Importance Of Security Measures

In the third part of my Understanding Energy Resilience series, I want to start with something many of you will have seen in the news: recent drone disruptions at major airports. Munich having to temporarily close its airspace. Oslo halting landings. Copenhagen pausing operations for hours. These incidents showed how quickly one small object can halt a critical service, create chaos and cost millions. Now take that thought to energy. If a drone over a runway makes headlines, a drone over energy infrastructure often doesn't. Yet the consequences can be just as real: disruptions to electricity supply, halted rail services and factories forced to stop production. Across Europe, operators are not allowed to neutralize hostile drones themselves – even when a threat is visible above critical infrastructure. Simply put: the rules have not caught up with reality. In my view, clarity and speed here are essential for public safety. Next to physical threats we also face digital ones. Every hour, around 35 million cyberattacks happen worldwide – almost 10,000 every second. Around 5% of them target energy companies and infrastructure. This is the world we operate in: attacks can appear out of nowhere and put entire systems to the test in real time. From my perspective, defending energy infrastructure comes down to a few key priorities: 1️⃣ Let protection happen: Regulation needs to enable energy operators to protect themselves. Clear rules must define who can intervene, when and how – including stopping a hostile drone. We cannot afford hesitation while minutes turn into outages. 2️⃣ Treat physical and digital as one: Fences, cameras and access control on the ground. Network separation and continuous monitoring in the control room. Physical and digital security must be treated as one because if someone can walk in, they can often plug in and disrupt the system. 3️⃣ Harden the infrastructure no one can afford to lose: The majority of physical and cyberattacks on energy systems target a small number of high-impact sites – such as substations, control rooms and interconnectors. Better detection and stronger barriers here make the difference between local disturbance and national outage. 4️⃣ Practice recovery, not just prevention: Real resilience is measured in how quickly power is restored. Simple restart plans, spare parts ready on site and regular drills with operators and authorities turn days in the dark into hours. 5️⃣ Stop naivety – talk openly about risk: We need public awareness without drama – which is one of the reasons I started this series. The more people understand that drones over critical sites are serious and that malware or phishing mails are no joke, the more support there will be for sensible protection. I believe this is the right balance: clear authority to act, practical protection on the ground and in the network with a constant focus on rapid recovery. In a more contested world, that is how energy systems stay open for business.

97% of orgs faced AI breaches in 2025 had zero access controls in place. Not weak; Not outdated controls. Zero [Source: IBM] Meanwhile, 35% of real-world AI security incidents came from simple prompts some causing $100K+ in losses without a single line of code [Source: Adversa] The gap between AI deployment speed and security implementation is only widening. Hence I am sharing 10 security checkpoints every AI agent needs before touching production systems: ✅ Output Validation → Middleware that verifies decisions against rules before execution. Traffic lights for AI actions. ✅ Access Control → Least privilege enforcement. Role-based permissions that limit what agents can touch. ✅ Credential Safety → Secrets management that keeps API keys away from prompts and logs. Store them like vault keys, not sticky notes. The other 7 checks are in the carousel including rate limiting that prevents runaway loops and human approval for high-stakes decisions 👇 Most teams rush deployment. Security becomes an afterthought until something breaks. Tell me your story: what security measure has prevented a disaster in your AI system? Follow me, Bhavishya Pandit, for practical AI production insights from the trenches 🔥 #ai #security #agents

