Financial Services Sessions at Accelerate 2026
Accelerate is almost here! 🙌 Check out our special track designed to connect with FSI professionals. Hear from Everpure experts about the latest trends and strategies for data management success in Financial Services. If this sounds right up your alley, we’ve curated a list of must-attend breakout sessions focused on all things Financial Services. Check out these don't miss sessions: Why 95% of AI Pilots Fail at Scale, and How a Finance Sector MSP Beat the Odds Sovereign by Design: Delivering a Cloud Operating Model within the Firm’s Own Borders Cyber Defense in a Flash: Spotting the Smoke Before the Fire Will you be joining us in person? Drop a comment below with the session you're most interested in and what you hope to learn!
Guarantee Data Availability: How to Create a Snapshot Bunker
June 23 | Register Now! Without data integrity and availability, recovery is difficult and may take days or even weeks. Everpure prioritizes remediation and recovery as the critical path to cyber resilience. Data availability is the foundation for reliable remediation and rapid recovery, enabling organizations to restore data regardless of the severity of a disaster or cyberattack. Why a snapshot bunker is the cornerstone of effective layered resilience Key considerations for architecting the bunker to ensure survivability How to set up and operate a snapshot bunker Register Now!Denver Pure User Group (PUG) meetup
Details Our next Denver Pure User Group (PUG) meetup is all about protecting and securing all your data. Join us to connect, learn, and engage with your local IT peers around strategies to battle ransomware, speed up recovery, and prepare business continuity solutions for disaster recovery. Discuss a tiered resiliency architecture and strategies to implement before, during, and after a cyber incident. Topic : Cyber Resilience and 1touch Venue Prost Brewing Co. - Northglenn Biergarten 351 W 104th Ave Unit A Northglenn, CO, 80234 Speaker Scott Taylor Director, Cyber Resilience, Field Solutions Architect Everpure Doug Gregory Area Vice President, 1touch Everpure Register here!"Where’s Waldo?", But for your Data
This past Saturday, my wife and I sat at my son’s college graduation ceremony doing what every proud parent does after running out of tears and tissues: staring at the giant screen, scanning a crowd of thousands, and playing a very emotional, very expensive version of Where’s Waldo? The camera pulled back and showed the graduating class. Thousands of caps. Thousands of gowns. Thousands of people who had just survived exams, group projects, late-night studying, bad cafeteria decisions, emotional phone calls home, and whatever personal version of “I’ll start the paper tomorrow” they subscribed to. Somewhere in that sea of mostly identical academic robes was my son. I knew he was there. We had dropped him off at college years earlier, paid tuition, bought supplies, endured move-in day, survived the separation anxiety, worried about him, cheered for him, and occasionally pretended to be calmer than we actually were. I knew exactly why we were in that room. But on that screen, in that moment, he was just one face among thousands. So I started searching for him. Every parent around me was probably doing some version of the same thing. We were not looking at a graduating class in the abstract. We were looking for our graduate. Everyone else on that screen mattered deeply to someone, but to us they were mostly context without identity: a massive, moving, emotional dataset with almost no metadata attached. That was the strange thing about the picture. It showed us everything and told us almost nothing. There were thousands of people on the screen, but unless you already knew who you were looking for, you did not really know what you were looking at. Somewhere between the pride, the camera angle, and my increasingly poor performance at parental facial recognition, my brain did what my brain unfortunately does. It connected a very human moment to the way enterprises think about data. Because this is exactly the problem most organizations have with their data. They know it is there. They know there is a lot of it. They know some of it is incredibly valuable, some of it is probably risky, and some of it is duplicated, outdated, forgotten, regulated, misplaced, or being accessed by people and systems nobody has thought about in years. But knowing there is a crowd is not the same thing as knowing who is in it. That is the part we do not talk about enough. For years, data management conversations were mostly about where the data lived, how it was protected, how fast it could be accessed, and how much it cost to keep it all running. Those things still matter. They will always matter. But they are no longer enough. The new question is not simply, “Where is the data?” The better question is, “What is this data, who does it belong to, why does it exist, who is using it, where has it moved, what risk does it carry, and should this AI model, business process, analyst, application, or employee be touching it at all?” That is a very different conversation, and that is why 1touch matters. Not because the industry needed one more product logo, one more acronym, or one more keynote phrase that sounds important until everyone quietly admits they are not exactly sure what it means. 