Secrets to Success Workshop: Supercharge Your Adoption of AI
February 12 | Register Now! Get ready to supercharge and fast-track your adoption of AI! Join Pure Storage for an exclusive virtual workshop on Thursday, February 12th. We'll unlock the secrets for successfully transforming AI pilots and exploratory initiatives into experience-backed, production-ready deployments. Register now to secure your spot for this highly engaging, virtual experience exploring new horizons with AI: Hear about AI industry trends and best practices for successful AI deployments. Learn why the right storage platform makes a difference in production AI. Take a deep dive into MLOps workflows in production for various use cases as part of an interactive exercise. Collaborate with peers, learn best practices, and workshop potential solutions with AI experts. Immerse yourself in expert insight on architecting, deploying, removing risk, simplifying and fast-tracking AI into production through demonstrations and engagement with industry experts and peers. Register Now!
5 AI Predictions Every Infrastructure Leader Needs to Know in 2026
January 22 | Register Now! In 2026, AI moves from experimentation to execution—and infrastructure leaders are in the driver's seat. Organizations that build the right data foundations now will be the ones turning AI into a true competitive advantage. Join us as we explore five trends shaping the future of AI infrastructure, from unlocking the value of your data to architecting for production-scale AI workloads. Walk away with a clear roadmap for positioning your infrastructure as the engine that powers your organization's AI ambitions. In this webinar, you'll learn: How to get your data ready for AI All about the NVIDIA AI Factory and Data Platform What it takes to scale AI from pilot to production Practical ways to bring AI into your organization Register Now!
Stop Prompting, Start Context Engineering
This blog post argues that Context Engineering is the critical new discipline for building autonomous, goal-driven AI agents. Since Large Language Models (LLMs) are stateless and forget information outside their immediate context window, Context Engineering focuses on assembling and managing the necessary information—such as session history, long-term memory (embeddings, RAG indexes), and tool outputs—for the agent every single turn. The post asserts that storage, not the LLM or the prompt, is the primary performance bottleneck for AI at scale. The speed of the underlying storage architecture dictates the agent's responsiveness because it must quickly retrieve and persist context data repeatedly.AI is Growing Rapidly. Is Our Talent Pipeline Keeping Up? 🚀
The AI revolution is currently building at a record pace, but the industry is facing a massive "people pipeline" problem. Industry leader Carrie Goetz, Principal and CTO at StrategITcom, highlights in the article, Building the People Pipeline for the Data Center Boom, that with nearly 500,000 open roles and a third of the workforce nearing retirement, we can no longer rely on poaching talent from competitors. Goetz proposes we must shift our focus to structured, skills-based apprenticeships and widen our reach to include veterans, tradespeople and neurodiverse talent. By demystifying the industry and showing students that tech careers involve much more than just coding, we can build a sustainable future for digital infrastructure. The conclusion: It’s time to stop just building facilities and start intentionally building the human workbench that powers them. --------------------------------------------------------------- 📣 Community Question: If you’re in the industry, share with us your own 'unconventional' path into the data center world. What's the one skill you use daily that isn't taught in a traditional classroom? Let's discuss! Click through to read the entire article above and let us know your thoughts around it in the comments below!
