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It's astonishing that $180 billion of the nearly $600 billion on cloud spend globally is entirely unnecessary. For companies to save millions, they need to focus on these 3 principles — visibility, accountability, and automation. 1) Visibility The very characteristics that make the cloud so convenient also make it difficult to track and control how much teams and individuals spend on cloud resources. Most companies still struggle to keep budgets aligned. The good news is that a new generation of tools can provide transparency. For example: resource tagging to automatically track which teams use cloud resources to measure costs and identify excess capacity accurately. 2) Accountability Companies wouldn't dare deploy a payroll budget without an administrator to optimize spend carefully. Yet, when it comes to cloud costs, there's often no one at the helm. Enter the emerging disciplines of FinOps or cloud operations. These dedicated teams can take responsibility of everything from setting cloud budgets and negotiating favorable controls to putting engineering discipline in place to control costs. 3) Automation Even with a dedicated team monitoring cloud use and need, automation is the only way to keep up with the complex and evolving scenarios. Much of today's cloud cost management remains bespoke and manual, In many cases, a monthly report or round-up of cloud waste is the only maintenance done — and highly paid engineers are expected to manually remove abandoned projects and initiatives to free up space. It’s the equivalent of asking someone to delete extra photos from their iPhone each month to free up extra storage. That’s why AI and automation are critical to identify cloud waste and eliminate it. For example: tools like "intelligent auto-stopping" allow users to stop their cloud instances when not in use, much like motion sensors can turn off a light switch at the end of the workday. As cloud management evolves, companies are discovering ways to save millions, if not hundreds of millions — and these 3 principles are key to getting cloud costs under control.

What is OTA ? (Chapter 1 To understand OTA) OTA (Over-the-Air) updates allow automotive ECUs to receive firmware, software, or configuration updates remotely  via a wireless connection (Wi-Fi, Cellular, or V2X). This eliminates the need for physical service visits, making software deployment faster, safer, and cost-effective. MMU-Based A/B Swap Memory Remapping for OTA Updates There are two primary types of OTA updates in automotive: OTA Type     Description                                    Example SOTA (Software OTA) Updates software applications like infotainment, navigation, UI/UX.         Map updates, voice assistant improvements FOTA (Firmware OTA) Updates ECU firmware, including low-level software and microcontroller programming.  Powertrain, ADAS, or safety-critical updates. e.g Dual Bank (A/B Partitioning) HW Shall Support SWAP A/B Features for Partitioning. Two firmware images are maintained (Active & Backup). The new update is written to the inactive partition while the system continues running. On successful validation, the system switches to the new firmware The bootloader controls which firmware partition is active by modifying the MMU (Memory Management Unit) settings. To perform an OTA update, several AUTOSAR and non-AUTOSAR components are involved: Module Functionality OTA Client (ECU) Receives update package from cloud. OTA Server (Cloud Backend) Manages update distribution & security. Bootloader (FBL - Flash Bootloader) Handles firmware flashing & rollback. Download Manager Manages data download and storage. Secure Communication Module Ensures encrypted transmission (TLS, Secure Boot). Diagnostic Services (UDS - ISO 14229) Supports DoIP/CAN updates via UDS (0x34, 0x36, 0x37). Example: Infotainment ECU receives an OTA update. OTA Client verifies authenticity (RSA signature check). FBL stores new firmware in Flash and restarts the ECU. If the update fails, rollback to the previous version. Memory Layout e.g. +--------------------------+ 0x08000000 (Start of Flash) |    Bootloader     | (Immutable)   +--------------------------+   |    Active Firmware  | (Currently Running)   +--------------------------+   |    Backup Firmware  | (OTA Update Target)   +--------------------------+   |    OTA Metadata    | (Update Info, Version, Hash)   +--------------------------+   |    Swap Partition   | (Temporary Storage)   +--------------------------+ 0x080FFFFF (End of Flash)  API Sequence for UDS-Based OTA Step API Call Description 1️⃣ Request Update Dcm_TriggerDownload(0x34) ECU requests firmware update via UDS. 2️⃣ Validate Image Dcm_RequestTransferExit(0x37) Checks RSA signature and hash. 3️⃣ Store Firmware Fls_Write() Writes firmware to backup partition. 4️⃣ Flash ECU Fbl_TriggerReprogramming() Bootloader overwrites active firmware. 5️⃣ Restart System EcuM_TriggerReset() ECU reboots into new firmware. 6️⃣ Verify Update NvM_ReadBlock() Checks firmware integrity after reboot.  