1touch matters because it is aimed directly at the problem of not knowing. The lie of visibility Most organizations believe they have visibility into their data because they have tools that can show them infrastructure. They can show arrays, volumes, file systems, buckets, databases, dashboards, latency charts, replication status, backup jobs, snapshots, anomalies, alerts, and the occasional red icon that ruins someone’s morning. All of that is useful. None of it guarantees understanding. An IT team can tell you a volume is 87 percent full, but that does not mean they know it contains expired customer records, old HR exports, forgotten underwriting files, production data copied into a test environment, or a spreadsheet with 40,000 Social Security numbers created in 2018 by someone who left the company three reorganizations ago. A security team can tell you an alert fired, but that does not mean they know whether it represents real exposure, a false positive, or just another noisy event in a pile nobody has enough hours to investigate. A data team can point to a lake, a warehouse, a catalog, and a governance process, but that does not mean the data is clean, trusted, current, properly classified, or safe to feed into an AI workflow. This is the uncomfortable truth: enterprise data visibility has often meant visibility into containers, not contents. We could see the auditorium. We could count the very uncomfortable seats. But we still could not tell which graduate was my son. The graduation screen was not useless. It showed scale. It proved the event was real. It helped me understand the crowd. But until I could identify the person I cared about, the picture was incomplete. Enterprise data estates work the same way. The problem is not that organizations have no tools. They often have too many. The problem is that many tools see the surface of the environment but miss the identity, relationship, movement, and meaning of the data inside it. That gap was inconvenient in the old world. In the AI world, it is dangerous. AI does not forgive ignorance Before generative AI entered every boardroom conversation, the consequences of not knowing your data were already serious: compliance exposure, bloated infrastructure costs, security blind spots, slow audits, manual discovery, painful legal requests, cloud migration delays, and business users waiting weeks for access to information because nobody could confidently say what was safe to use. Then AI showed up and made the problem louder. AI feeds on data. Lots of it. Structured data, unstructured data, documents, emails, transcripts, PDFs, customer records, logs, knowledge bases, support case histories, SaaS exports, file shares, objects, and anything else that might help a model answer a question, summarize a situation, automate a workflow, or make a decision. That sounds exciting until you remember that most enterprises do not fully know what is in all of those places. And AI is not magic. If the input is wrong, the output inherits that problem. Sometimes the model hallucinates. Sometimes it exposes something it should not. Sometimes it makes a recommendation based on data that was never supposed to leave a specific jurisdiction. Sometimes it answers confidently from a document that was obsolete three policies ago. Sometimes it gives the right answer to the wrong person, which may be the scariest version of all because the technology can look like it is working while quietly violating the trust model of the business. That is why “AI-ready data” cannot simply mean “we pointed a model at a repository.” That is not readiness. That is hope with an API call. AI-ready data needs context. It needs classification, identity, policy, and confidence. It needs a way to distinguish between a harmless document, a restricted record, a regulated attribute, an exposed credential, and a data fragment that only becomes sensitive when connected to other fragments somewhere else. A number or a name by itself may not mean much. A location, transaction, or timestamp by itself may not mean much either. But connect the number to the name, the name to the patient record, the patient record to a geography, the geography to a regulation, the regulation to a storage location, and the storage location to an access path, and suddenly you are not looking at random data anymore. You are looking at risk. Or value. Often both. This