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🚨CISA & NSA release Crucial Guide on Network Segmentation and Encryption in Cloud Environments🚨 In response to the evolving requirements of cloud security, the Cybersecurity & Infrastructure Security Agency (CISA) and the National Security Agency (NSA) recently released a comprehensive Cybersecurity Information Sheet (CSI): "Implement Network Segmentation and Encryption in Cloud Environments." This document provides detailed recommendations to enhance the security posture of organizations operating within cloud infrastructures (that probably means you). Key Takeaways Include: 🔐 Network Encryption: The document underscores the importance of encrypting data in transit as a defense mechanism against unauthorized data access. 🌐 Secure Client Connections: Establishing secure connections to cloud services is fundamental. 🔎 Caution on Traffic Mirroring: While recognizing the benefits of traffic mirroring for network analysis and threat detection, the guidance cautions against potential misuse that could lead to data exfiltration and advises careful monitoring of this feature. 🛡️ Network Segmentation: Stressed as a foundational security principle, network segmentation is recommended to isolate and contain malicious activities, thereby reducing the impact of any breach. This collaboration between NSA and CISA provides actionable recommendations for organizations to strengthen their cloud security practices. The emphasis is on strategically implementing network segmentation and end-to-end encryption to secure cloud environments effectively. Information security leaders are encouraged to review this guidance to understand better the measures necessary to protect cloud-based assets. Implementing these recommendations will contribute to a more secure, resilient, and compliant cloud infrastructure. Access the complete guidance provided by the NSA and CISA to fully understand these recommendations and their application to your organization’s cloud security strategy. 📚 Read CISA & NSA's complete guidance here: https://lnkd.in/eeVXqMSv #cloudcomputing #technology #informationsecurity #innovation #cybersecurity

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

Once upon a time, the hyperscalers built in silence and bragged about sales, not capacity. Today, compute is the star of the show - a spectacle of steel, silicon, and storytelling. We now get data-center launch events, exclusive tours, and podcast cameos. Yesterday, we got an exclusive first look at Microsoft’s new data center, Fairwater 2, with Satya Nadella himself as the tour guide. And in classic Satya fashion, the theme was: models are cool, but let me show you the boring stuff that mints money. 1. The AI Factory Fairwater 2 is part of a chain of “AI factories” stitched across regions, each stuffed with next-gen chips and pushing past 2 GW of capacity. A 1-petabit network lets training jobs span buildings like they’re tabs in a browser. But Satya’s obsession is fungibility: don’t build a monument to a single chip generation. “You want to be scaling in time, not scale once and be stuck with it.” Hence Microsoft’s mix of owned builds + leased GPU capacity to balance speed-to-market with risk of stranded assets. 2. The Business Model Flip Subscriptions will become token entitlements, not software access. The future is per-user + per-agent pricing. Every agent gets an identity, storage, permissions, and an observability trail - basically a virtual employee with compute overhead. M365 shifts from end-user suite to infra for autonomous coworkers. 3. SaaS Margin Shock ≠ Doom AI compresses SaaS margins because inference is expensive. Satya’s take: yes, margins dip, but the market explodes. GitHub Copilot went from nonexistent to multi-B$ run rate in ~12 months. TAM expansion more than offsets the margin pressure, and efficiency improves over time. 4. Infrastructure > Models Satya’s spiciest line: “If you’re a model company, you may have a winner’s curse… You may have done all the hard work, done unbelievable innovation, except it’s one copy away from that being commoditized.” Open weights, checkpoint portability, and in-house fine-tunes erode the defensibility of raw model APIs. The defensible layer is scaffolding - identity, storage, observability. Models are temporary. Governance is forever. 5. GitHub as Agent HQ The developer-agent market is now a $5–6B run rate category. Copilot leads, but GitHub wins no matter who builds the best bot. Microsoft’s plan: turn GitHub into Agent HQ, where companies spin up fleets of agents, orchestrate workflows, and audit which bot broke prod this time. 6. Silicon, OpenAI, and MAI Nvidia remains “life itself” but Microsoft is growing its in-house silicon portfolio (Maia, Cobalt) for cost control and vertical fit. Plus: Azure retains exclusivity over OpenAI’s stateless API, keeping enterprise traffic anchored to Microsoft even if ChatGPT runs elsewhere. 7. The Geopolitics Moat Trust = infrastructure. Global faith in American tech, not just model quality, will dictate where AI runs. Satya’s bet: models will come and go. The platform that provisions, secures, and governs them will not. Long Live MSFT.