Having processed 100,000+ customer feedbacks with AI, I can confidently say: Customer feedback analysis is one of the best use cases for AI. And it’s also one of the worst. Let me explain: I’ve worked a lot lately on use cases that deal with analyzing customer feedback with AI: → Comments → Surveys → Reviews Thanks to Large Language Models, it’s never been easier to process this stuff at scale. Where you previously had to build complex topic models, you can now simply paste text into ChatGPT and get an interesting-sounding answer. Which is exactly the problem. Because dumping customer feedback into an LLM doesn’t actually give you any insight. It gives you a summary. Like: “Users want better usability.” “They’re confused by onboarding.” (You didn’t need AI to tell you that.) LLMs rarely generate true novelty. They compress to the mean. Which means they’ll repeat what you already suspect – just faster and fancier. If you want actual insight, you need structure. Here’s the 5-step method I use with clients: 1. Start with a problem hypothesis. Don’t ask: “What are they saying?” Ask: “Are they struggling with X?” 2. Build a taxonomy that maps to action. “Didn’t get login email” → “Email deliverability issue” “Too expensive” → “Pricing objection” 3. Count what matters. Still the most underrated skill in analytics: frequency by category. 4. Analyze temporal trends. *When* people say something often matters more than *what* they say. 5. Look for what’s missing. No positive feedback in 1,000 comments? That’s a signal too. I’m sharing the exact approach I use to turn raw customer comments into real buyer insight – using AI the right way. If you work with feedback, you’ll want this breakdown. Sign up before Friday 12pm CET → https://lnkd.in/eFzzQrMJ

𝗣𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗶𝗻 𝗢𝗧 𝗶𝘀 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗮 𝗖𝗩𝗦𝗦 𝘀𝗰𝗼𝗿𝗲. 𝗜𝘁'𝘀 𝗮 𝗱𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. In IT, patching can often be a race against time. In OT/ICS, it's a 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻. Applying a patch without a thorough process can pose a greater risk to operations than the vulnerability itself. Before you patch that critical PLC or HMI, don't just look at the severity score. Follow a deliberate approach. Our checklist breaks it down into four key phases: Phase 1: Triage & Info Gathering Verify the vulnerability, understand the asset's role, and review the patch itself. Is it even applicable? Phase 2: Risk & Impact Analysis Assess the true operational risk. What's the impact of patching vs. the risk of inaction? A high-severity vulnerability on a non-critical, isolated asset may not be your top priority. Phase 3: Planning & Preparation Develop detailed patching, rollback, and validation plans. Schedule a maintenance window that minimizes operational disruption. Phase 4: Communication & Approval Notify all stakeholders, get formal approval through your change management process, and document the final decision. The goal isn't just to patch everything, but to patch the right things at the right time with the right plan. Liked it ? Reshare #OTCybersecurity #ICS #IndustrialCybersecurity #PatchManagement #RiskManagement #CyberSecurity #OperationsTechnology