is where 1touch becomes important, because its value is not just identifying patterns and sticking labels on files. Its value is in discovering, classifying, and contextualizing data across environments so organizations can understand not only what exists, but what it means. That distinction matters. The difference between labeling and knowing At graduation, every student had the same basic label: graduate. That label was accurate, but it was wildly insufficient. One graduate may be heading to medical school. Another may be joining a startup. Another may be the first person in their family to earn a degree. Another may have worked two jobs to get there. Another may have changed majors three times and somehow still finished on time, which frankly deserves its own medal. The label tells you the category. The context tells you the story. Data works the same way. A traditional tool might identify something that looks like a credit card number, Social Security number, email address, medical code, account number, or passport field. That is useful, but it can also create noise. Strings of digits appear everywhere. Test data looks real. Real data looks fake. A file name can lie. A folder path can be misleading. A database column called “ID” might be harmless, or it might be the key to everything. Context is what turns a guess into intelligence. 1touch approaches this problem by looking at the broader semantic environment around the data. It is not just asking, “Does this pattern match something sensitive?” It is asking, “What surrounds it? What system did it come from? Who accesses it? Where does it move? What other data is connected to it? What business process does it support? What regulatory meaning does it carry?” That matters because in the real world, data risk rarely lives in a single isolated field. It lives in relationships. The same way my son was not immediately identifiable to the room because he was wearing a cap and gown like everyone else, sensitive enterprise data is often not obvious because it is dressed like everything else. It sits in file shares, databases, cloud repositories, SaaS platforms, mainframes, archives, exports, and forgotten project folders. It blends into the crowd. The old approach was to scan the crowd every so often and hope you recognized enough faces. The newer requirement is continuous understanding: discovering data where it lives, watching how it moves, connecting fragments across systems, and building a living map of identity, access, classification, and risk. Not a once-a-year inventory. Not a spreadsheet. Not a governance theater exercise where everyone nods in a meeting and then goes back to copying production data into development because the test system “needed something realistic.” A living map. That is the real promise. Why this matters The value of 1touch can be easy to undersell if we describe it only as sensitive data discovery or Data Security Posture Management (DSPM). Those descriptions may be accurate, but they are not the business problem. A prospect is not waking up hoping to buy a classification engine. They are waking up with pressure from the board, auditors, regulators, cyber insurers, application owners, AI initiatives, cloud migration teams, and business leaders who want faster access to “clean” data without increasing risk. And for those of us who have been around this industry long enough to have a few emotional support scars, this problem is not new. We were talking about lifecycle data management and data classification projects 20 years ago. Kazeon, StoredIQ, and others were all trying to help customers understand what was hiding inside their unstructured data environments before the phrase “dark data” became a fashionable way to describe a very unfashionable mess. I personally used Kazeon back in 2006, before EMC acquired it and eventually killed it. The idea was right. The experience was painful. I remember a project where it took almost two months to scan the environment, process the results, and prepare the report. We finally sat down with the customer, proudly showed them the findings from roughly 5TB of unstructured data, and waited for the moment where they would appreciate all the classification goodness we had brought into their lives. Instead, the customer looked at us and asked the only question that mattered: “Where is the rest of my 55TB?” There are moments in a technical meeting when the room temperature changes without the thermostat being involved. This was one of them. Apparently the tool did not have permissions to scan the rest of the environment. So after two months of work, the result was technically accurate and practically incomplete, which is the most dangerous kind of confidence. We had a report. We had charts. We had findings. What we did not have was the whole truth. That is why this matters now. The enterprise data problem