I add up the numbers. They come to $15.3 billion for 2026. In the accompanying text, the entire market is supposed to reach just $7.5 billion by 2034. On the forecast publisher’s website, the same figures have already migrated to 2025–2035 in another table.

Perhaps the authors were counting different segments or mixed up the years. Without a methodology, there is no way to tell, but explaining the discrepancy was their job. As it stands, fifteen companies in this chart have somehow sold twice the size of the future market. Diego seems less impressed than LinkedIn.

What interests me is how readily we share a graphic like this when our own logo sits high enough. Sometimes a calculator is all it takes to break the marketing spell.

Cybersecurity used to tell a simpler story: a threat emerged, a startup developed a defence, found a niche, grew, got acquired or disappeared. Of course, competitors always existed, and not every startup invented a new category. But specialisation at least made it possible to explain what the customer was paying for.

One comparison has stayed with me from those “sweet 2000s”: margins of 20–30% on certain security deals versus 3–5% on fiercely competitive hardware. That is a personal recollection of channel economics, not an established industry average. Vendor, distributor and integrator margins are different things; a discount off list price is not profit.

Still, the appeal of the business is clear. You can charge more for scarce expertise and credible protection than for another almost interchangeable box.

This is where, for me, the extra work of presales begins. A good engineer understands how a solution works. A good presales engineer must also explain why the customer should pay for this particular solution, what it costs to operate and which other purchase will have to wait. Being technically right does not, by itself, unlock a budget.

And budgets are limited. An agent and a firewall may solve different problems while competing for the same money. Eventually, a vendor asks a wonderful question: why keep justifying a standalone product when you could offer the whole package?

Enter homegrown alternatives to adjacent products, startup acquisitions, bundles and ecosystems. Palo Alto Networks calls it platformization: bringing separate tools together on a single platform. Increasingly, I hear Californication: everyone is integrating with everyone, buying each other and promising the customer an exclusive relationship while eyeing the budget next door. Writing this, I feel a little like Hank Moody - just without the old Porsche. Yet.

The customer may genuinely have fewer integration headaches. The vendor gets a larger share of spending, and leaving becomes more expensive for the customer. Both can be true at once.

Big IT companies are expanding into security too. They already have users, clouds, enterprise contracts and sales channels. Why leave the adjacent budget to someone else? Sometimes the result is a strong product. Sometimes it is a promise that will never catch up with the presentation. Fake it till you make it. Sleep. Rave. Repeat.

I think of American Beauty and that familiar obsession with appearing successful. It fits the corporate stage remarkably well.

This year, I started watching stock-market candlesticks and became much less responsive to “amazing.” I began asking more often how a beautiful story turns into revenue, cash flow and company value.

Market capitalisation affects compensation, fundraising and the ability to buy competitors. The Wolf of Wall Street comes to mind as a film about selling the desire to get rich. A compelling story about the future becomes a valuable commodity in its own right.

In AI, the familiar plot has been put on fast-forward. At the top are countless assistants, agents and services making similar promises. Underneath sit capital-intensive models, clouds, chips, data centres and electricity. Building an application may become cheaper, while competing at the frontier of model development takes resources on an entirely different scale.

Cybersecurity and AI are already intertwined in the same cycle: AI helps attack, AI helps defend, and AI itself needs protection. Anthropic describes the use of its models in cyber operations and its efforts to counter that misuse. The markets still have boundaries, but the technology creating new opportunities also expands the attack surface and demand for oversight. The security industry knows this economic logic well.

“Everyone is a programmer now” is a wonderful slogan. Getting a working prototype has become easier. Making it reliable, securing it, maintaining it and finding a paying customer is a separate job. If building an app is easy for you, it is easy for your competitor too.

AI can deliver enormous benefits and still disappoint investors in individual companies. Growing usage guarantees neither high margins nor returns that justify every valuation. Hardware buyers, cloud providers and application developers capture different portions of the cash flow.

Then I read Dario Amodei’s new essay. He warns that within 6–12 months, a more capable swarm of AI agents acting against human intentions could be able to take over the internet with a persistent botnet. That is his risk scenario. But in my head, the corporate prose has already been shortened to: “AI will build a botnet and kill the internet.”

This is where the two stories fully converge. The industry promises a technology that will change everything, then warns that without sufficient oversight it could destroy the infrastructure it runs on. The warnings of catastrophe are getting louder. And the commercial duality is striking: capabilities drive demand for adoption; dangers drive demand for protection and regulation.

Amodei proposes external evaluators embedded in labs, coordination within what Amodei calls “democratic countries” and global agreements. We should scrutinise both the risks and the rules: who will pay for compliance, who gets to evaluate everyone else, and how open will the market remain to new entrants? A sincere concern for safety can coexist with measures that strengthen the market leaders.

Conversations with colleagues in the US, the UK and the EU have left me with the impression that the American market is mostly absorbed in its own reflection, while the rest of us catch the spray. I can speak for those conversations, not for the whole world. Semiconductors, energy, research and demand exist far beyond the United States. Wall Street may set the volume, but it does not make all the music.

So my oversaturation hypothesis is specific: in parts of cybersecurity and AI, there are too many interchangeable offerings relative to customers’ willingness to pay for them separately. That does not mean the world has enough protection or that AI is useless. It means that justifying one more subscription is getting harder.

People will say I am taking too broad a view. Perhaps. The window of my childhood bedroom, on the ninth and top floor, looked out over the endless Kazakh steppe. Where others saw neighbouring windows, a slide, benches and a sandpit - or even their own swimming pool and barbecue grill - I had a horizon.

That does not make me smarter. It just taught me to look beyond my own courtyard.

But these days, before discussing the market’s horizons, I try to add up the numbers. Diego approves, if only because it does not require getting off the sofa.

© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

On March 14, 2023, OpenAI introduced GPT-4.

Eight days later, on March 22, the Future of Life Institute published an open letter. It called for an immediate pause of at least six months in the training of systems more powerful than GPT-4. The pause was supposed to be public, verifiable, and shared by all major players. If the labs could not agree, the authors wanted governments to impose a moratorium.