💡There’s an interesting trend I observed with organizations recently: they are choosing to save money and simplify their operations by using slower but cheaper storage systems. This is especially true when they handle large amounts of data and sub-second latency isn't critical. Let’s find out what’s motivating this. Data loses its value over time. Once data becomes older and rarely accessed, real-time performance becomes less crucial. While developers need to access historical data for analysis, ad hoc queries, and compliance requirements, they can accept some latency. Their priority now shifts to storing this older data most cost-effectively and efficiently. Compute-storage decoupling is something that we inherited from the Hadoop era, allowing storage systems to use tiered storage for improved cost-efficiency and scalability. ✳️ Object stores became the de facto tiered storage Amazon S3 was officially launched in 2006. Almost 20 years later and with trillions of objects stored, we now have reliable infinite storage. People started to call this cheap, infinitely scalable storage a Data Lake(or Lakehouse nowadays). For developers, it offers a simple path to disaster recovery. When you upload a file to S3, you immediately get eleven nines of durability—that's 99.999999999%. To put this in perspective: if you store 10,000 objects, you might lose just one in 10 million years. As object stores like S3 become more affordable, databases and OLAP systems have increasingly utilized deep object storage to enhance cost efficiency and durability. For example, PGAA, the EDB’s analytics extension for Postgres, allows you to query hot data and cold data with a single dedicated node, ensuring optimal performance by automatically offloading cold data to columnar tables in object storage, reducing the complexity of managing analytics over multiple data tiers. ✳️ Not only databases, but streaming data platforms are evolving too Redpanda and WarpStream show how modern streaming platforms can save money while maintaining good performance. They do this by using a mix of fast local storage (SSDs) for quick access and cloud storage for most of their data, avoiding costly cross-AZ data transfers. ✳️ Why not make the object stores Iceberg compatible? That will transform simple storage solutions into powerful data management systems like data lakehouses. This compatibility brings essential features like schema evolution, time travel capabilities, ACID transactions, and performance optimizations—all while maintaining the cost benefits of object storage. This gives organizations the flexibility to choose their own query engine and catalog, making data platforms more modular and composable.

Cybersecurity loses when shortcuts win! Especially in Patch Management. Typical shortcuts taken by IT teams in patching: 1) Skipping testing: Deploying patches without testing leads to compatibility issues. 2) Delaying patching: Waiting until the next cycle leaves systems exposed. 3) Partial patching: Only patching critical systems while ignoring lower-priority ones. 4) Overlooking failed patching: Assuming patches applied successfully without validation. 5) Ignoring certain endpoints: Leaving remote devices unpatched, increasing entry points. 6) Relying on manual processes: Manual patching slows deployment & risks human error. 7) Not prioritizing based on risk: Treating all vulnerabilities equally instead of focusing on critical ones. The most mature companies follow these best practices. → Conduct thorough patch testing in isolated environments. → Establish automated patching workflows with regular scans. → Validate patch deployments & track failed applications. → Prioritize critical patches using risk-based assessment. → Include all devices, especially endpoints, in their patching strategy. → Use patching tools that integrate with VAPT software out-of-box. Shortcuts may feel easier now, but they lead to bigger gaps later. And a simple headache turns into a migraine! Avoid them to keep your organization secure. P.S. Which shortcut have you seen most often? ---- Hi! I’m Rajeev Mamidanna. I help CISOs strengthen Cybersecurity Strategies + Build Authority on LinkedIn.

Letter V: Vulnerability Management: Best Practices for a Patchwork World Our ‘A to Z of Cybersecurity’ explores Vulnerability Management - the ongoing process of identifying, prioritizing, and remediating vulnerabilities in your systems and software. It's like patching the leaks in your digital fortress! In a world of constantly evolving threats, vulnerability management is a critical practice: The Vulnerability Landscape: · Software Vulnerabilities: New vulnerabilities are discovered all the time, so staying up-to-date is crucial. · Exploit Availability: Cybercriminals are quick to develop exploits for known vulnerabilities. · Patch Management Challenges: Deploying patches across a complex IT infrastructure can be challenging. Building a Strong Defense: · Vulnerability Scanning: Regularly scan your systems for known vulnerabilities using automated tools. · Prioritization & Remediation: Prioritize patching based on the severity of the vulnerability and the potential impact. · Patch Management Process: Develop a systematic process for deploying patches efficiently and testing for compatibility issues. Continuous Vigilance: · Staying Up-to-Date: Subscribe to security advisories from software vendors and relevant cybersecurity organizations. · Vulnerability Intelligence: Leverage threat intelligence feeds to stay informed about emerging vulnerabilities. · Penetration Testing: Regularly simulate cyberattacks to identify and address any remaining vulnerabilities. Vulnerability management is an ongoing process, not a one-time fix. By implementing a comprehensive strategy, you can proactively identify and address vulnerabilities before they can be exploited by attackers. #QuickHeal #Seqrite #Cybersecurity #VulnerabilityManagement