🔐 Data in Use --Protection Strategies ⚠️ The Challenge When data is being processed in memory (RAM/CPU), it’s usually decrypted, which makes it vulnerable to: 💥 Insider threats 💥 Malware/memory scraping 💥 Cloud provider access ✅ Solutions for Data in Use 1. Homomorphic Encryption (HE) Data stays encrypted even during computation. Supports analytics, AI/ML, and calculations without exposing raw values. 💥 Use case: A hospital can run statistics on encrypted patient data without seeing individual records. Downside: Very slow for large-scale real-time workloads (still improving). 2. Secure Enclaves / Trusted Execution Environments (TEEs) Hardware-based isolation → a secure “enclave” inside the CPU where data is decrypted and processed. Even the system admin or cloud provider cannot see inside. ✨ Examples: 💥 Intel SGX 💥 AMD SEV 💥 AWS Nitro Enclaves → lets you isolate EC2 instances for secure key management, medical data processing, payment transactions, etc. 💥 Use case: A bank can run fraud detection models on sensitive financial data in the cloud without exposing it to AWS staff. 3. Confidential Computing Broader concept: combines TEEs, encrypted memory, and sometimes HE. Ensures that data remains protected throughout its lifecycle (rest, transit, use). ✨ Cloud examples: 💥 AWS Nitro Enclaves 💥 Azure Confidential Computing 💥 Google Confidential VMs 4. Secure Multi-Party Computation (MPC) Multiple parties compute a function jointly without revealing their private inputs. Often used in cryptocurrency custody, federated learning, and zero-knowledge proofs. 💥 Example: Banks collaboratively detect fraud patterns without sharing customer records. #learnwithswetha #encryption #datainuse #learning #dataprotection #privacy

Hybrid Cloud Is Becoming the Default Enterprise AI Platform For years, “cloud-first” was treated like a universal truth. But as AI moves from proofs of concept to production, enterprise platforms are being stress-tested in ways traditional application stacks never were. From my perspective, hybrid cloud isn’t a compromise anymore—it’s quickly becoming the most practical operating model for modern enterprises. AI changes the cost conversation because it introduces workloads that are compute-hungry and often always-on. When you’re training models, fine-tuning continuously, or running inference at scale across the business, the economics can shift fast. Elasticity is still valuable, but predictability becomes just as important—especially when leadership wants reliable unit costs and fewer billing surprises. Latency also stops being an optimization goal and becomes a hard requirement. There are plenty of use cases where you simply can’t afford the round trip to a distant region and back. When decisions need to happen in milliseconds, placing inference closer to users, devices, and operations is less about preference and more about making the system viable. Then there’s data gravity, governance, and sovereignty. Real-world enterprises don’t get to pretend that sensitive data, jurisdictional rules, and internal controls are optional. Many organizations will keep critical datasets and portions of the AI pipeline close to where the data is created and governed, because that’s often the simplest path to compliance and risk reduction. What’s emerging is a practical three-tier model that I expect to become the norm: public cloud for speed, experimentation, and burst capacity; on-prem for consistent production workloads that benefit from tight control and predictable economics; and edge for real-time inference where latency and availability are non-negotiable. The winning strategy isn’t choosing one environment—it’s placing workloads where they run best, technically and financially. If you’re shaping your enterprise platform strategy for 2026, start with this: are you optimizing around an ideology, or around workload reality? #HybridCloud #EnterprisePlatforms #AIInfrastructure #CloudComputing #EdgeComputing #PlatformEngineering #FinOps #DataGovernance #EnterpriseIT #DigitalTransformation