did not begin with AI. AI simply made the consequences of incomplete understanding much harder to ignore. Twenty years ago, a bad classification project meant a frustrated customer, an awkward meeting, and a lot of manual cleanup. Today, the same kind of blind spot can contaminate an AI pipeline, expose regulated data, break a sovereignty policy, delay a migration, or give executives a false sense of security. For existing customers, the value is even more strategic. They already trust the platform to store, protect, move, and serve their data. The next logical question is whether it can help them understand the data as well. That is the bridge 1touch helps build. That is important because customers are tired of stitching together disconnected tools where one product sees storage, another sees identity, another sees security events, another sees data catalogs, another sees cloud posture, and another sees compliance workflows. Everyone sees something, but nobody sees enough. Customers do not need more fragmented visibility. They need connected context. Most importantly, it helps us explain why the conversation has moved from where data sits to what the data actually means. Back to the screen Eventually, during the ceremony, I found my son. Definitely when his name was announced and he walked across that stage. But the moment stayed with me because it was such a simple reminder: seeing a crowd is not the same as knowing the people in it. Every person on that screen had a story, a history, a family somewhere in the stands trying to yell the loudest, and a future that was about to begin. From a distance, they looked identical. Up close, they were anything but. Enterprise data is like that too. From a dashboard, it can look like capacity, files, objects, tables, volumes, buckets, repositories, shares, records, and logs. But inside that data are customer identities, patient histories, citizens tax records, contracts, intellectual property, employee information, business secrets, stale copies, duplicate exports, forgotten archives, useful insights, hidden risks, and the raw material for the next generation of AI-driven business processes. The organizations that win will not be the ones that simply store the most data. They will be the ones that know what their data means. That is why 1touch matters. Because the future of data management is not just finding Waldo. It is understanding the entire crowd. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywashereSecurity Is Not a Feature — It's the Foundation
Let's get something out of the way upfront: this is not a ransomware horror story. This is not a "cyber resilience framework" deep-dive full of three-letter acronyms that could potentially make your eyes glaze over if it's not your cup of tea. And this is definitely not a pitch deck disguised as a blog post. This is the real story of how Everpure thinks about security — at the architecture level — and why that distinction matters more than most people realize when they're evaluating storage platforms. Because here's the thing: security isn't a bolt-on. It's not a checkbox. And it's certainly not a conversation you should have to schedule separately from the one about performance or reliability. At Everpure, security is baked in from the ground up — and once you understand how, you'll never look at a storage spec sheet the same way again. Start With the Five S's At Everpure, we talk a lot about what we call the Five S's of data: Simplicity, Speed, Scale, Sustainability, and Security. They're not independent pillars — they're interlocking principles that define every design decision we make. Simplicity because complexity is the enemy of agility. If you can't iterate quickly, you can't grow. Speed because we've been all-flash since day one — full stop. Every generation of our platform has been optimized around flash, not retrofitted for it. Scale because data doesn't stop growing, and your storage shouldn't hit a wall when your business doesn't. Sustainability because power, cooling, and physical footprint are real constraints — especially now, as those pressures trickle down from hyperscalers to everyone else. Security because none of the other four matter if your data isn't protected. Security is the one that tends to get either oversimplified ("we encrypt everything") or overcomplicated ("here's our 47-page compliance matrix"). Neither is helpful. What's helpful is understanding how it works, why it's different, and what it means in a real conversation with a real customer. The Compliance Landscape: What Customers Are Actually Asking About Before we get into the architecture, let's talk about the validations — because customers are increasingly asking about them, and the answers matter. FIPS 140-3 is the latest standard from the Cryptographic Module Validation Program (CMVP), managed by NIST. It validates that a cryptographic