Turn Your Data into a Competitive Advantage
AI adoption is accelerating across every industry—but the real gap isn’t in ambition, it’s in operationalizing AI reliably and at scale. If your organization is looking to move from early pilots to production-grade AI, FlashStack for AI shows how you can make that shift with confidence. FlashStack AI Factories, co-engineered by Pure Storage, Cisco, and NVIDIA, delivers AI Factory frameworks and provides clients a predictable, scalable path to train, tune, and deploy AI workloads—without introducing operational risk. FlashStack delivers meaningful advantages that help teams operationalize AI more effectively: Consistent, production-grade AI performance powered by NVIDIA’s full-stack architecture—ensuring compute, networking, and storage operate as a synchronized system for dependable training and inference. Faster deployment and easier scaling, enabled by unified management through Pure1 and Cisco Intersight, reducing operational overhead and accelerating time to value. Stronger cyber resilience and reduced risk, with SafeMode immutable snapshots and deep integration with leading SIEM/SOAR/XDR ecosystems to safeguard high-value AI data. Meaningful business outcomes, from shortening AI innovation cycles to powering new copilots, intelligent assistants, and data-driven services. Together, these capabilities help enterprises turn raw data and processing power into AI-driven results—securely, sustainably, and without operational complexity. Read More! FlashStack AI Factories
AI & Analytics Insights: Curing AI Amnesia with Sports Cars and Semi-Trucks
January 13 | Register Now! 11:00AM PT • 2:00PM ET To ring in 2026, host Andrew Miller revisits the topic of the decade—AI!—with Ian Saunders. He’s a true practitioner in the space who works directly with customers on a daily basis. We’ll wander through: AI hype cycle - We’re a couple years in and what lessons have we learned. New AI challenges - AI forgets who you are (aka “the need for Context Engineering”) and preparing enterprise data for LLMs (today’s biggest challenge for enterprise AI adoption). Heard of the MIT study that 95% AI initiatives fail? We’ll unpack the reasons. How Pure Storage helps with today’s AI challenges FlashArray™ + FlashBlade® + Portworx® to help with the real underlying challenges around Context Engineering (in some scenarios with 20X performance boost, thanks to KVA). Pure Storage Data Stream solves for today’s biggest challenge facing enterprises around successful AI PoCs. And don’t worry, we’ll fully deliver on curing AI amnesia and where sports cars and semi-trucks fit into the picture!Is Compute Scarcity Stalling Your AI Progress? ⚡
AI success isn’t just about the model. In the article, "Managing AI In An Era Of Compute Scarcity: Governance Takes Center Stage", Palanivel Rajan Mylsamy: Director of Engineering Program Management at Cisco explains why compute governance is the new priority for tech leaders. Mylsamy highlights that compute power has become the "new gold," making it the primary bottleneck for scaling. To succeed, organizations must move away from wasteful resource allocation and embrace intelligent routing and hybrid infrastructures that balance security with cost-effectiveness. Ultimately, you can’t scale AI on a weak foundation; true growth requires moving from "what’s possible" to "what’s operationally sound." 📣 Community Question: How is your organization handling the compute crunch? Are you leaning more toward cloud, on-prem or a hybrid model? Explain why. Let's discuss! Click through to read the entire article above and let us know your thoughts around it in the comments below!Pure Fusion Expert Demo: From Fleet Creation to Policy‑Driven Provisioning
December 16 | Register Now! Manual provisioning and reactive management can slow innovation and drain valuable IT time. What if you could manage your enterprise data intelligently? Join us for a Pure Fusion™ Expert-led Demos Webinar: Walk through Pure Fusion configuration and fleet creation to securely federate arrays and gain one, consistent data management experience across your environment. See remote provisioning in action—manage any array from any array and provision storage anywhere via GUI, CLI, or API. Learn how policy‑driven presets standardize protection, QoS, and naming for repeatable, error‑free deployments—and get AI‑driven placement recommendations. Register Now!OT: The Architecture of Interoperability
In previous post, we explored the fundamental divide between Information Technology (IT) and Operational Technology (OT). We established that while IT manages data and applications, OT controls the physical heartbeat of our world from factory floors to water treatment plants. In this post we are diving deeper into the bridge that connects them: Interoperability. As Industry 4.0 and the Internet of Things (IoT) accelerate, the "air gap" that once separated these domains is evolving. For modern enterprises, the goal isn't just to have IT and OT coexist, but to have them communicate seamlessly. Whether the use-cases are security, real time quality control, or predictive maintenance, to name a few, this is why interoperability becomes the critical engine for operational excellence. The Interoperability Architecture Interoperability is more than just connecting cables; it’s about creating a unified architecture where data flows securely between the shop floor and the “top floor”. In legacy environments, OT systems (like SCADA and PLCs) often run on isolated, proprietary networks that don’t speak the same language as IT’s cloud-based analytics platforms. To bridge this, a robust interoperability architecture is required. This architecture must support: Industrial Data Lake: A single storage platform that can