🗞️ Needed report By CyberArk on a burning issue : identity security. A decisive element that will determine our ability to restore digital trust. 🔹 « Identity is now the primary attack surface. » Defenders must secure every identity — human and machine 🔹 with dynamic privilege controls, automation, and AI-enhanced monitoring 🔹and prepare now for LLM abuse and quantum disruption. Machine identities are the fastest-growing attack surface 🔹Growth outpaces human identities 45:1. 🔹Nearly half of machine identities access sensitive data, yet 2/3of organizations don’t treat them as privileged. Quantum readiness is urgent 🔹Quantum computing will break today’s cryptography (RSA, TLS, identity tokens). 🔹Transition planning to quantum-safe algorithms must start now, even before standards are finalized. Large Language Models include prompt injection, data leakage, and misuse of AI agents. So organizations must treat them as a new class of machine identity requiring monitoring, access controls, and secrets management. 🧰 What can we do? ⚒️ 1/ Implement Zero Standing Privileges (ZSP) • Remove always-on entitlements; grant access dynamically and just-in-time. • Minimize lateral movement by revoking privileges once tasks are complete 👥2/ Secure the full spectrum of identities • Differentiate controls for workforce, IT, developers, and machines. • Prioritize machine identities: vault credentials, rotate secrets, and eliminate hard-coded keys. 🛡️ 3/ Embed intelligent privilege controls • Apply session protection, isolation, and monitoring to high-risk access. • Enforce least privilege on endpoints; block or sandbox unknown apps. • Deploy Identity Threat Detection & Response (ITDR) for continuous monitoring. ♻️ 4/ Automate identity lifecycle management • Use orchestration to onboard, provision, rotate, and deprovision identities at scale. • Relieve staff from manual tasks, counter skill shortages, and improve compliance readiness. 5/ Align security with business and regulatory drivers • Build an “identity fabric” across IAM, PAM, cloud, SaaS, and compliance. • Tie metrics (KPIs, ROI, cyber insurance conditions) to board-level priorities. 6/ Prepare for next-generation threats • Establish AI/LLM security policies: control access, monitor usage, audit logs. • Begin phased adoption of post-quantum cryptography to protect long-lived sensitive data. Enjoy the read

AI systems become risky when there are no guardrails controlling how they behave at scale. Over the years, I’ve seen teams rush into building AI capabilities— but very few spend enough time designing the systems that keep AI safe, reliable, and accountable. That’s where AI Governance & Security comes in. Think of this as the foundation layer for enterprise AI systems 👇 🔹 Identity & Access Control RBAC, ABAC, IAM, MFA, SSO—control who can access what, and under which conditions. 🔹 Data Protection Encryption, tokenization, masking, secure pipelines—protect sensitive data across its lifecycle. 🔹 Risk Management Risk scoring, bias detection, hallucination monitoring, threat intelligence—identify and reduce AI risks early. 🔹 Monitoring & Observability Real-time tracking, anomaly detection, logging—understand how your AI behaves in production. 🔹 Audit & Accountability Traceability, audit logs, documentation—ensure every decision can be reviewed and explained. 🔹 Compliance & Governance GDPR, EU AI Act, ISO 42001—align AI systems with regulatory and ethical standards. 🔹 Human Oversight HITL, approvals, escalation workflows—keep humans in control for critical decisions. A few critical patterns I’ve seen work in real systems: ✔ Define ownership of AI decisions (RESP) ✔ Enforce policies, don’t just document them ✔ Continuously monitor drift, bias, and anomalies ✔ Always maintain traceability across data and decisions ✔ Introduce human checkpoints for high-risk actions The biggest mistake? Treating AI governance as a compliance checkbox. It’s not. It’s what separates experimental AI systems from enterprise-grade, production-ready AI systems. Because in AI… it’s not just about what the model can do. It’s about how safely, reliably, and responsibly it does it at scale. Follow Vaibhav Aggarwal for more such insights!!