At first, more than one thousand people signed the letter. Over time, the number passed 31,000. The signatories included Elon Musk, Steve Wozniak, Yoshua Bengio, Stuart Russell, and other well-known researchers and business leaders.

The letter asked big and uncomfortable questions. Should machines flood our information space with propaganda and lies? Should we automate every job? Should a few unelected technology executives make decisions about the future of civilization?

It sounded serious. Perhaps even historic.

There was no pause. Not even for a day.

Not because the risks had disappeared. And not because anyone had proved that the next generation of models was safe. The pause failed because the letter offered a moral answer to a problem that had already become an economic, technological, and geopolitical race.

Signing the letter cost nothing. A real stop would have cost market share.

Why the Pause Was Doomed

First, the letter created no obligations. There was no regulator, verification process, or penalty. There was not even an accepted way to decide when a system had become “more powerful than GPT-4.”

More powerful at what? Mathematics? Coding? Parameter count? Context length? The ability to hallucinate with confidence during a board meeting?

Second, the people with their hands on the controls did not press the brake. Sam Altman, Sundar Pichai, and Satya Nadella did not sign the letter. The companies with the models, clouds, capital, and computing infrastructure made no shared promise to stop.

Third, this was a classic prisoner’s dilemma. One participant may honestly believe that a pause makes sense. But it cannot know whether its competitors will also stop. A lab that freezes its research alone will not save humanity. It will simply give its competitors a six-month lead.

Fourth, the countdown started after the starting gun had already fired. The models released in the spring and summer of 2023 were already in development. In July, Elon Musk, who had signed the pause letter, publicly launched xAI. Today, its website proudly describes the Colossus cluster with 200,000 GPUs.

We cannot honestly say that every one of these models broke the call not to create a system “more powerful than GPT-4.” The vague definition makes that impossible to prove. But we can say one thing with confidence: the race did not stop for a single day.

During the time that was supposed to be used to create common rules, the market turned generative AI from an experiment into a required part of almost every large technology company’s strategy.

An even more revealing event took place on May 30, 2023. The Center for AI Safety published a short statement on extinction risk. It was signed by Sam Altman, Demis Hassabis, Dario Amodei, Geoffrey Hinton, and Yoshua Bengio. The leaders of major AI labs publicly accepted that AI could create risks on the scale of pandemics and nuclear war. Then they continued the race.

I do not think they were all lying. The conclusion is more uncomfortable: the system was stronger than the personal beliefs of the people inside it. Warning about risk became compatible with moving faster. Safety became a separate workstream, a framework, and a set of presentations. It did not become a brake pedal.

The industry did not ignore safety after the letter. It turned safety into an institution. New teams, committees, frameworks, and budgets appeared. So did a whole job market: alignment researchers, model evaluators, red team engineers, AI governance leads, and safeguards analysts.

In July 2023, OpenAI announced a new Superalignment team. It promised the team 20% of the compute it had secured over four years and openly recruited new researchers and engineers. Less than a year later, the team was disbanded and its work was moved into other groups. The company then created a Safety and Security Committee, whose stated role was to make recommendations to the board.

This does not mean that people in such teams are pretending to work. Many of them do important and difficult work. But a department, a budget, and a list of job openings do not create a brake. The real question is not how many safety specialists a company employs. The question is whether they have the formal power to stop a training run or a release. Without that power, safety remains a parallel workstream. It studies, tests, classifies, and recommends while the main machine keeps accelerating.

In the end, there was no shared voluntary pause and no global government moratorium.

The Letter Was Not About Electricity

We should not rewrite history. The 2023 letter was mainly about loss of control, disinformation, job automation, and risks to civilization. It was not an environmental manifesto.

But energy became the most physical sign of the failed pause. We can debate model capabilities forever. Megawatts are harder to debate. They appear on the power bill.

According to the International Energy Agency, data centers around the world used about 415 TWh of electricity in 2024. That was around 1.5% of global electricity use. In the IEA base case, the figure will reach 945 TWh by 2030, more than double the 2024 level.

According to later IEA data, electricity use by all data centers grew by another 17% in 2025. AI-focused data centers grew by about 50%. And no, rooftop solar will not cover all of this extra load. The IEA expects natural gas and coal to supply more than 40% of the additional data center electricity demand through 2030.

This does not mean that AI will burn all the electricity on the planet tomorrow. That claim would be the same aggressive marketing from the opposite side. But the direction is clear. Individual components are becoming more efficient, while total consumption is growing even faster.

When Fear Became a Market

After ChatGPT, almost the entire IT industry changed its language within a few quarters.

Servers became AI factories. Network fabrics became AI fabrics. Normal storage arrays became AI-ready storage. A document catalog became data intelligence. Search became RAG. A container with a model became private enterprise AI.

This does not mean that these technologies are useless. Many of them are very good. The problem starts when a fact that is true at the component level is presented as a major system-level benefit. All it takes is removing the denominator.

The best marketing rarely lies about the numerator. It hides the denominator.

The Magic of Twenty Percent

Let us say that a new storage platform uses 20% less electricity than the old one. That is a good result. It should be part of the platform decision.

Then comes the marketing jump. The saved power can supposedly be used for more GPUs, more AI cluster capacity, and less GPU starvation.

Now let us put the denominator back.

The IEA estimates that servers use about 60% of the electricity in a modern data center on average. Storage uses around 5%. Networking uses up to 5%. Cooling ranges from about 7% in efficient hyperscale facilities to more than 30% in older enterprise data centers.

If a component that uses 5% of the total becomes 20% more efficient, the data center directly saves about 1%:

5% × 20% = 1%.

The final result may be slightly higher because cooling demand also falls. But this is not a new computing era.

In a 1 MW data center, storage would use 50 kW at this ratio. A 20% reduction saves 10 kW. This is close to the rated maximum power of one eight-GPU DGX H100, before adding the power needed for its network and cooling. About one extra node for each megawatt of infrastructure is not zero, especially in a large data center. But it is far from the major expansion of AI capacity suggested by a polished presentation.

The savings are real. The interpretation is inflated.

We have not even asked whether those free kilowatts can really be turned into GPU capacity. A new node needs rack space, the right power density, cooling, networking, licenses, capital, and available capacity in the correct part of the data center. Electricity does not teleport from a storage array to an accelerator when someone clicks a button in PowerPoint.