Venture capital and media attention fixate on foundation model capabilities, but the competitive battleground in AI has shifted to the unsexy, boring parts of AI - things like orchestration layers, retrieval systems and connective infrastructure. Organisations do not deploy “a model”. They deploy workflows integrating models with proprietary data, existing software systems, human review processes, compliance controls and operational monitoring. The sophistication of this second-order infrastructure increasingly determines who wins in AI deployment. The Model Context Protocol exemplifies this shift. By providing a standardised interface for AI systems to connect with external tools and data sources, MCP solves the “M times N” problem that plagued earlier integration efforts. Connecting M models to N tools previously required M times N custom integrations, each demanding bespoke engineering, testing and maintenance. MCP reduces this to M plus N by providing a common protocol. The seemingly technical detail of interoperability standards enables the ecosystem effects that allow agentic AI to scale across organisations and use cases. Retrieval-Augmented Generation represents another critical infrastructure layer. Generic models know only what appears in their training data. Enterprise value requires grounding AI responses in current, proprietary organisational information. RAG systems retrieve relevant context from document stores, databases and knowledge graphs, then inject that context into the model’s reasoning process. The engineering required to make this work reliably encompasses vector databases, embedding models, semantic search, ranking systems, access controls and cache management. These components are invisible to end users but determine whether an AI system produces valuable insights or expensive nonsense. The orchestration market has grown explosively as organisations recognise that managing multiple specialised models and tools requires sophisticated coordination. Rather than forcing every query through a single expensive frontier model, orchestration systems route requests intelligently. Simple queries go to fast, cheap models. Complex reasoning tasks go to sophisticated models. Specialised tasks go to fine-tuned domain models. This arbitrage across model capabilities and costs determines the unit economics of AI deployment. These systems sit between enterprise users and external AI providers, enforcing usage policies, managing costs, logging interactions for audit and blocking potentially harmful outputs. Deploying AI without a gateway has become as negligent as deploying web servers without firewalls. The governance, compliance and risk management capabilities embedded in these infrastructure layers determine whether enterprises can scale AI deployment while maintaining controle. The companies building superior connective tissue will matter more than those training marginally better models.

FinOps for hybrid cloud – what’s new & what’s changing? Hybrid cloud is not just "cloud plus a data center." It’s a collision of compute models, pricing logic, tagging systems, and visibility gaps. Here’s what’s really changing: 1. Tags don’t travel across borders. Cloud gives you native tags. On-prem gives you naming conventions. AI agents? Good luck tracing anything at all. In hybrid setups, every platform speaks a different tagging dialect - and most FinOps tools expect a single language. Fix: Build a translation layer. Map every tag, label, and telemetry source into a common schema - like TBM 5.0 or FOCUS. Tag what you can, map what you can’t. It’s not perfect tagging - it’s consistent context. 2. You can't optimize what teams don’t understand. Infra teams think in CPU hours. Finance teams think in GL codes. Product teams care about cost per outcome - but don’t have access to the numbers. This mismatch is the reason why cost optimization efforts get ignored or delayed. Fix: Switch to unit cost storytelling. Frame every FinOps discussion around “cost per customer session,” “cost per API call,” or “cost per insurance claim.” When engineers and CFOs look at the same KPI, magic happens. 3. On-prem idle isn't free - it’s just invisible. Cloud idle shows up on your bill. On-prem idle hides in your racks. The myth? “We already paid for it, so it's fine.” The reality? That unused capacity is a silent cost. And nobody's being held accountable for it. Fix: Treat idle as risk premium or future demand buffer. Then allocate its cost like insurance - spread across services that rely on its availability. 4. Observability costs are the new FinOps blind spot. You added logs, traces, metrics… Now your observability bill is bigger than your AI spend. In hybrid systems, telemetry doesn’t just help - it hurts if left unchecked. Fix: Implement telemetry budgets. Decide: Which spans matter? Which logs convert to insight? Observability without prioritization is just expensive noise. 5. AI breaks every legacy cost model. Hybrid cloud isn’t just servers and services anymore - it’s agents, LLMs, retrievers, and pipelines spread across multiple systems. One task might touch five clouds, three APIs, and burn a million tokens - none of it showing up clearly in your FinOps dashboard. Fix: Shift your thinking from asset-level costs to outcome-level costing. Ask: What did this workflow produce? What did it consume? Build cost models around outcomes, not just infrastructure units. Even without perfect observability, this mental model helps separate profitable AI patterns from budget sinkholes. Hybrid cloud isn’t the future. It’s already here. And FinOps can’t be cloud-only anymore. If your current FinOps strategy can’t handle these realities - you’re just watching spend, not governing it. #FinOps #Mavvrik