Your biggest cybersecurity threat might not be your employees — it might be your coffee machine. Everyone’s worried about employees clicking phishing emails… …but who’s worried about the smart thermostat leaking your sensitive data? (You should be.) When we talk about human cyber risk, it’s not just laptops and emails. It’s the people who plug in devices they don’t understand — or don’t think about — that open the backdoor. The truth is: The Internet of Things (IoT) is your weakest (and most ignored) security link. 📺 Smart TVs. 🏅 Fitness trackers. ☕ Coffee machines. 🔔 Video doorbells. 💡 Smart lighting. 🌡️ Even that “harmless” Wi-Fi-enabled fish tank thermometer in your lobby. (Yes, that actually happened to a casino in 2019 where the whole high roller database was exfiltrated through an IoT connected fish tank thermometer. Ouch.) If it connects to the internet, it can connect a threat actor to you. ACTIONABLE TAKEAWAYS: ✔️ Audit your IoT Devices: List everything in your business and home that’s internet-connected. If you don’t track it, you can’t protect it. ✔️ Segregate Networks: Keep IoT devices on a separate Wi-Fi network from business operations and sensitive information. ✔️ Change Default Credentials: Most IoT breaches happen because devices are left on factory settings. Change all passwords — immediately. ✔️ Update Firmware: Your smart devices need updates just like your computer does. Patch regularly or retire them if they’re no longer supported. ✔️ Train Your People: If they’re plugging it in, they’re opening a portal. Awareness matters. Train users to think before they connect. Bottom line: Human risk isn’t just about bad passwords and phishing clicks. It’s about our instinct to trust technology we don’t fully understand. If you employ humans, if you use IoT, you have risk. Manage your humans. Manage your tech. Or someone else will. #HumanRisk #Cybersecurity #IoTSecurity #InsiderThreat #CyberHygiene #Leadership #SecurityAwareness

𝗛𝗼𝘄 𝗜 𝗨𝘀𝗲 𝗧𝗵𝗿𝗲𝗮𝘁 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘁𝗼 𝗦𝘁𝗮𝘆 𝗔𝗵𝗲𝗮𝗱 𝗼𝗳 𝗔𝘁𝘁𝗮𝗰𝗸𝘀 🔍⚡ Last quarter, we almost missed it. It didn’t start with an alert. No high-severity incident. No obvious malware. Just a single line in a log — a failed login attempt from an IP that looked ordinary. But something felt off. 🔍 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝗲𝗿𝗲 𝘁𝗵𝗿𝗲𝗮𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴... Earlier that day, I had read a deep-dive from a security researcher 🧠 about a new attack pattern: 👉 Low-and-slow credential spraying 👉 Geo anomalies that bypass basic rules 🌍 👉 Minimal noise, maximum stealth That “normal” IP? It matched a freshly reported indicator. 🧠 𝗦𝗼 𝗜 𝗳𝗼𝗹𝗹𝗼𝘄𝗲𝗱 𝘁𝗵𝗲 𝘀𝗶𝗴𝗻𝗮𝗹... 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗻𝗼𝗶𝘀𝗲 Instead of waiting for alerts: 👉 Pulled logs across VPN, IAM, endpoints 🖥️ 👉 Enriched the IP with threat intel feeds 📡 👉 Mapped behavior to MITRE ATT&CK 🧩 👉 Built a hypothesis: early-stage access attempt Then I started hunting 🎯 And found more… Same pattern. Multiple users. Silent attempts. ⚙️ 𝗪𝗵𝗲𝗿𝗲 𝘁𝗵𝗲 𝗶𝗻𝘁𝗲𝗹 𝗰𝗮𝗺𝗲 𝗳𝗿𝗼𝗺 This wasn’t luck 🍀 — it was a system: 👉 Open-source intel (blogs, GitHub, researcher reports) 🌐 👉 Commercial feeds (real-time IOCs & adversary infra) 📊 👉 Dark web monitoring (credential leaks & chatter) 🕶️ 👉 Industry groups & sharing communities 🤝 Each source = a piece of the puzzle Together = the full picture 🧠 🚨 🚨 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗱𝗶𝗱 𝗻𝗲𝘅𝘁 👉 Blocked malicious IP ranges 🚫 👉 Forced password resets 🔑 👉 Tuned detections based on TTPs ⚙️ No breach. No escalation. No damage. 💡 𝗧𝗵𝗲 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 Attackers don’t kick the door down 🚪 They test it quietly first… If you rely only on alerts, you’re already behind ⏳ Threat intelligence helps you move from: ➡️ Reactive → Proactive ➡️ Alerts → Anticipation 𝗦𝗶𝗻𝗰𝗲 𝘁𝗵𝗲𝗻, 𝗺𝘆 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲: 👉 Focus on behavior (TTPs), not just IOCs 🎯 👉 Build continuous intel feedback loops 🔄 👉 Hunt with context, not guesswork 🔍 You can use ANYRUN to Speed up and simplify alert triage, incident response, and threat hunting with Threat intelligence Lookup -> https://lnkd.in/gFD8DPJ3 Have you ever stopped an attack early because of threat intel? 🤔 #CyberSecurity #ThreatIntelligence #SOC #ThreatHunting #BlueTeam #InfoSec #CyberDefense For daily cybersecurity updates, follow: Kaaviya Balaji