Idle Does Not Mean Full Power

The second popular story goes like this: an expensive GPU waits for data while still using full power, producing maximum heat, and turning electricity into nothing.

GPU starvation is real. An accelerator may wait for the CPU, network, synchronization between nodes, preprocessing, the scheduler, memory, or storage. Low use of expensive hardware is bad economics.

But the words “still using full power” create the wrong physical picture.

The official maximum power of a DGX H100 is 10.2 kW. In measurements of an eight-GPU H100 node at Brookhaven National Laboratory, stable idle power was about 1.8 kW, or around 18% of the rated maximum. The highest average power during a real training workload in the same study was about 7.79 kW. The details are available in this study of H100 node power use.

An entire idle farm will, of course, use more than the total idle power of its GPUs. CPUs, memory, storage, networking, fans, UPS systems, and cooling still use electricity. Short I/O pauses may also be too brief for the hardware to enter a deep power-saving state. But stable idle is not full power.

To prove that storage is the reason GPUs are not doing useful work, we need before-and-after measurements of:

• GPU utilization and power draw;

• the share of time lost to I/O wait;

• throughput across the whole data pipeline, not only the storage array;

• completion time for the same job;

• energy used for a completed training run or another useful result.

Without those measurements, this is not architectural proof. It is a carefully selected story.

Selling “AI-Ready Data”

The third story is even easier to sell because its denominator is almost impossible to see.

Enterprise data really is messy. It lives in file shares, databases, SaaS platforms, archives, and backups. It includes duplicates, old versions, personal data, trade secrets, and files whose owner cannot be found.

Discovery, classification, deduplication, normalization, and access control are necessary. In the late 1990s, Bruce Schneier popularized a simple idea in information security: security is a combination of people, process, and technology. Buying one more platform cannot replace responsibility, governance, or an understanding of your own data.

So why do we now act as if a new storage system or data intelligence layer can automatically turn this mess into “AI-ready data”?

A platform can read a file quickly. It can find a passport number, add a tag, create an embedding, write to a vector index, and apply a policy. But it does not know:

• which of the three copies of a report is final;

• whether the company has the right to use a document for training or RAG;

• whether a formally correct document matches the real business process.

These are questions of responsibility, meaning, law, and governance. Technology can support the process. It cannot create organizational truth from nothing.

Well-prepared data is not an abstraction. The abstraction is the belief that buying one more platform will prepare it automatically.

Efficiency That Saves Nothing

Let us assume that every vendor claim is honest. New GPUs perform more operations per watt. Storage uses less power per terabyte. Liquid cooling reduces PUE. Models become smaller. What happens to the total load?

Most likely, new computing will use the released capacity.

This is the old Jevons paradox. Better resource efficiency reduces the cost of each unit of useful work and encourages more total use. In the competitive AI race, saved power rarely becomes electricity that is never consumed. It becomes extra budget for the next batch of GPUs.

Energy efficiency becomes permission to scale, not a limit on scaling.

This is why two statements can be true at the same time:

1. Each new component is more efficient than the previous one.

2. The industry as a whole uses more and more electricity.

The marketing presentation shows the first. The power grid sees the second.

Where It All Went Wrong

It did not happen when ChatGPT appeared. It did not happen when NVIDIA released CUDA. It did not even happen when several companies decided to build models with hundreds of billions of parameters.

It went wrong when the warning became part of the sales pitch, while safety and sustainability became extra reasons to keep scaling.

We learned to say all of these things at the same time:

• AI may change or destroy the world we know;

• so you must buy our AI platform now;

• energy use is becoming a critical problem;

• so let us free 1% of capacity and install more GPUs.

This is not a conspiracy. A conspiracy would require coordination. This is a normal system of incentives. Everyone makes a rational local decision, while no one controls the overall result.

The accelerator vendor sells the drilling rig. The network vendor sells the pipes. The storage vendor sells the tank. The cooling vendor sells the pumps. Cybersecurity promises to build a fence around the well. Data governance promises to put a label on every bucket.

Everyone gets paid when the machine keeps moving. No one gets a bonus for asking whether we should drill at all.

What Is Left?

I do not believe that another collective letter will stop the race. In 2023, there may still have been a short political window for it. Now AI has budgets, careers, national programs, data centers, and investor expectations built around it.

But we still have the denominator.

Every time a vendor promises to free power, speed up an AI pipeline, or feed starving GPUs, we should ask simple questions:

• What share of total energy use comes from your component?

• What percentage of time do the GPUs really spend waiting for it?

• How did completion time change for the same task?

• How many kWh are used for each useful result before and after?

• And the most uncomfortable question: what measurable value will the computation we are about to run create?

Aggressive marketing does not like denominators. With a denominator, a 20% miracle quickly becomes 1%.

For years, people called data the oil of the twenty-first century. When the generative AI well appeared, sellers of drills, pipes, pumps, and buckets of every size quickly gathered around it. Almost no one asks whether there is any oil in the well, who needs it, or how much energy will be burned to extract it.

The task is now simpler: shout louder, push harder, and place your bucket under the spray before someone else does.

The 2023 letter asked everyone to step away from the well for six months and agree on the rules. It failed for one simple reason: no one wanted to be the last ethical team in the market.

© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

Marketing works by narrowing attention. It lights one part of the stage so brightly that everything outside the beam becomes difficult to see.

Consumer brands do it with color, music, status, urgency, and aspiration. Enterprise technology vendors use a more respectable-looking toolkit: analyst quadrants, keynote stages, customer logos, architecture diagrams, acronyms, roadmaps, and the word platform.

Corporate buyers like to believe they are immune. They have RFPs, procurement departments, architects, security reviews, committees, and spreadsheets with weighted criteria. But corporate users are no more rational than ordinary consumers. They simply conceal the same emotions—fear, prestige, familiarity, career risk, and fear of missing out—behind professional vocabulary.

A buying committee is still a room full of human nervous systems.

Under conference lighting, every roadmap looks inevitable. Every dashboard appears complete. Every integration seems seamless. Then the fluorescent, almost acid-colored distortion begins to fade. Sometimes the product underneath is genuinely strong. Sometimes the bright shell hides immature features, shallow integrations, operational compromises, or simply less substance than the campaign implied.