module — the thing actually doing the encryption — meets a defined security standard. Everpure's FlashArray is FIPS 140-3 validated. That's the current gold standard, and it matters especially as post-quantum cryptography conversations start entering the room. (More on that in a moment.) Common Criteria is an international standard for evaluating the security of IT products — not just storage, but networking, applications, hardware modules, and more. Everpure's FlashArray is certified under the Network Device collaborative Protection Profile (NDcPP) via NIAP, while FlashBlade holds an EAL2 certification. Independent testing and verification confirm that each platform meets its defined security target. You can actually enable Common Criteria mode directly on a FlashArray — it's a CLI command, not a professional services engagement. PCI DSS compatibility is table stakes in financial services, but it increasingly shows up in other industries too. It means end-to-end data masking, encryption in-flight and at rest, and a well-documented audit trail. Everpure's platforms are designed to support PCI DSS requirements natively — though it's worth noting that PCI DSS certification belongs to the merchant environment as a whole, not to any individual storage component. TLS 1.2 and 1.3 are the current standards for securing data in-flight at the management layer. Everpure standardizes these across all management communications — and yes, you can turn off older cipher suites if your security posture requires it. TAA Compliance means that Everpure's hardware is manufactured in the United States. For customers in regulated industries or government, this isn't a nice-to-have — it's a requirement. And for anyone who cares about supply chain transparency, Everpure can show its work. None of this is marketing fluff. These are independently validated, publicly verifiable certifications. You can find all of them — current CVE database, FIPS status, NIST 800-53 alignment, media sanitization documentation — at our Customer Trust portal. Bookmark it as It's fully public-facing and constantly updated. The Hardware Story: Why No Keys on the Drive Is the Point Here's where things get interesting. Take a Direct Flash Module — Everpure's approach to flash — and look at what's not on it. No CPU. No memory. No encryption keys. It is not a self-contained storage array. It is purpose-built flash media, and everything else — the intelligence, the encryption, the key management — lives in software. Why does that matter? Because self-encrypting drives (SEDs) are a pain. Anyone who's managed them in a regulated environment knows this intimately. When the encryption is in the hardware, you inherit all the complexity that comes with it: drive-level key management, FTL overhead, KMIP integration headaches, and the ever-present risk that a single drive failure or misconfiguration creates a data accessibility nightmare. Everpure's approach flips this entirely. Because the Direct Flash Module has no CPU, no memory, and no keys, all encryption is handled at the software layer — in Purity, running across the entire system. This means no hardware dependency, no FTL management overhead, and no encryption key tied to a specific piece of media. The portability this creates is remarkable. And as you'll see in a moment, it's the foundation of everything else. How Everpure's Encryption Actually Works Let's peel back the layers here, because this is genuinely cool — and it's the kind of thing that separates a confident storage conversation from a "let me get back to you" one. Everpure's encryption architecture is built around three components: The Data Encryption Key (DEK) is the actual key used to encrypt customer data. There's one per array, and it doesn't change. You might think: why would you never rotate the key that's protecting your data? The answer is that the DEK never needs to rotate because of what wraps it. The Key Encrypting Key (KEK) is a key that encrypts other keys — specifically, it wraps the DEK. This is standard cryptographic practice, and it's the mechanism that makes key rotation safe, fast, and completely transparent to the workload. The Armored DEK is the DEK after it's been wrapped by the KEK. This is the piece that gets distributed. At no point is the raw Data Encryption Key exposed in clear text. It's always wrapped, always protected. Here's where the architecture gets elegant: when a FlashArray or FlashBlade initializes, it generates a KEK. That KEK wraps the DEK to create the Armored DEK. The Armored DEK is stored as a complete copy in every Direct Flash Module header — but it cannot be decrypted without the KEK. The KEK itself is derived from a scrambled key, which is split into individual shares and distributed one per DFM header using a sharding algorithm that requires a quorum to reconstruct. What does quorum mean in practice? The