate uncertainty. 1️⃣ Understanding Consumer Behavior: What’s Changing? Inflation reshapes spending habits. Some consumers trade down to budget-friendly options, while others delay non-essential purchases. Businesses must analyze: 🔹 Spending patterns: Are customers shifting to smaller pack sizes or private labels? 🔹 Channel preferences: Is there a surge in online shopping due to better deals? 🔹 Regional variations: Inflation doesn’t hit all demographics equally—hyperlocal data matters. 📊 Example: A retail chain used real-time sales data to spot a shift toward economy brands, allowing it to adjust promotions and retain price-sensitive customers. 2️⃣ Pricing Trends: Data-Backed Decision-Making Raising prices isn’t the only response to inflation. Smart pricing strategies, backed by AI and analytics, can help businesses optimize margins without losing customers. 🔹 Dynamic pricing models: Adjust prices based on demand, competitor moves, and seasonality. 🔹 Price elasticity analysis: Determine how much a price hike impacts sales before making a move. 🔹 Personalized discounts: Use customer data to offer targeted promotions that drive loyalty. 📈 Example: An e-commerce platform analyzed customer behavior and found that small, frequent discounts led to better retention than infrequent deep discounts. 3️⃣ Demand Forecasting & Inventory Optimization Stocking the right products at the right time is critical in an inflationary market. Predictive analytics can help businesses: 🔹 Anticipate demand surges—especially in essential goods. 🔹 Optimize supply chains to reduce excess inventory and prevent stockouts. 🔹 Reduce waste in perishable categories like F&B, where price-sensitive demand fluctuates. 📦 Example: A leading FMCG brand leveraged AI-driven demand forecasting to prevent overstocking of premium products while ensuring budget-friendly variants were always available. 💡 The Takeaway Inflation isn’t just about rising costs—it’s about shifting consumer priorities. Companies that embrace data-driven decision-making can optimize pricing, fine-tune inventory, and strengthen customer loyalty. 𝑯𝒐𝒘 𝒊𝒔 𝒚𝒐𝒖𝒓 𝒃𝒖𝒔𝒊𝒏𝒆𝒔𝒔 𝒂𝒅𝒂𝒑𝒕𝒊𝒏𝒈 𝒕𝒐 𝒊𝒏𝒇𝒍𝒂𝒕𝒊𝒐𝒏𝒂𝒓𝒚 𝒑𝒓𝒆𝒔𝒔𝒖𝒓𝒆𝒔? 𝑨𝒓𝒆 𝒚𝒐𝒖 𝒖𝒔𝒊𝒏𝒈 𝒅𝒂𝒕𝒂 𝒕𝒐 𝒓𝒆𝒇𝒊𝒏𝒆 𝒚𝒐𝒖𝒓 𝒔𝒕𝒓𝒂𝒕𝒆𝒈𝒚? 𝑳𝒆𝒕’𝒔 𝒅𝒊𝒔𝒄𝒖𝒔𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #datadrivendecisionmaking #dataanalytics #inflation #inventoryoptimization #demandforecasting #pricingtrends

Navigating the Intersection of Technology, Risk and Governance : 🔸 In the modern boardroom, the siloed approach of considering "IT issues," "compliance", "corporate strategy", "financial numbers" as distinct chapters is retreating. ✔️ As an advisor and Independent Director specializing in #TechReg , cyber and governance, I spend my time at the intersection of these three forces. In the automated, AI-driven world where #innovation needs to match steps with #trust, these forces are merged into a single, complex narrative, where the Boards need to view TechReg not as a hurdle, but intertwined onto the financial, risk and strategy discussion rooms (or committees) as gear-throttle-break that can take the business forward in the desired speed. 🔸 The "governance" piece is currently being tested by Generative AI. We are at crossroads where the pressure to adopt AI to stay relevant is clashing with the need for ethical guardrails and data integrity. ✔️ I advocate a "Governance by Design" framework, wherein oversight and controls are considered and incorporated at the inception of a project, rather than as a bolt-on after say, the software has been deployed. 🔸 Cybersecurity has graduated from the server room to the boardroom, thanks to the guidelines / mandates from key Indian regulators such as RBI, SEBI, IRDAI. However, the challenge I still see is the use of technical jargon, whereby conversations may get stuck. ✔️ I often play the role to 'translate' such tech terms into business and fiduciary 'English'; example "zero-trust architecture" and "endpoint detection" into automated controls built in to ensure that users need to prove their approved rights and authority to access systems, and, controls in the employees' systems to monitor, detect, intimate for any virus, malware etc. 🔸 Effective #cyber #governance involves asking not just questions such as 'are we secure'. ✔️ I help the Boards review detailed presentations, with impact analysis, financial numbers, risk rating et all, on say, how long can we survive a total systems outage, and steps-roles-procedures to recover from the same. ✔️ As an Independent Director, my goal is to ensure that the Board doesn't just "oversee" technology and financial ratios but truly understand how they should talk in sync and become a fundamental value driver in a digital first business. 🔸 With the world moving towards prescriptive technology regulation in the face of increasing number and category of threats, whether RBI, SEBI, IRDAI, DPDP Act and international rules such as DORA, EU AI Act et all, #compliance has moved from a back-office function into competitive advantage. ✔️ I help the Board to take a multi-directional lens to assess, say, how tech scalability and operational risk appetite fit into the 5-year business growth plan; to build the bridge between tech governance and financial balance sheet. #cyberboarddirector #cybersecurity #technology #riskmanagement #digitaltransformation