This does not mean that marketing is always a lie. It means that marketing is never evidence by itself.

That is the useful way to read Pure Storage’s twelve consecutive appearances as a Leader in Gartner storage Magic Quadrants: not as an award, not as proof of universal superiority, and certainly not as a reason to stop asking questions. It is a long-running market signal—one that deserves attention precisely because maintaining architectural and commercial relevance over twelve years is much harder than producing one impressive launch.

The record was earned under the Pure Storage name. The company adopted the Everpure name in February 2026; the rebrand does not retroactively change the history, but it does raise a useful question about whether the broader new identity will be matched by equally mature capabilities beyond storage.

What Twelve Years Actually Prove

In 2014, the decisive storage question was comparatively narrow: could an all-flash array deliver enterprise reliability, predictable latency, usable data reduction, and manageable economics?

By 2019, Gartner had replaced its separate Solid-State Arrays and General-Purpose Disk Arrays reports with a broader Magic Quadrant for Primary Storage. The market was no longer judging flash as an isolated hardware category. Buyers increasingly expected software-defined operations, automation, cloud connectivity, flexible consumption, and support for a wider range of workloads.

By 2025, the category had expanded again into Enterprise Storage Platforms, covering block, file, and object storage across structured and unstructured data. The center of gravity had moved from the array to the platform: common management, fleet-wide visibility, cyber resilience, service-level commitments, and a consistent operating model across environments.

Surviving those category changes matters. A vendor can optimize a launch for one analyst cycle. It is harder to remain relevant while the object being evaluated changes from a flash appliance into an enterprise data platform.

But twelve years do not prove that every Pure product is best for every workload, that every feature is equally mature, or that the company should win every procurement. A quadrant describes a vendor’s position in a market. It does not reproduce your architecture, your team’s skills, your migration risks, your contract, or the failure modes that will appear at 03:00 on a Sunday.

The Three Eras Behind the Run

1. The all-flash challenge: 2014–2018

Pure Storage’s early advantage was not simply that it sold flash. Several vendors could put SSDs into an array. The more consequential proposition was an operating model built around flash from the beginning: data reduction, simplified management, predictable performance, non-disruptive software and hardware evolution, and a commercial promise that storage should not require periodic forklift replacement.

The important innovation was the combination. Hardware architecture, software behavior, support, and lifecycle economics were presented as one system rather than as separate purchases.

That model became Evergreen.

Its strongest idea was psychological as much as technical: customers should not have to fear the next refresh cycle. If controllers could be upgraded without a disruptive data migration, the array stopped behaving like a depreciating island and began to resemble a continuously evolving service.

2. From array to operating model: 2019–2022

As the category broadened into primary storage, raw performance became less differentiating. Flash was no longer exotic. The more useful questions became operational:

• Can the platform be managed consistently across a fleet?

• Can upgrades happen without a migration project?

• Can capacity and performance be consumed more flexibly?

• Can the same operational model extend into cloud environments?

• Can a small team operate the estate without building a priesthood around it?

Pure’s answer combined Evergreen, cloud-based fleet management through Pure1, automation, subscription models, and an emphasis on simplicity. This was a credible strategic progression: the company did not abandon the architectural idea that built its position; it expanded that idea into a broader consumption and management model.

This is also where scrutiny should increase. The word platform can describe a genuinely coherent control plane—or it can be a marketing ribbon tied around a portfolio of unevenly integrated products. Buyers should test which one they are seeing.

3. Platform promises and service levels: 2023–2025

The latest era is defined less by owning a particular box and more by purchasing an outcome: availability, performance, energy efficiency, recoverability, upgradeability, and a predictable lifecycle.

That shift is real and useful. It also creates new opportunities for marketing hypnosis. A service-level promise can sound absolute on a keynote slide while the contract defines exclusions, measurement windows, customer obligations, and remedies that are far narrower. A guarantee may compensate the customer with service credits without compensating for the operational damage of an outage.

The mature question is therefore not, “Does the vendor offer an SLA?” It is:

What exactly happens—technically, operationally, and contractually—when the promised outcome is not delivered?

Where Pure Storage Genuinely Led

Pure deserves credit for turning non-disruptive lifecycle management from a technical feature into a coherent business model. Evergreen linked controller evolution, software continuity, support, and customer economics in a way that was easy to understand and difficult for incumbent refresh models to ignore.

The subsequent arrival of similar lifecycle programs elsewhere supports two conclusions at the same time.

First, the original idea was influential. NetApp announced its Storage Lifecycle Program in 2023, including non-disruptive controller upgrades for eligible customers. HPE introduced the Timeless Program for Alletra Storage MP in 2024, also centering the offer on non-disruptive controller modernization for qualifying subscriptions.

Second, the category is no longer unique. Once competitors adopt the principle, leadership depends on execution, eligibility rules, architectural limits, support quality, and contract economics—not on who can tell the cleanest origin story.

Calling every later program a “copy” may be emotionally satisfying, but it is analytically weak. Competitors respond to customer demand, technical possibility, and commercial pressure. They also implement similar ideas through different architectures and contractual terms. The buyer’s task is to examine those differences, not applaud the mythology of a single brand.

Where the Bright Shell Can Hide Weakness

No vendor is equally mature across every protocol, workload, integration, and operating environment. A strong block-storage heritage does not automatically guarantee identical depth in file, object, cloud-native, edge, analytics, or AI use cases. A polished common interface does not prove that the underlying services share the same operational behavior.

Likewise:

• A non-disruptive controller upgrade does not eliminate capacity planning, migration between architectures, or application-level testing.

• A resilience feature does not replace tested recovery procedures and clearly assigned responsibility.

• A ransomware narrative does not prove recovery time under your data volumes and dependencies.

• A sustainability claim needs a defined baseline, measurement method, and lifecycle boundary.

• A subscription can improve flexibility while also introducing term commitments, eligibility conditions, and long-term commercial dependence.

• A roadmap is not a delivered feature.