system can tolerate drive losses and still unlock all data, as long as enough DFMs remain present and healthy to reconstruct the scrambled key. No single drive is a single point of failure for your encryption keys. When a read request comes in, here's what happens: the system reconstructs the scrambled key from a quorum of DFM shares, derives the KEK, and uses it to unwrap the Armored DEK — exposing the DEK temporarily in memory, never persisted in clear text — and uses it to decrypt the data. The process is reversed for writes. At no point is customer data stored or persisted in clear text. Everything written to NVRAM is encrypted before it ever reaches upper-level system processes. This isn't "we encrypt everything." This is a specifically designed cryptographic architecture that is portable, resilient, and opaque to any unauthorized party — including someone who physically removes a drive. Key Rotation: The Part Most Vendors Skip By default, Everpure rotates the Key Encrypting Key every 24 hours. Automatically. No KMIP server required. No scheduled maintenance window. It just happens. When a KEK rotates, the system generates a new one, re-encrypts the Armored DEK, and redistributes the updated scrambled key shares across all DFM headers. The DEK itself doesn't change — the workload never sees it — but the wrapping layer that protects it is refreshed daily. When drives are added or removed, the system treats this as a high availability event: it generates a new KEK immediately, re-encrypts everything, and rebalances the shards across the new drive configuration. The key material always matches the current system state. And when a DFM is removed from the system? The scrambled key shares on that drive correspond to a KEK that no longer exists — or will be rotated away within 24 hours. A removed drive becomes cryptographically useless. This is how Everpure delivers what some would call "instant media sanitization" — not by wiping the drive, but by invalidating the key that makes its contents meaningful. Rapid Data Locking: When You Need the Nuclear Option For environments where security isn't just a compliance requirement but a physical reality — air-gapped facilities, defense deployments, high-security data centers — Everpure has a capability called Rapid Data Locking (RDL). The concept: the Key Encrypting Key can be placed on a pair of hardware security tokens (one YubiKey per controller, two total) and inserted into the array. As long as the tokens are present, the array operates normally. If they are removed and the array is subsequently rebooted or power-cycled, the array cannot complete startup without the tokens present — the data remains physically intact, but it is cryptographically inaccessible. The array becomes, in the most literal sense, an expensive brick. Reinsert the tokens and power the array back on, and it boots up normally. This is the kind of capability that used to require expensive, bespoke security architecture. For Everpure customers, it's a feature of the platform. Dark Sites Are Getting Less Dark One more topic worth addressing: dark site deployments. Air-gapped environments have always involved painful tradeoffs — disconnected from cloud management, manual support processes, limited visibility into system health. That's changing. Dark site customers can now see their assets within Pure1 — subscriptions, health status, the ability to open and manage support cases — without compromising their air-gap requirements. Log obfuscation tooling is available today and will be integrated directly into the platform going forward, giving customers granular control over what telemetry leaves their environment and when. For partners and customers managing dark site deployments, this is a meaningful quality-of-life improvement. And it's consistent with how Everpure builds everything: the security architecture makes the operational flexibility possible, not the other way around. The Takeaway Security conversations in the storage industry tend to go one of two ways: a recitation of certifications that nobody fully understands, or a vague reassurance that "everything is encrypted." Neither builds confidence. Neither answers the real question, which is: how does this actually work, and why should I trust it? Everpure's answer starts with architecture. Software-managed encryption, no hardware key dependency, automatic key rotation, cryptographic portability, quorum-based scrambled key distribution, and capabilities like Rapid Data Locking that scale to the most demanding security requirements in the world. The certifications — FIPS 140-3, Common Criteria, TLS 1.3, TAA — aren't the story. They're the evidence. The story is that security was designed in from the beginning, not layered on afterward. That's a meaningful difference. And now you know why.Part 2: MCP Is Interesting. Everpure Fusion Makes It Useful.