Lets Learn #Quantum – Post #16: Post-Quantum Cryptography (PQC) The Invisible Safe: Why Hackers Are Stealing Data They Can't Read Yet The biggest short-term impact of quantum computing isn't what it can create. It is what it can destroy. Right now, our digital world relies on encryption algorithms like RSA to protect banking, emails, and cloud data. Standard supercomputers would take thousands of years to crack them. But quantum computers change the rules. Running Shor’s Algorithm, a quantum computer could break today's encryption in hours. The Threat Happening Right Now Why care today if full-scale quantum computers are still year away? Because cybercriminals are actively executing a strategy known as Harvest Now, Decrypt Later (HNDL). Imagine a thief stealing a locked titanium safe. They cannot open it today, so they hide it in a basement and wait. Years from now, a new tool is invented that pops that safe open instantly. That is HNDL. Bad actors are intercepting and archiving sensitive enterprise data today, waiting for the day a quantum computer can unlock it. If your data needs to remain secret for the next decade, it is already at risk. Enter PQC: Upgrading the Locks Post-Quantum Cryptography (PQC) is the defense. It is a new generation of math shields designed to resist attacks from both conventional and quantum computers. The breakthrough? PQC runs seamlessly on your current servers, smartphones, and cloud platforms. Think of it as swapping out a traditional door lock for a multi-dimensional biometric scanner. The house stays the same; only the lock changes. Instead of traditional math, PQC relies on Lattice-Based Cryptography. Think of it like a maze with thousands of overlapping dimensions instead of two. Even a quantum computer gets completely lost trying to find the exit. The Strategic Reality You cannot swap out the security architecture of a global enterprise overnight. Migrating infrastructure takes years, which is why forward-thinking leaders are already auditing networks and testing PQC algorithms today using a hybrid approach. The quantum threat is not a future IT issue. It is a current strategic risk. The question for leadership is no longer: "When will a quantum computer be built?" The real question is: "Will our data still be secure when it arrives?" #QuantumTechnology #PostQuantumCryptography #PQC #QuantumSecurity #CyberSecurity #QuantumComputing #DigitalTransformation #DataProtection #TechnologyLeadership Co-authored with Atul Tripathi Sundar Ram, Sachin Arora, Himanshu Ghawri, Azizur Rahman, Shivendra singh, Prasun Nandy, Jaydeep Sarkar, Joydeep Roy, Arihant Garg, Amit Kumar, Hetal Shah, Arun Rangaraju, Sayantan Chatterjee, Rajesh Kumar Ojha, Dr. Raghav Manohar Narsalay, Praveen Sasidharan, Sundareshwar K (Sundar), Manu Dwivedi, Venkat Nippani, Himadri Ganguly, Ritesh Jain, Abhijit Chakraborty, Sumit Srivastav, Anit Shanker #soyoucan