This is the moment when enterprise buyers must surface from the pool of acid-colored distortion. Remove the stage lighting. Ignore the quadrant dot for an hour. Ask the engineers to demonstrate an upgrade, a failure, a restore, a workload spike, and a broken integration. Read the footnotes in the guarantee. Speak to customers whose environments resemble yours—not only to the reference selected by the vendor.

Sometimes the sober view confirms the marketing story. Sometimes it reveals incompleteness or emptiness behind the bright exterior. Both outcomes are valuable, because the purpose of evaluation is not to validate a campaign. It is to discover reality before signing the contract.

What Gartner Can—and Cannot—Tell You

Gartner evaluates vendors against weighted criteria under two broad dimensions: Ability to Execute and Completeness of Vision. That can help buyers build a market map, identify credible suppliers, and understand how the analyst believes the category is evolving.

It should be a starting point, not a purchasing decision.

Gartner itself states that a Magic Quadrant is not intended to be the sole tool for selecting a vendor. Inclusion is not an endorsement, and the familiar four-quadrant graphic is not a league table. A Leader may still be a poor fit for a specific environment; a Challenger, Visionary, or Niche Player may be the stronger choice for a particular workload, geography, operating model, or budget.

The moment a vendor turns a quadrant into a medal, part of the analysis has already been replaced by theater.

Three Questions That Cut Through the Theater

Before treating any storage platform as strategic, ask the vendor to answer three questions with a live demonstration, contractual language, or independently verifiable customer evidence:

1. Maturity: Are block, file, and object capabilities equally mature for my workloads, or merely available under one brand and interface?

2. Failure: What happens when availability, performance, efficiency, or recovery commitments are missed—and what exclusions apply?

3. Economics: What is included for the full term, what requires a new purchase, and how do renewal, capacity growth, and exit costs change the TCO?

If the answer is another slide deck, the answer is incomplete.

The Sober Conclusion

Pure Storage’s twelve-year run is meaningful. It suggests that the company did more than market a fashionable flash array: its core ideas about simplified operations, non-disruptive evolution, and lifecycle economics continued to matter as Gartner repeatedly widened the category.

That deserves recognition—but not devotion.

The strongest tribute to a genuinely good technology is rigorous scrutiny. Good architecture survives uncomfortable questions. Mature products survive failure testing. Fair commercial models survive a reading of the small print.

Bright marketing can hypnotize a household consumer and an enterprise architecture committee with equal ease. The enterprise version simply has better graphics and a larger budget. Leadership begins when we notice the hypnosis, step outside the color field, and inspect what remains.

Sometimes there is substance.

Sometimes there is an unfinished product behind a perfect campaign.

The quadrant cannot make that distinction for you.

Stay awake. Stay skeptical. Stay naked.

© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

Sources

• Gartner: Magic Quadrant for Enterprise Storage Platforms, 2025

• Gartner: Magic Quadrant FAQ

• Gartner: Content Compliance Policy

• Pure Storage: named a Leader for the twelfth consecutive year, 2025

• Pure Storage: Gartner’s 2019 Primary Storage category change

• Everpure: Pure Storage becomes Everpure, 2026

• NetApp: Storage Lifecycle Program announcement, 2023

• HPE: Timeless Program announcement, 2024

I still remember those six hours of continuous, hypnotic dancing. Before that exact moment, up until the age of 26, I had never danced at all. My body had felt as if it were tightly bound by heavy, invisible chains. But in the dim light of that iconic venue (a brand that, to the best of my knowledge, now only exists in our collective memory), those chains finally shattered. To commemorate it, I bought a t-shirt with a phrase that became an internal manifesto: “World needs more rebels.”

Two months have passed since my last post. Two months filled with heavy workloads, extensive travels, and a subtle yet steady process: I have been slowly crawling out of my defensive shell and engaging with people. And a surprising revelation followed—the world around me turned out to be far more welcoming than I had trained myself to believe. The fierce desire to constantly pit myself against it simply faded away. What or who played the decisive role in this shift, I will leave outside the scope of this narrative.

But the fact remains: it seems I have finally conquered that elusive Victoria—the Latin word for Victory, to which my previous post was dedicated. Small, deeply personal victories are what restore our core confidence. And with that confidence back in the arsenal, we can begin conquering entirely new peaks.

Yesterday marked the launch of Apple’s annual conference. Tim Cook officially announced his departure from the role of CEO, closing a massive chapter in corporate history. Yet, it is the voice of the other Steve, along with his timeless quotes, that lives on vividly in my memory. Jobs was and remains a mental icon for me, which makes it all the more gratifying to hear others quoting his philosophy today.

There is a profound magic in this: you completely disconnect from the abstract global “neural network” for two full months, step back into the ether, and realize that other independent neurons were pondering the exact same concepts. You haven’t spoken or synced up, yet you arrived at the same conclusions. It behaves just like a complex technical hypothesis run through two separate AI language models—when they independently yield the identical output, your confidence in the viability of that idea skyrockets. The architecture of thought synchronized organically.

Why do both of these Apple CEOs inspire me so deeply? The answer is right in front of us. To look back and analyze everything that transpired over these past two months, I opened a device that “stood on its own feet” and found its philosophy under the first Steve, and utilized a cloud service that fully matured and scaled under the second. The winding, non-linear life path of the first instills a conviction that my own erratic experiences are not a waste of time or a string of fatal errors, but a necessary, rock-solid staging ground for future heights. The second taught me rigorous discipline and the courage to show the world exactly who you are—completely unedited and without filters.

Somewhere in the middle of this internal transformation, I managed to inhabit polar opposite realities: wearing the structured cap of a CEO making executive calls, and suddenly finding myself an absolute wanderer in London—disconnected, without signal, without maps, and without a single pound in my pocket. That contrast is incredibly grounding. Out of that chaos emerged a clear, actionable plan for the remainder of the year, anchored by four core pillars:

1. PHYSICAL CONDITION: Systematic training and discipline, but strictly optimized for well-being rather than at the expense of health. No more working to the point of exhaustion.

2. COMMUNICATION: A total focus on integration and language mastery—sharpening both my English and Czech.

3. EXPERTISE: Professional recertification across Everpure products and core technologies. Defending data architecture demands sharp, up-to-date weaponry.