Nobody Has Solved Vulnerability Management Let's face it - vulnerability management remains unsolved—not for lack of tools or effort, but because the problem is rooted in the reality of complex, ever-evolving IT environments and misaligned priorities. The Root Cause 🚨 Prioritisation Paralysis: Security teams commonly label “everything” as a priority, leading to an unsustainable situation where real threats get lost in the noise. When all vulnerabilities are urgent, none actually are, diluting focus and overloading remediation teams. 🚨 Lack of Standardisation: Without industry-standard ratings, organisations juggle different scoring systems (CVSS, vendor scores, managerial directives), making effective risk prioritisation nearly impossible. 🚨 Silos & Communication Gaps: Security and IT operate in isolation—security wants speed, IT wants stability. This results in missed patches, rushed deployments without proper testing, and unclear accountability. 🚨 Information Blind Spots: Organisations lack full visibility into their attack surface, shadow IT, and contextual risk data. This leads to decisions made in the dark, undermining any best efforts at prioritisation. Why Current Approaches Struggle ⚠️ Overwhelming Volume: Monthly maintenance, zero-day threats, and critical app updates all compete for attention. Most teams fall back on rigid cycles, missing the nuance needed for real-world threats. ⚠️ Manual & Reactive Processes: Reliance on spreadsheets or siloed tools results in a reactive, rather than proactive, approach to patching. Best Practices for Patch Prioritisation To break the cycle, leading practice is moving toward a risk-based approach: 💡 Track-Based Remediation: Assign vulnerabilities to distinct tracks—routine, critical application, or urgent zero-days—and manage each according to risk and business impact. 💡 Continuous Contextual Analysis: Integrate vulnerability intelligence, exploit likelihood, compliance requirements, and business exposure into prioritisation—not just severity scores. 💡 Automation & AI: Use AI for fast analysis of vast data sources, applying predictive models to score risk more accurately. Automate patch testing and deployment to close gaps and improve consistency. 💡 Unified Visibility: Invest in tools that give a comprehensive, context-rich view of your organisation’s true attack surface and current exposures. The Path Forward Nobody has solved vulnerability management because the challenge isn’t just technical—it’s operational, cultural, and contextual. Until organisations bridge silos, clarify ownership, embrace risk-based prioritisation, and utilise advanced automation, vulnerability management will continue to be a juggling act.