In Part 1, I tried to give MCP a proper “…splanation,” mostly because the first several times I heard people talking about Model Context Protocol, I had the same look Joey had in Friends when the salesman asked him if his friends ever had a conversation and he just nodded along without really knowing what they were talking about. That was me. MCP this. MCP server that. Agentic AI. Tool calling. Context windows. Protocols. Hosts. Clients. Servers. At some point, I realized I was nodding with the confidence of a man who had understood approximately 41% of the conversation and was hoping nobody asked a follow-up question. The simple version is this: MCP is a standard way for AI applications to connect to tools and data. It is not the AI model itself. It is not the magic brain. It is the plumbing that lets the AI reach into approved systems, ask better questions, retrieve useful context, and potentially take action through well-defined tools. That is important in the abstract. But for Everpure customers and prospects, it becomes much more interesting when we stop talking about MCP as a general AI concept and start talking about what it could mean for storage operations, data infrastructure, and Everpure Fusion. Because this is where the conversation moves from “AI is coming someday” to “your infrastructure may already need to be ready for how AI will interact with it.” Everpure recently published a blog with a sneak peek of the Everpure Fusion MCP Server, describing it as an open-source service that connects AI assistants to Everpure Fusion storage fleets through the Model Context Protocol. The important part is not simply that an AI assistant can talk to storage. That would be interesting, but it would also be easy to misunderstand. The important part is that the assistant can interact with the storage environment through the Fusion control plane, which already understands fleet-wide context across FlashArray and FlashBlade. That distinction matters. Without Fusion, many environments are still managed in a way that looks very familiar to anyone who has spent time supporting infrastructure. One array over here. Another array over there. Scripts in one folder. Notes in another. Naming standards that started strong and then apparently met reality. Screenshots in tickets. Tribal knowledge in the heads of a few people who somehow remember which workload lives where, which array is doing what, and why nobody should touch that one volume because “there was a reason,” even if nobody is entirely sure what the reason was anymore. That model may work, but it does not scale gracefully. More importantly, it is not especially friendly to automation, and it is definitely not ideal for AI-assisted operations. Most troubleshooting in mature environments is not hard because people lack tools. It is hard because the context is not immediately obvious. The storage admin has one view. The DBA has another view. The virtualization team has another view. The application owner has a completely different view, usually delivered through a ticket that says something deeply scientific like “the app feels slow.” Everyone may be looking at a valid piece of the puzzle, but the real work is in the correlation. Which volume maps to which workload? Which array is hosting it? What did latency look like during the reported window? Were IOPS elevated? Was bandwidth constrained? Did anything change recently? Are we looking at a storage issue, a database issue, an application issue, a noisy neighbor, a misconfigured VM, a bad query, or just another case of “the network is innocent until proven guilty, but still somehow looks suspicious standing there”? That is where Fusion and MCP together become compelling. The Everpure Fusion MCP example makes the idea real. Instead of forcing an administrator to manually build low-level REST API calls or jump between tools, the MCP-aware AI assistant can query Fusion through higher-level tools exposed by the MCP server. In the example Everpure blog described, a storage admin can ask about workloads and volumes supporting a production SQL environment, including arrays, IOPS, latency, and bandwidth over a recent time window. The assistant can then correlate that storage perspective with information from another MCP server, such as SQL Server context around database files, wait types, and query behavior. That does not mean the AI replaces the storage admin. It does not mean the AI replaces the DBA. It does not mean everyone goes to lunch while the robot fixes production. And this is where I need to bring in The Big Bang Theory again, because apparently this is who I am now. There is a scene in the show where Raj is very open to the idea of aliens and extraterrestrial life. At the planetarium, Raj can look at flashes of light in the sky and talk about how scientists cannot fully rule out the possibility of alien civilizations. It is funny because Raj is a scientist, but he is also Raj, so the line between rigorous possibility and “maybe the aliens are waving at us” gets wonderfully blurry. That is how some people talk about AI operations right now. A light flashes in the sky, and suddenly someone is ready to announce that the robots are here to run the data center. Let’s not do that. The point is not that the AI is an alien civilization arriving to take over infrastructure operations. The point is that the interface is changing. The way humans interact with infrastructure is starting to move from manual lookup, command execution, and tribal knowledge toward assisted reasoning, guided action, and cross-system correlation. That is much more practical than aliens. It is also much more useful. Fusion already gives customers a fleet-wide control plane. It gives you the ability to think above individual arrays, above one-off configuration, and above the old habit of managing infrastructure like every system is its own little island with its own weather pattern. MCP gives that control plane another interface, one designed for the way AI agents work. This is why Fusion adoption matters. If your