4. THE INTERNAL GENTLEMAN: Finding the mental stamina and strength to simply listen. It is time for raw honesty: being abrasive requires absolutely zero effort from me; it is simply how I grew up. But cultivating the capacity to truly hear others and respect their space—that is a discipline that requires the genuine strength of a rebel.

   

I look toward the horizon with a sense of genuine, clear-eyed optimism. Defying standard marketing trends, I plan no rebranding efforts, no name changes, and no conceptual pivots. This blog will remain exactly what it is. I think it is simply evolving into a slightly more polite, yet fiercely authentic naked cyber sec—unmasked, real, and hopefully, incredibly engaging for you, dear reader.

© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

What kinds of integrations have I seen throughout my career?

1. Marketing announcements between two different companies’ products

2. Technical integration through SDKs and APIs

3. Internal integration of products inside one large corporation

Want details? I’ve got plenty.

1. Marketing Announcement
The emptiest type. A beautiful croissant with nothing inside. Lots of noise, joint webinars, press releases. Under the hood — usually just shared APIs, syslog, and a couple of tested configurations.
The real goal is to create a cartel to push out a common competitor. The desire for mutual customer penetration usually ends with 2–3 pilot projects and then salespeople spreading the news by word of mouth. Friendship is friendship, but money is separate. Everyone wants a piece of someone else’s pie.

I’ve seen Web Application Firewall + DDoS protection cloud, data masking systems + database monitoring, and many others. Looks great on stage. Works weakly in production.

2. Technical Integration via SDK
More interesting technically. One vendor embeds another’s solution, usually on a Revenue Share model. Fast and convenient, but almost zero flexibility.
Examples: Web Gateway + antivirus, DLP + image recognition system. Such modules are often sold on the principle “you can’t live without them.”

3. Internal “Friendship” Inside One Corporation
The most fascinating and simultaneously saddest story.
The company loudly trumpets “one unified ecosystem” in every presentation, while inside, independent product teams fight for resources and don’t want to let their internal competitors onto their territory. This is especially common when there’s an old and a new product, or after mergers and acquisitions.

The result: dedicated salespeople pushing only their own direction, customer brain explosions (“why is one employee praising one thing and another praising something completely different?”), and the constant need for someone inside the company who can resolve all these internal conflicts.

What else don’t you know about the ugly side of IT integrations, but were afraid to ask?

Want me to continue “shocking” you with real cases? Let me know. Feedback helps me keep the right tone.

Stay naked.

© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

I remembered a product presentation by my future boss that ended with the classic line: “Come to the dark side — we have cookies.” At that time the world looked binary to me: clear light and darkness, no fifty shades of grey. I was hooked on security systems that could detect both external hackers and internal threats — insiders, corrupt employees, or those who looked loyal but kept a “fig in the pocket.”

That passion led me to two favourite technologies: IPS/NTA for network visibility and DLP for data leakage prevention.

The core ideas were solid even back then. The problem was execution. Everything required massive manual tuning because we didn’t have AI assistants that could simplify these tasks by orders of magnitude. Classification, policy creation, maintenance — it all demanded heavy upfront consulting. Many projects started with enthusiasm and died in the details.

Today the fundamental principles haven’t changed, but the tools have. As smart people say: knowledge of a few principles frees you from knowing many facts.

Modern DSPM platforms (with heavy use of LLMs and contextual graphs) finally make the old dream viable: automatic discovery and classification across the entire estate — cloud, on-prem, mainframe, SaaS — with minimal manual effort, real-time risk scoring, and automated orchestration into existing DLP, SIEM, and IAM tools.

The fairy tale we told ourselves in 2012 has become reality. What used to be blocked by the sheer volume of manual work is now achievable in weeks, not months.

My Top Practical Tips for Hunting Violators

External threats (network / attacker perspective):

1. Clearly separate network-based and agent-based approaches. Mirror traffic at minimal chokepoints — ideally the core switch. Agent-based monitoring eats resources fast (cloud APIs are a separate conversation).

2. Protocol support should be broad, but real value comes from metadata analysis speed and depth. Real-time blocking of sophisticated attacks is still mostly a fairy tale. Focus on understanding collected metadata to trace and remediate.

3. Track events religiously. Anomaly detection alone doesn’t guarantee 100 % coverage, but it will definitely help you spot the elephant in the china shop.

4. Maintain a living network map and asset inventory. This isn’t just for nice management reports — it’s your daily tool for vulnerability hunting and context.

Internal threats (data leakage / insider perspective):

5. To truly understand data flows inside the organisation, start building and maintaining a connectivity graph.

6. Keyword analysis remains the solid foundation for leak hunting; everything else (ML, context, etc.) is a powerful addition.

7. Remember: DLP is not a product — it’s a triangle of people, processes, and technology. I like to illustrate it as “dark matter”: it absorbs everything and should release nothing. The “quasars” you see are exactly the leaks.

8. Monitoring without immediate prohibition is often the most effective long-term measure. Prevention and sanctions work best as exceptions, not the default.

Universal rule that applies to both hunting:

9. Ideas (and insights) are like fish. If you want to catch little fish, you can stay in the shallow water. But if you want to catch the big fish, you’ve got to go deeper. Down deep, the fish are more powerful and more pure. They’re huge and abstract. And they’re very beautiful. — David Lynch

In threat hunting this means: don’t stop at surface-level alerts and obvious IOCs. Dive deeper into context, relationships, business impact, and subtle behavioural patterns. The real “big fish” — sophisticated insiders, advanced persistent threats, or critical dark data exposures — hide in the depths. Modern tools with AI and multidimensional graphs finally let us go there without drowning in manual effort.

The approaches we loved 10–15 years ago are still relevant. The difference now is that AI and modern data intelligence layers remove the biggest historical blocker — endless manual labour.

If this resonated or gave you a useful idea, you can BUY ME A COFFEE.

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© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

Sitting on a Starbucks terrace with coffee and a cigarette, I noticed a tall, muscular guy in a vest from the local Black Dogs LE MC (Czech Republic). That instantly took me back to my one-time participation in a Night Wolves event, my last visit to their clubhouse in Moscow’s Mnevniki district, and the best “business manual” series my old friend — a Lead AE at the Russian office of a major American IT vendor — ever recommended: Sons of Anarchy.