handle block, file, and object data is essential for bridging the gap between IT and OT. This unified approach prevents data silos by allowing proprietary OT sensor data to coexist on the same high-performance storage as IT applications (such as ERP and CRM). The benefit is the creation of a high-performance Industrial Data Lake, where OT and IT data from various sources can be streamed directly, minimizing the need for data movement, a critical efficiency gain. Real Time Analytics: OT sensors continuously monitor machine conditions including: vibration, temperature, and other critical parameters, generating real-time telemetry data. An interoperable architecture built on high performance flash storage enables instant processing of this data stream. By integrating IT analytics platforms with predictive algorithms, the system identifies anomalies before they escalate, accelerating maintenance response, optimizing operations, and streamlining exception handling. This approach reduces downtime, lowers maintenance costs, and extends overall asset life. Standards Based Design: As outlined in recent cybersecurity research, modern OT environments require datasets that correlate physical process data with network traffic logs to detect anomalies effectively. An interoperable architecture facilitates this by centralizing data for analysis without compromising the security posture. Also, IT/OT convergence requires a platform capable of securely managing OT data, often through IT standards. An API-First Design allows the entire platform to be built on robust APIs, enabling IT to easily integrate storage provisioning, monitoring, and data protection into standard, policy-driven IT automation tools (e.g., Kubernetes, orchestration software). Pure Storage addresses these interoperability requirements with the Purity operating environment, which abstracts the complexity of underlying hardware and provides a seamless, multiprotocol experience (NFS, SMB, S3, FC, iSCSI). This ensures that whether data originates from a robotic arm or a CRM application, it is stored, protected, and accessible through a single, unified data plane. Real-World Application: A Large Regional Water District Consider a large regional water district, a major provider serving millions of residents. In an environment like this, maintaining water quality and service reliability is a 24/7 mission-critical OT function. Its infrastructure relies on complex SCADA systems to monitor variables like flow rates, tank levels, and chemical compositions across hundreds of miles of pipelines and treatment facilities. By adopting an interoperable architecture, an organization like this can break down the silos between its operational data and its IT capabilities. Instead of SCADA data remaining locked in a control room, it can be securely replicated to IT environments for long-term trending and capacity planning. For instance, historical flow data combined with predictive analytics can help forecast demand spikes or identify aging infrastructure before a leak occurs. This convergence transforms raw operational data into actionable business intelligence, ensuring reliability for the communities they serve. Why We Champion Compliance and Governance Opening up OT systems to IT networks can introduce new risks. In the world of OT, "move fast and break things" is not an option; reliability and safety are paramount. This is why Pure Storage wraps interoperability in a framework of compliance and governance, not limited to: FIPS 140-2 Certification & Common Criteria: We utilize FIPS 140-2 certified encryption modules and have achieved Common Criteria certification. Data Sovereignty: Our architecture includes built-in governance features like Always-On Encryption and rapid data locking to ensure compliance with domestic and international regulations, protecting sensitive data regardless of where it resides. Compliance: Pure Fusion delivers policy defined storage provisioning, automating the deployment with specified requirements for tags, protection, and replication. By embedding these standards directly into the storage array, Pure Storage allows organizations to innovate with interoperability while maintaining the security posture that critical OT infrastructure demands. Next in the series: We will explore further into IT/OT interoperability and processing of data at the edge. Stay tuned!👤 Addressing the "Shadow AI" Threat in Healthcare Security
Driven by clinician burnout and the desperate need for efficiency, healthcare providers are increasingly turning to unsanctioned, public-facing AI tools (like general-purpose chatbots) to assist with tasks. This practice, often referred to as Shadow AI, creates a major security risk because the data entered into these tools can leave Protected Health Information (PHI) exposed and compromise compliance with regulations like HIPAA. In the article, "In Healthcare, Threat of Shadow AI Outpaces Security as Clinician Adoption Accelerates", according to Nate Moore, Founder of Enlite IT Solutions Inc., the problem is that the pace of AI adoption is quickly outpacing security governance. The goal isn't to ban innovation, but to enable it safely. In lieu, Moore recommends a shift: instead of banning AI, organizations must create secure "AI sandboxes." These governed environments enable staff to test pre-vetted models safely, balancing innovation with data protection. 📣 Community Question: Given the balance between enhancing clinician efficiency and maintaining strict patient data security, what is the most vital step healthcare IT leadership should take right now to effectively manage the risks of Shadow AI? Let's discuss! Click through to read the entire article above and let us know your thoughts around it in the comments below!