Data Quality isn't boring, its the backbone to data outcomes! Let's dive into some real-world examples that highlight why these six dimensions of data quality are crucial in our day-to-day work. 1. Accuracy:  I once worked on a retail system where a misplaced minus sign in the ETL process led to inventory levels being subtracted instead of added. The result? A dashboard showing negative inventory, causing chaos in the supply chain and a very confused warehouse team. This small error highlighted how critical accuracy is in data processing. 2. Consistency: In a multi-cloud environment, we had customer data stored in AWS and GCP. The AWS system used 'customer_id' while GCP used 'cust_id'. This inconsistency led to mismatched records and duplicate customer entries. Standardizing field names across platforms saved us countless hours of data reconciliation and improved our data integrity significantly. 3. Completeness: At a financial services company, we were building a credit risk assessment model. We noticed the model was unexpectedly approving high-risk applicants. Upon investigation, we found that many customer profiles had incomplete income data exposing the company to significant financial losses. 4. Timeliness: Consider a real-time fraud detection system for a large bank. Every transaction is analyzed for potential fraud within milliseconds. One day, we noticed a spike in fraudulent transactions slipping through our defenses. We discovered that our real-time data stream was experiencing intermittent delays of up to 2 minutes. By the time some transactions were analyzed, the fraudsters had already moved on to their next target. 5. Uniqueness: A healthcare system I worked on had duplicate patient records due to slight variations in name spelling or date format. This not only wasted storage but, more critically, could have led to dangerous situations like conflicting medical histories. Ensuring data uniqueness was not just about efficiency; it was a matter of patient safety. 6. Validity: In a financial reporting system, we once had a rogue data entry that put a company's revenue in billions instead of millions. The invalid data passed through several layers before causing a major scare in the quarterly report. Implementing strict data validation rules at ingestion saved us from potential regulatory issues. Remember, as data engineers, we're not just moving data from A to B. We're the guardians of data integrity. So next time someone calls data quality boring, remind them: without it, we'd be building castles on quicksand. It's not just about clean data; it's about trust, efficiency, and ultimately, the success of every data-driven decision our organizations make. It's the invisible force keeping our data-driven world from descending into chaos, as well depicted by Dylan Anderson #data #engineering #dataquality #datastrategy

Third-party security validation is still a checkbox exercise in 2026. We accept SOC 2 reports we never read. We send questionnaires vendors copy-paste answers into. We call it "vendor risk management" and move on. Meanwhile, those same vendors are deploying AI agents with access to your data, your APIs, and your infrastructure. The attack surface changed. The validation process did not. But this is scary because: - Most vendor assessments still rely on self-attested evidence nobody verifies - AI agents and API integrations create persistent access that quarterly reviews cannot catch - Critical vendors deserve continuous validation, not annual questionnaires - External attack surface scans, independent pen tests, and real-time monitoring exist today - Not every vendor needs this. But your critical ones absolutely do. We built an entire GRC industry around trusting paper. Paper is not enough anymore. Your critical vendors deserve engineering-grade validation, not a checklist. #GRCEngineering #AI

If your automation stopped working tomorrow, how long could your business continue operating before your customers felt it? We’ve seen it: ■ Retailers frozen at checkout because POS systems failed. ■ Airlines grounded when scheduling tools crashed. ■ Banks paralyzed by cyberattacks. Automation, AI, data platforms, and cloud-based ecosystems have unlocked new opportunities for efficiency, personalization, and growth. But the more we integrate, the more dependent we become. What happens when a critical platform fails? Can your business still serve its customers if automation were to freeze for just a few hours? Or would a simple disruption cascade into a complete shutdown? Digital transformation shouldn’t mean digital fragility. I believe that technology should empower us, not hold us hostage. Here are some strategies to ensure your business stays resilient in a digital-first world: 1. Map your critical dependencies: Understand which platforms, tools, and systems are essential for serving customers. Identify single points of failure and create alternatives before issues arise. 2. Build manual backups: Train teams to handle key operations without full reliance on automation. This ensures continuity when systems fail or platforms go offline. 3. Stress-test your systems: Simulate platform outages or data disruptions to evaluate response times, identify weaknesses, and prepare contingency plans. 4. Invest in cybersecurity & redundancy: As businesses grow digitally, so do risks. Prioritize secure infrastructure, cloud backups, and fail-safe mechanisms to minimize disruption. 5. Empower people, not just platforms: Technology should enhance human capability, not replace it. By upskilling teams, companies ensure employees can step in when automation halts. As tech leaders, we need to rethink risk management, stress-test operations, and ensure customer experience doesn’t collapse when the tech stack hiccups. #Automation #AI #Data #Tech