It was perfect decompression after a heavy business day. Nothing clears the head like bikes, brotherhood, and controlled chaos.

“Companies don’t do business with companies.
People from one company do business with people from another.”

Despite the surface-level differences — motorcycles, gangs, guns, and drugs in the series versus suits, Excel files, and procurement procedures in real life — the core ideas are strikingly similar:

• Ruthless competition

• Discipline and hierarchy

• Intelligence gathering (OSINT and insiders)

• Building deep trusted relationships

• Confidential negotiations

• Strategy and power struggles for resources and positions

Two Very Different Clubs, Same Code

Black Dogs LE MC (Czech Republic)
Founded in 2002, this is the first Law Enforcement Motorcycle Club in the Czech Republic. Membership is restricted to police officers, soldiers, firefighters, paramedics, and other state servants (99% LE). The club emphasizes discipline, service to the law, and riding together. It is independent and a co-founder of the Czech National LE MC Coalition.

Night Wolves MC (Russia)
Founded in 1989 in Moscow by Alexander “The Surgeon” Zaldastanov. One of the largest and most influential motorcycle clubs in Russia (estimated 5,000–7,000 members across 45+ chapters). Closely tied to the Kremlin, they organize Victory Day runs and large-scale bike shows. The club and its leader are under Western sanctions.

Two clubs on opposite sides of the geopolitical divide, yet living by surprisingly similar principles: loyalty, hierarchy, brotherhood, and a strong code.

The Dark Mirror: Hunter S. Thompson and the Hells Angels

To understand the darker side of this culture, it’s worth remembering the greatest gonzo journalist, Hunter S. Thompson. In 1965 he began with an article for The Nation, then spent over a year fully embedded with the Hells Angels — riding with them, drinking in their bars, and living their life as a participant-observer.

The result was his 1967 classic “Hell’s Angels: A Strange and Terrible Saga” — one of the most honest portraits of 1%er outlaw motorcycle culture ever written. Thompson showed the raw mix of brotherhood, violence, freedom, alcohol, and rebellion against society without cheap sensationalism.

At the end of his time with the club, he was severely beaten by members. The relationship ended badly, yet the book remains a landmark.

10 Rules from Traditional 1%er Motorcycle Clubs

Here are ten core rules commonly found in the charters (bylaws) of traditional outlaw motorcycle clubs like the Hells Angels and similar groups:

1. Loyalty to the club above all else.

2. Always back your brothers.

3. No talking to outsiders. No snitching.

4. Respect the hierarchy and chain of command.

5. Prospecting period is mandatory — full membership and trust are earned over a long trial.

6. Club property (Colors, Vest, Brand, reputation) is sacred and untouchable.

7. Never lie to or steal from your own.

8. Membership is a lifelong commitment. Leaving the “company” does not cancel past obligations or responsibilities.

9. Strict selection — new members (or key partners) accepted only by unanimous vote. One “no” is enough.

10. Discipline for violations is swift and severe.

Now replace the word “motorcycle club” with “corporation” — and you get a surprisingly accurate unofficial bible of corporate business worldwide.

That’s the naked truth.

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© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

Based on real events. All names changed, companies/brands anonymized (except actor Vladimir Vdovichenkov from “Bumer 2” and black BMW X5).

So, Bumer. Film Two.

Released in 2006 when the first part was already a massive hit. The main theme became ringtones for thousands. We watched Part 1… during a Social Studies class in IT college 😂

Context: watch at least the first 17 minutes with English subs. In the director’s cut (5–7 min) there’s a deleted scene: “Cat” (Vladimir Vdovichenkov) to the prison boss – words I’ll never forget:
“Why did you kill a good guy… he had only several months left to parole…”

The film’s key symbol: material dream of the boys — a black BMW X5.
Premiere: March 7, 2006 (Russia & Kazakhstan). My first university year. One year later I met Vladimir Vdovichenkov… at the traffic police getting plates for new cars. Mine – bronze Hyundai Accent from dad. His – guess two times 😏

Real events hit in 2013. Age 25. Left bank cybersec tech role, became pre-sales manager (cybersecurity focus) at a big IT integrator. Preparing million-dollar tender proposal: TCO, architecture, vendor & client calls.

14:00 – buried in Excel. Hear boss: key outsourced customer hit by massive DDoS. Business down, terminals in richest regions offline, top management in panic.

Google search → volunteer to help. No standard playbook → sent to “hold the dying man’s hand.”

15:00. Racing with tech manager. Explain problem & solutions on the way. Found Frostbyte (our partner) & Shieldex in 5 min. Call A.Zvizdilov – get a storm of swearing: “We won’t protect this client for any money – he accused us of staging the attack.” Calm him down, agree. Fix with AE: if client says yes – protect at list price + standard discount, no haggling.

16:00. 30th floor business center, view almost like Central Park NYC. Present options: SECaaS optimal, on-prem “free” but limited, hybrid best of both. Draw scheme on whiteboard.

17:00. Chemistry with CIO. Panic fades, reason returns. Action: whitelists, collect terminal IPs piece by piece, BGP tunneling, extra firewall, approval, prod. 3 intense hours. Order pizza.

21:00. Pepperoni, black soda, mood up. Reroute traffic, wait for DNS prop, test, tune. Not instant – another ~5 hours, but calm & professional. Business restored.

04:00 next day. Watch sunrise together. Handshakes, go home.

09:00. Call through sleep: tender manager — “Where’s TCO?!” Deadline 18:00. Aggression: “I need it NOW!” Shave, shower, wipe tears of frustration.

10:00. Proposal sent, I’m on the carpet — restrained but pissed.

Outcome: bonus (bought first motorcycle), ride in boss’s black BMW X5, personal coaching from him. Built deep trust with CIO – became their trusted architect. Shared expertise, built IR plans.

Later attack on customer DB via web server — they were ready. Detected leak via read spike in Storage monitoring, blocked channel in time. It turned out to be just a couple of lines of malicious code injected into the website. Who could have done that? 🤔

Peak twist: I’m on client side, defending them to authorities & R.Borodach (Frostbyte investigations head) — “All measures taken, no admin liability for data breach.”

3 years on: R.Borodach arrested. Frostbyte: “No known violations,” fired same day. Still serving sentence.