Unlocking the Secrets of Cloud Costs: Small Tweaks, Big Savings! Three fundamental drivers of cost: compute, storage, and outbound data transfer. 𝐂𝐨𝐬𝐭 𝐎𝐩𝐬 refer to the strategies and practices for managing, monitoring, and optimizing costs associated with running workloads and hosting applications on provider’s infrastructure. 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐌𝐢𝐧𝐢𝐦𝐢𝐳𝐞 𝐂𝐥𝐨𝐮𝐝 𝐇𝐨𝐬𝐭𝐢𝐧𝐠 𝐂𝐨𝐬𝐭𝐬: 💡𝐑𝐢𝐠𝐡𝐭-𝐒𝐢𝐳𝐢𝐧𝐠 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬: 📌 Ensure you're using the right instance type and size. Cloud providers offer tools like Compute Optimizer to recommend the right instance size. 📌 Implement auto-scaling to automatically adjust your compute resources based on demand, ensuring you're only paying for the resources you need at any given time. 💡𝐔𝐬𝐞 𝐒𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬: 📌 Serverless solutions like AWS Lambda, Azure Functions, or Google Cloud Functions allow you to pay only for the execution time of your code, rather than paying for idle resources. 📌 Serverless APIs combined with functions can help minimize the need for expensive always-on infrastructure. 💡𝐔𝐭𝐢𝐥𝐢𝐳𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐝 𝐒𝐞𝐫𝐯𝐢𝐜𝐞𝐬: 📌 If you're running containerized applications, services like AWS Fargate, Azure Container Instances, or Google Cloud Run abstract away the management of servers and allow you to pay for the exact resources your containers use. 📌 Use managed services like Amazon RDS, Azure SQL Database, or Google Cloud SQL to lower costs and reduce database management overhead. 💡𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐂𝐨𝐬𝐭 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 📌 Use the appropriate storage tiers (Standard, Infrequent Access, Glacier, etc.) based on access patterns. For infrequently accessed data, consider cheaper options to save costs. 📌 Implement lifecycle policies to transition data to more cost-effective storage as it ages. 💡𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐞 𝐂𝐨𝐧𝐭𝐞𝐧𝐭 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐍𝐞𝐭𝐰𝐨𝐫𝐤𝐬 (𝐂𝐃𝐍𝐬): Using CDNs like Amazon CloudFront, Azure CDN, or Google Cloud CDN can reduce the load on your backend infrastructure and minimize data transfer costs by caching content closer to users. 💡𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐀𝐥𝐞𝐫𝐭𝐬: Set up monitoring tools such as CloudWatch, Azure Monitor etc. to track resource usage and set up alerts when thresholds are exceeded. This can help you avoid unnecessary expenditures on over-provisioned resources. 💡𝐑𝐞𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫 𝐌𝐮𝐥𝐭𝐢-𝐑𝐞𝐠𝐢𝐨𝐧 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭𝐬: Deploying applications across multiple regions increases data transfer costs. Evaluate if global deployment is necessary or if regional deployments will suffice, which can help save costs. 💡𝐓𝐚𝐤𝐞 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐨𝐟 𝐅𝐫𝐞𝐞 𝐓𝐢𝐞𝐫𝐬: Most cloud providers offer free-tier services for limited use. Amazon EC2, Azure Virtual Machines, and Google Compute Engine offer limited free usage each month. This is ideal for testing or running lightweight applications. #cloud #cloudproviders #cloudmanagement #costops #tech #costsavings

Your website isn’t converting?  These 3 UX mistakes are likely the reason. The problem isn’t how your site looks. It’s how users feel when they land on it and what happens next. Here’s what we fixed for ELITE (and what you might need to fix too): 1. Clutter is killing your conversions  ↳ ELITE old site was trying to say everything at once.  ↳ And when everything feels important nothing stands out. The fix: → One message per screen → One clear call to action → A clean, focused layout Clarity wins. Simplicity converts. 2. Generic visuals make your brand forgettable  ↳ Stock icons. Mismatched colors. Zero brand personality.  ↳ Users couldn’t feel the brand. So they bounced. The fix: → Bold, custom images → High-contrast, modern design → A visual style that actually reflects the brand Want users to remember you? Show them who you are visually. 3. Walls of text don’t get read  ↳ Most websites still write like it’s 2010. Long paragraphs. No structure.  ↳ But users scan. If they don’t get answers fast they leave. The fix: → Clear headings → Short, swimmable sections → Copy that’s easy to follow, built for quick decisions Good design grabs attention. Good copy keeps it. This wasn’t just a visual upgrade for ELITE. It was a complete user-first transformation. ✔ Clear messaging ✔ Stronger visual identity ✔ Faster, easier decisions for users Still making one of these mistakes on your site? Let’s fix it. Drop your thoughts or questions below 👇 Which of these 3 problems do you still see on websites today?