How to Protect AI Data Centers from Lightning Risks

Small, Specialized AI Models: The Future of Networking Large AI Models for networking management and optimization felt like a far-off concept — something more futuristic than probable — over the last several years. They were too expensive, resource-intensive, and too complex to be viable; however, the pendulum is swinging toward Small / Specialized Models -- AI that is designed for efficient execution of specific, well-defined tasks. Specialized models can be applied to a range of tangible networking problem sets: 1. Detection of anomalous network traffic patterns. 2. Predictive failure models that can identify potential equipment failures before they occur and are able to preemptively avoid downtimes. 3. Bandwidth and QoS optimization to enhance objective performance. 4. Detection of intelligent situational awareness alerts in support of NOC .operations The pros of using small/specialized models are immediate: 1. Lower hardware/component resource consumption 2. Faster learning and response rates 3. Less operational expense, with more scale For IT / engineers this suggests that AI is less of a futuristic capabilities proposal, and more ready state and reliability and reliability, integrated with the things we do every day, without developing a massive datacenter, or multiple cloud-based applications. I think there will soon come a time when all organizations will have one/more small, specialized AI Models for networking; independent assistants that act like precision tools for each engineer -- to help make engineering smarter. #AI #Networking #IT #EdgeAI #NetworkOperations #Cybersecurity #AITools #Mehdi_bahramipour

AI is only as strong as the infrastructure behind it. I’ve seen firsthand how centralized hyperscale data centers, while powerful, are not sustainable long term. They consume enormous amounts of energy, concentrate risk, and leave critical sectors vulnerable to disruption. That’s why I advocate for GridEdge AI, a decentralized model that brings compute power closer to where data is generated. Whether it’s utilities, healthcare, or defense, resilience comes from smaller, modular systems tied directly into local infrastructure. My background in energy storage has shown me that power is the true enabler. Without advanced, secure battery systems, edge computing isn’t possible. At American Lithium Energy, we’re pairing defense-grade batteries with microgrids so AI can operate independently of fragile centralized networks. The stakes are high. A single attack could cripple national systems. Decentralized, NDAA-compliant supply chains and post-quantum security are the way forward if we want to protect both commercial operations and national defense. Read more of my thoughts on building resilient GridEdge AI systems here: https://lnkd.in/gVwrMBMa

5G : Experiential Networked Intelligence (ENI) for Next-Generation Optimization Artificial Intelligence (AI) and Machine Learning (ML) are reshaping 5G networks, enabling real-time adaptation, predictive analytics, and automated optimization across ultra-dense deployments, network slicing, and edge computing environments. By leveraging ML for traffic forecasting, dynamic slice reconfiguration, anomaly detection, and advanced beamforming, operators can ensure superior Quality of Service (QoS) and Quality of Experience (QoE) while reducing manual intervention. Standardization bodies are providing a robust framework for AI adoption in 5G. ITU-T Y.3172 (2019) defines the architectural principles for ML integration, 3GPP Release 18 specifies AI-driven RAN automation and SLA assurance, ETSI GS ENI 005 V2.1.1 (2021‑12) offers a reference architecture for Experiential Networked Intelligence with closed-loop control and cognitive functions and GSMA AI in Networks Guidelines (2021) provide operators with guidance on trustworthy, interoperable, and explainable AI deployment, ensuring alignment with multi-vendor 5G ecosystems. At the same time, AI introduces risks that cannot be overlooked, including model bias, unpredictable network behaviors, security vulnerabilities, and compliance challenges. Implementing robust validation, continuous monitoring, and explainability mechanisms is essential to mitigate these risks while fully harnessing AI’s potential, enabling self-optimizing 5G networks that deliver efficient, reliable, and high-quality digital experiences to users and industries alike. #AI #MachineLearning #5G #NetworkOptimization #QoS #QoE #CyberSecurity #RiskManagement

💡 Balancing Rapid AI Advancement With Operational Resilience: https://lnkd.in/dyCBBFxm ➡️ AI is scaling at breakneck speed — and that’s both exciting and risky. The infrastructures powering it — data centres, power, cooling, networks — are under enormous strain. Without built-in resilience, a cyber incident or physical failure could cascade into wide outages. 👉 To defend against that, we need deep visibility: mapping dependencies across IT, OT, and physical systems to uncover hidden failure paths. Then adopt “assume breach” strategies — microsegmentation, Zero Trust, and isolation — so that compromises are contained, not catastrophic. ▶️ Ultimately, building resilience isn’t optional — it’s mission-critical! Innovation only thrives when the foundation is secure. Governments, operators and the private sector must collaborate on shared intelligence, redundancy, and proactive defence to ensure AI’s potential doesn’t get undermined by operational fragility. #BusinessContinuity  #BuildingResilience  #OperationalResilience  #Risk  #AI

Building an AI-ready network isn’t just about bandwidth. It is also not about adding more hardware either. Being AI-ready means making your network adaptable, context-aware, and intelligent enough to keep up with the AI systems it supports. The next generation of campus and branch networks will be judged not by raw capacity, but by their ability to: Recognize AI-driven flows in real time. Prioritize critical workloads automatically. Secure sensitive data without slowing operations. In other words, AI is changing the rules of networking. Traditional metrics like throughput, uptime, latency are no longer enough. Networks need to think more like AI: Dynamic. Predictive. Responsive to changing conditions in real time. If your network can’t make decisions as quickly as the AI applications running on it, you’re not AI-ready, you’re just faster at doing the same old tasks. Being AI-ready is strategy, not just infrastructure. It’s about designing networks that: Scale intelligently. Adapt automatically. Deliver seamless experiences no matter where users or devices are. Bandwidth is easy. Adaptability is hard. And that’s exactly what will separate the networks and the engineers who thrive in the AI era from those who get left behind. 🔁 Repost to help more engineers stay ahead of the curve.