Moral (short & hard):

1. Control ALL your assets. Even a simple landing page can be entry point. Never mix public & business flows. Think 10× about risks before publishing.

2. Store data only on Enterprise-Ready Storage. Invest in monitoring & response processes – saves nerves, time, and proves innocence when shit hits the fan.

3. Read my blog, take ownership, but stay away from gray zones. Respect the law. Ignorance is no excuse.

Resources: Mobylnik (soundtrack)

Movie or real life — which is crazier? Drop thoughts below.

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© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

Today I want to talk about new data storage technologies and virtualization in particular. Right away: no praising companies I worked for, work at, or will work for. It’s cool when someone climbs so high that challengers lose the desire to compete directly. But every security guy knows: behind every beautiful marketing slide hides the same thing — caffeine addiction, ruined personal life, work-life balance broken over the knee, peeking into the windows of the neighboring business center, pouring water from glass to glass.

And everyone who has tried BDSM (I mean Business Development Sales & Marketing) knows: to keep your glass full, you have to dry someone else’s a little.

So let’s talk about the outdated and long-obsolete approach of storing data on physical servers. Because it’s been bugging me.

Remember that clumsy, humming supertower in the server room? Puffing, groaning, but God forbid you bump it with your elbow or spill coffee nearby — the head admin gets a near-heart attack because the entire accounting department lives on it.

Scale doesn’t matter: in a big company you might have 200 of these.

Remember? You can’t do anything with it — old-school rule: “It works — don’t touch it.” Yes, with bugs. Yes, by mood. Once every 1–2 years you have to stay up all night restoring because a disk failed or a fan died. All with downtime and nerves.

And cybersec is not just about “nobody finds your security hole.” It’s first of all about business resilience, continuity, risk control, and making sure catastrophic events don’t put the company out of business.

Then virtualization came and brought virtual farms. Most people screamed: “Unsafe! How can you share power between 2–3 (max 7–10) beautiful modern servers with a shared resource pool?”

Turns out — you can. And it’s much safer than 50 dying repurposed PCs.

But life moves on, and we move with it. The needle of virtualization turned out stronger than morals, and your dealer decided to raise prices sharply. Where to put your Enterprise files now?

My answer: 1–2 unified storage systems with well-organized role-based access for all departments, replication of critical data, fast and convenient migration between storages, encryption, deduplication, and compression.

But that’s just my opinion. You decide and act.

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© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

My story started long before I ever touched a computer. In 1936, thousands of Poles and Germans from the border regions of Ukraine (mostly Volyn and Zhytomyr) were deported to Kazakhstan by order of the Soviet government. They were loaded into freight trains with only an hour to pack. Their past was erased in one trip.

One of those trains carried my grandfather – a little Polish boy – and his family. They ended up in Labor Settlement No. 12 of Karlag (now the village of Pervomayka in Akmola Region, Kazakhstan).

Karlag was one of the biggest agricultural camps in the Gulag system. At its peak, it covered almost 1.8 million hectares with hundreds of small points, farms, and sites.

People survived in the endless steppe, built farms, and raised children. My grandfather made it through. Despite his “unreliable” background, he finished a railway college, married a doctor (my grandmother), and in 1955 my father Alexander was born in the same Akmola Region.

I was born on July 27, 1988, in Stepnogorsk – a completely closed town that wasn’t even on public maps until the late 1980s. The main factories were the Central Mining and Chemical Plant (uranium mining and processing) and the Progress plant (where they developed components for biological weapons in Soviet times). After the USSR collapsed, the town suffered hard in the 1990s: factories closed, people left. My family moved too – first to Moscow Region, then to Moscow in 2000.

I loved computers from early childhood. We got an 286 PC – the second one in the whole town – when I was four years old. That decided my path. I went to College of Information Technologies, and later chose university in cybersecurity – back then it seemed the most romantic field in IT.

University was brutal: 5-6 exams twice a year, constant stress, math, physics, philosophy, database and network security, low-level and high-level programming, tons of paper-based security rules, and basics of cyber warfare. My fuel was five Ritter Sport chocolates a day. Languages were hard, but math and history always came easy.

After graduation I started as a security administrator in a big bank. A couple of years later I realized I wanted to work more with people than with machines. I moved to system integration. Over the years in Russia I worked at well-known vendor companies: Kaspersky, Positive Technologies, Garda Technologies, Solar Security. I traveled all over the country – from Kaliningrad to Yuzhno-Sakhalinsk, and many times to Kazakhstan and Kyrgyzstan on business trips.

Everything was going fine until 2022, when the war started. I left — first to Kyrgyzstan, then to Kazakhstan. I got a residence permit and applied for Polish citizenship through repatriation. In 2025 I finally returned to the homeland of my ancestors.

Why am I telling you this long personal story?

Because this blog is called Naked CyberSec. After 15+ years in the field, I’m deeply convinced: real cybersecurity is impossible without trust.

Everything we build – Zero Trust architectures, immutable snapshots, AI enterprise infrastructure – rests on trust. Even in Zero Trust the second word is still trust. Trust, but verify. I know who I am. Now you know too.

This blog is my way of being completely open. No masks. With a history that goes through the Gulag, deportations, closed uranium cities, to today — living in Prague as a digital nomad with my dog Diego.

Stay naked. To be continued...

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© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.

So, the blog is live. Naked CyberSec. No fancy logo (yet), no marketing team breathing down my neck, just me, a laptop, and my dog Diego snoring like a tiny chainsaw next to me on the couch in Prague.

It’s March 19, 2026. 00:30 in Prague. The streets are quiet now—only the occasional tram rumble and night owls heading home. Diego is snoring peacefully beside me, and I’m finally hitting “Publish” on the first post of Naked CyberSec. No more excuses.

Why now? Why this blog at all?

Because the infosec world (and IT in general) is drowning in polished corporate fluff, vendor spin, and “best practices” that sound great in webinars but fall apart in real production environments. We need more naked truth — raw, unfiltered, from someone who’s actually been there.

Short answer: because the IT world needs more naked truth and less polished corporate fluff.

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© 2026 Michaił Domalewski / Naked CyberSec. All rights reserved.