environment is still managed mostly array by array, script by script, ticket by ticket, and screenshot by screenshot, then AI can only help so much. It may summarize the pain beautifully, but it is still summarizing pain. When you use Fusion to create a more consistent, policy-driven, fleet-aware operating model, you are not just modernizing storage management. You are making the environment more understandable to automation, to operations teams, and now to AI agents that need structured context in order to be useful. That is a very different conversation from “look, the AI can query storage.” The better conversation is this: if AI is going to become part of operational workflows, then your infrastructure needs to be ready to participate in those workflows. Fusion is one of the ways you prepare for that. Not someday. Now. And Fusion is not the only example of this direction. Another Everpure technical article shows how an MCP server can be built to integrate with FlashBlade, allowing an AI assistant to query system data and even take direct actions through a natural-language interface. That example is useful because it shows the bridge between the old world and the new one. In the old world, storage management often meant CLI commands, scripts, API calls, screenshots, and specialized knowledge living in the heads of a few very tired people. In the new world, those capabilities can be surfaced through an AI-assisted experience that understands the available tools and can help operators ask better questions in plain English. Again, that does not mean the AI should blindly run your infrastructure while everyone disappears. Please do not read this article and tell your change advisory board that “the blog guy said the robot can handle it.” That is not the point, and I would like to remain welcome in polite infrastructure society. The point is that the operational model is changing. For years, we have talked about automation in infrastructure, but a lot of what we called automation still required a human to know exactly what to automate, where to look, which command to run, which script was safe, which API endpoint mattered, and which piece of documentation had not quietly aged into fiction. AI-assisted operations changes the interaction pattern. Instead of always beginning with the operator knowing the exact command or API call, the operator can begin with the question. Why did this workload slow down? Which volumes support this application? What changed in the last four hours? Which arrays are carrying the highest latency? Which workloads are consuming the most bandwidth? Which policies are inconsistent across the fleet? Where do we have capacity pressure? Which storage objects are tied to this SQL environment? Those are the kinds of questions humans actually ask when something is happening. MCP gives AI assistants a standard way to ask approved systems for the data behind those questions. Fusion gives the storage estate a more consistent, policy-aware, fleet-level way to answer. That combination is where the opportunity lives. Now, because this is enterprise technology and not a children’s book, we also need to talk about the dangerous part. One of the readers posted this comment on Linked in yesterday: The moment an AI system can access tools and data, the conversation changes. A chatbot that gives a bad answer is annoying. An agent that takes the wrong action in a business system can become a real incident. If a model can read sensitive files, query databases, send messages, modify records, trigger workflows, or touch infrastructure, then security is not a feature. Security is the premise. This is where some of the MCP enthusiasm needs adult supervision. We have spent years telling users not to click strange links, not to approve unknown applications, not to reuse passwords, and not to download random files. Now we are building systems where an AI assistant might read strange content, call external tools, and act on behalf of the user. That can be incredibly powerful, but only if we are honest about the risk. In some ways, MCP may expose organizational problems faster. If your data is scattered, stale, contradictory, or politically curated, an AI agent connected to it will not magically produce truth. It may simply produce a more polished version of the confusion. If your workflows are unclear, connecting AI to them may help automate the ambiguity, which is not quite the same thing as progress. The model can gather information, call tools, and complete steps, but people still need to define what should happen, what should not happen, what requires approval, and what good looks like. For Everpure customers and prospects, the more important question is not whether MCP is interesting. It is whether your environment is ready for this kind of interaction. That is where I would encourage customers to take a serious look at Fusion. Not because Fusion is another checkbox on a feature list, and not because every new technology conversation needs to end with someone saying “platform” three times into a mirror. Fusion matters because it changes the operational model. It gives you a way to manage data infrastructure as a fleet, with policy, consistency, automation, and context. Those are exactly the things AI agents need if they are going to do more than produce nicely formatted guesses. If you already met all the prerequisites (Purity 6.8.+, LDAP enabled), use it. Explore it. Get comfortable with it. Stop thinking about Fusion as something reserved for a future automation project after everyone finally gets through the current list of fires, renewals, upgrades, and meetings that should have been emails. MCP may be the plumbing that helps AI connect to the enterprise. Fusion helps make the storage environment worth connecting to. And that is the real call to action. Fusion is how Everpure customers make sure their data infrastructure is ready for it. Appreciate you reading. Dmitry Gorbatov © 2025 Dmitry Gorbatov | #dmitrywashereTechSummit: Seattle
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