⚡ AI isn’t just changing workloads — it’s changing the physics of the grid. The rise of high-density AI computing has introduced a new phenomenon for data centers and utilities alike: AI power bursts. These sudden spikes in energy demand don’t just strain capacity — they create subsynchronous oscillations (SSOs) that can lead to overheating, equipment damage, and costly outages. Eaton’s latest innovation — an industry-first firmware update for its Power Xpert (PXQ) system — is designed to detect and mitigate these fluctuations. By using Edge-based analytics to spot SSOs in real time, operators can act before small disturbances escalate into major failures. Why does this matter? AI loads are unlike anything before — they fluctuate faster and harder than traditional workloads. Weak grids are especially vulnerable — regions already struggling with stability now face greater risk. Resilience is shifting from backup to proactive — detecting issues at the microsecond level is now just as important as redundancy. This is more than just one product launch. It signals a turning point: as AI scales, the industry must adopt grid-to-chip strategies that combine intelligent power distribution, real-time monitoring, and advanced backup. 👉 The takeaway: The future of AI data centers won’t be defined only by compute or cooling, but by how well we can engineer power systems that think as fast as the workloads they support. What’s your view — will proactive detection become the new baseline for data center resilience? #AI #DataCenters #EnergyManagement #Infrastructure #Resiliency Matt Hafford Michael Ferguson Lucy T. Georgiana Dabica Ben H. Rebecca Andrew

Cybersecurity in smart grids is no longer just an IT issue, it’s a business risk. A new 2023 study, “Trustworthy AI Framework for Proactive Detection & Risk Explanation in Smart Grid” highlights how AI must do more than detect threats. It must prove why decisions were made and quantify risk in real time. Why this matters to decision-makers: ⚡ Cyberattacks on distributed energy resources (DERs) can cripple grid operations. ⚡ Contracts & SLAs now demand accountability “who is liable if the AI fails?” ⚡ Regulators are pushing for explainable, auditable systems. The proposed framework uses Shapley value interpretation (for root-cause analysis) and Ward’s minimum variance formula (for real-time risk scoring) making AI explainable, reliable, and defensible. 👉 Full review link in comments. Before signing your next SLA, ask: Can this AI explain its security decisions and stand up in court? #SmartGridSecurity #TrustworthyAI #CyberRisk #Perficient #Innovate

AI has become one of the most important applications and workloads in Fortune 500 data centers. Yet, AI is fraught with serious issues and inaccuracies. Read the article in Data Centre & Network News - DCNN as enterprise AI and cyber storage leader Infinidat discusses how AI inaccuracies threaten the adoption of AI and shows how Retrieval Augmented Generation (RAG) innovation provides the to AI inaccuracies and hallucinations solution - https://okt.to/vpUjc7 Infinidat Bill Basinas Tim Dales Jason Nichols Ken Newell Ken Scott Fran Bruno Carly Shideler Adriana Andronescu Bryan Williams Patrick Lee Michael Colby David Beltrán Vera David Sawyer David Ross Dan Cornelius Daniele Maurizi Danny Seitz Danielle Rondeau Danielle Goode Daniel Potts Donato Ceccomancini Andrea Sappia Andrea Fraietta Corrado Scolari Giuseppe Coppola Milena Branca Stephan Rath Electra Siafaka Joseph Schubert Hanna V. JT Lewis Brandon McIntire Chad Warden Ted Larson Elliott Dun Lenovo Infrastructure Solutions Group Bret Gibbs Hande Sahin-Bahceci

A Look at Our Latest AI Features and Enhancements Enhanced Predictive Analytics   Our AI now leverages improved machine learning models that provide more accurate forecasts of energy demand and supply fluctuations. This helps optimize grid operations and reduce costs. Real-Time Anomaly Detection   New algorithms detect irregularities in energy consumption and grid performance instantly, enabling faster responses to faults and minimizing downtime. Advanced Load Balancing   We’ve upgraded load balancing capabilities to dynamically allocate energy resources more efficiently across distributed networks, improving reliability during peak usage. Automated Maintenance Scheduling   AI-driven predictive maintenance schedules now identify potential equipment failures before they occur, reducing unplanned outages and extending asset lifespan. Cybersecurity Integration   Our AI features now include embedded cybersecurity protocols that proactively monitor and defend against evolving threats, ensuring data integrity and network safety. User-Friendly Dashboard   The latest interface enhancements provide clearer visualizations and customizable reports, empowering operators to make informed decisions quickly. Which of these features would you like to explore in more detail or see demonstrated in your specific context?

The hybrid era is here — and it’s powered by IBM Power11! Unveiled by Questronix Corporation and IBM, Power11 is built to drive agility, resilience, and intelligence in the age of AI and quantum-safe security. Read more about the launch here https://lnkd.in/gah54Y-w #IBMPower11 #Questronix #QNX