A junior reached out to me last week. One of our APIs was collapsing under 150 requests per second. Yes — only 150. He had tried everything: * Added an in-memory cache * Scaled the K8s pods * Increased CPU and memory Nothing worked. The API still couldn’t scale beyond 150 RPS. Latency? Upwards of 1 minute. 🤯 Brain = Blown. So I rolled up my sleeves and started digging; studied the code, the query patterns, and the call graphs. Turns out, the problem wasn’t hardware. It was design. It was a bulk API processing 70 requests per call. For every request: 1. Making multiple synchronous downstream calls 2. Hitting the DB repeatedly for the same data for every request 3. Using local caches (different for each of 15 pods!) So instead of adding more pods, we redesigned the flow: 1. Reduced 350 DB calls → 5 DB calls 2. Built a common context object shared across all requests 3. Shifted reads to dedicated read replicas 4. Moved from in-memory to Redis cache (shared across pods) Results: 1. 20× higher throughput — 3K QPS 2. 60× lower latency (~60s → 0.8s) 3. 50% lower infra cost (fewer pods, better design) The insight? 1. Most scalability issues aren’t infrastructure limits; they’re architectural inefficiencies disguised as capacity problems. 2. Scaling isn’t about throwing hardware at the problem. It’s about tightening data paths, minimizing redundancy, and respecting latency budgets. Before you spin up the next node, ask yourself: Is my architecture optimized enough to earn that node?

Having spent years working on distributed systems, I wanted to share a detailed breakdown of Facebook's impressive architecture that serves billions of users daily. 🏗️ Core Architecture Components: 1. Frontend Layer: - Client interface connects to multiple services through DNS - Load balancers distribute traffic across API gateways - CDN optimization for static content delivery 2. Data Processing Pipeline: - Sophisticated write/read server separation for optimal performance - Multiple API gateways handle request routing and load distribution - Dedicated video/image processing service with worker pools - Feed generation tasks run asynchronously through dedicated queues 3. Storage Architecture: - Multi-tiered caching system reducing database load - Directory-based partitioning for efficient data distribution - Master-slave database configuration enabling:  • High availability  • Read scalability  • Disaster recovery - Shard manager handling data partitioning and replication 4. Real-Time Features: - Dedicated notification service with queue management - Search functionality with results aggregators - Elastic search implementation with caching layer - Like service integration with feed generation 5. Performance Optimizations: - Strategic cache placement at multiple levels - Asynchronous processing for compute-heavy tasks - Horizontal scaling capabilities at every tier - Specialized workers for media processing 🔍 Technical Deep-Dive: The architecture demonstrates several critical patterns: - Microservices decomposition for independent scaling - Event-driven design for real-time updates - Polyglot persistence with different storage solutions - Circuit breakers and fault isolation - Eventually consistent data model ⚡ Performance Considerations: - Read/write splitting reduces contention - Caching at multiple layers minimizes latency - Async processing prevents blocking operations - Partitioning enables infinite horizontal scaling - CDN integration optimizes content delivery globally 🛡️ Reliability Features: - Multiple API gateways prevent single points of failure - Slave DB replicas ensure data redundancy - Sharding enables better fault isolation - Queue-based design handles traffic spikes - Worker pools manage resource utilization 📈 Scaling Strategies: - Horizontal scaling across all services - Partition tolerance through sharding - Load balancing at multiple levels - Stateless services for easy replication - Cache hierarchies for performance 🎯 Key Engineering Decisions: 1. Separating read/write paths 2. Implementing content-aware routing 3. Using specialized processing queues 4. Maintaining data consistency through careful service design 5. Employing multiple layers of caching 💡 Learning Points: - How to handle web-scale data processing - Balancing consistency vs availability - Managing real-time features at scale - Implementing efficient content delivery - Designing for fault tolerance