Microsoft has had a narrow escape from total embarrassment: A security company uncovered a critical vulnerability that could have compromised all Azure Cosmos DB databases — both those of customers and Microsoft’s own.
Google subsidiary Wiz found a flaw in the database’s Gremlin API, usually used for storing and managing property graph data.
If bad actors had discovered it first, they could have exploited it to acquire what Wiz called the Cosmos Master Key, which would have enabled them to use the primary key of any Cosmos database, resulting in read and write access to any account. They would also have had access to a list of every database on the service, with identifiers such as subscription and tenant IDs.
Azure Cosmos DB is a NoSQL database that underpins Microsoft’s cloud services. It can be accessed through SDKs for framework such as Python, Node.js, Java, and .NET.
Wiz described how it discovered the vulnerability in a blog post. It disclosed details of the flaw to Microsoft in November 2025.
Microsoft deployed a hot fix within two days, but it took another eight months to re-engineer the infrastructure, removing the Cosmos Master Key and introducing new guardrails to Cosmos DB to prevent similar attacks.
It is not the first time Cosmos DB customers’ primary keys have been under threat: In 2021, Wiz found a flaw in data exploration tool Jupyter Notebook that could be exploited to access the database keys and other secrets.
This article first appeared on CSO.
Every cloud architect, every financial services executive, and frankly every enterprise CTO should be paying attention to what is going on in the United Kingdom right now. Britain has formally designated Microsoft, Google, Amazon Web Services, and Oracle as “critical third parties” to the financial sector, meaning they are now subject to direct oversight by UK financial regulators rather than being treated as distant infrastructure suppliers outside the regulatory perimeter.
The designations took effect on July 13, 2026, and the oversight will be carried out by the Bank of England, the Prudential Regulation Authority, and the Financial Conduct Authority. The point is straightforward. If these platforms underpin critical banking and financial operations, then their resilience is no longer just a procurement issue. It is now a matter of financial stability.
This is a bigger deal than many people understand. For years, enterprises have treated cloud platforms as outsourced technology providers. Regulators are now making a different argument. They are saying that when enough banks, insurers, payment firms, and market infrastructures depend on the same small group of cloud providers, those providers become systemic infrastructure whether they like the label or not. Reuters reported that the UK’s framework will bring requirements such as resilience testing, self-assessments, and incident reporting directly to these providers. In other words, this is not just more policy language. It is operational oversight aimed at the platforms themselves.
The first thing to understand is that this move is not anti-cloud. It is the opposite. It is an acknowledgment that cloud has become too important to remain lightly touched when it comes to systemic risk. Regulators are not trying to unwind cloud adoption in financial services. They are accepting the reality that core financial operations now depend on a concentrated set of external platforms. When that happens, the conversation changes from vendor management to infrastructure resilience.
The market has spent years talking about cloud concentration risk as if it were some vague future concern. It is no longer abstract. The UK is acting because concentration at this level means that a disruption at one major provider can ripple across multiple institutions simultaneously. That is the core issue. It is not whether Microsoft, Google, Amazon, or Oracle are competent operators. In many cases, they are exceptionally good at what they do. The problem is that too much critical activity depends on too few shared platforms.
This is a concept many business and technology leaders still struggle to internalize. A single bank can do a fine job managing its own vendor exposure and still be part of a broader systemic vulnerability if every other bank is relying on the same provider, the same region, the same identity layer, or the same operational dependencies. Regulators are stepping in because the risk is collective, not just institutional. That is why this matters well beyond the UK. It provides a framework for thinking about cloud not as outsourced capacity, but as part of the plumbing of the financial system itself.
Traditionally, regulators focused on the bank, insurer, or market institution and expected that entity to manage the risks created by outside suppliers. That approach still exists, and financial institutions remain accountable for their own resilience. However, the UK now says that, in some cases, this indirect model is not enough. If a third party becomes critical enough, regulators want direct line of sight into that provider’s resilience posture as well. This is probably the most important architectural and policy signal in the entire move.
That changes the relationship between the financial sector and the cloud providers in a meaningful way. Direct oversight means cloud providers are increasingly being treated less like optional technology partners and more like essential utilities that support national economic confidence. Once that happens, architecture decisions start to carry a different weight. Decisions about control planes, failover models, identity dependencies, regional design, observability, and incident response are no longer just internal engineering choices. They become part of a broader resilience conversation that may now include regulators.
A lot of enterprises have been behind the curve. They built architectures under the assumption that the cloud was simply a faster and cheaper hosting model. It is not. At scale, cloud is shared critical infrastructure. The UK has now made that point explicit.
It’s tempting to read this and assume the problem has now been solved. It has not. Direct oversight of hyperscalers does not remove the responsibility of banks or other enterprises to architect well. In fact, it should push them to architect better. A regulated provider can still be a concentration point. A resilient platform can still be used in a fragile way. And a well-run cloud service can still become part of a badly designed dependency chain.
If you are in a regulated or highly sensitive industry, you should take this as a warning that resilience can no longer be treated as a side topic. You need to know exactly which services are business-critical, which control planes are shared, which identity systems create cross-platform dependencies, and how your recovery assumptions actually work in the real world. Most organizations are much weaker here than they believe.
The UK’s move should also force a hard conversation about operational transparency. If regulators are requiring incident reporting and resilience evidence from major providers, enterprises should be demanding clearer dependency mapping and better architectural visibility inside their own environments. You cannot build true resilience on top of assumptions and vendor slide decks.
The deeper message is that cloud is crossing a line from enterprise technology choice into national and sector-level infrastructure policy. Once a few governments and central regulators start treating cloud this way, the rest of the world tends to follow in some form. We have already seen comparable concern in Europe, and now the UK has made its own move with direct designations and direct oversight.
Architects, CIOs, and boards need to mature their thinking quickly. The conversation is no longer limited to feature depth, discounts, migration speed, or which provider has the best AI story this quarter. The conversation is now about resilience, concentration, transparency, control, and systemic dependency. Those are bigger issues, and they are not going away.
The UK is telling the market something very clearly. Microsoft, Google, Amazon, and Oracle are not just selling cloud services into the financial sector. They are becoming part of the infrastructure on which that sector depends. Once you understand that, everything changes, including how we architect for the future.
The European Commission’s latest push to reduce dependence on foreign technology providers is not surprising. If Europe believes that critical digital services could be disrupted by foreign governments, foreign legal systems, or foreign-owned providers, it will, of course, respond. That concern is now being expressed in the language of “kill switch” risk, meaning the fear that the cloud, AI, or semiconductor services that Europe depends on could be interrupted or constrained by forces beyond its control.
At a high level, that concern is valid. Europe is right to worry about strategic dependence. If critical public services, regulated workloads, or national-interest systems rely on infrastructure controlled elsewhere, sovereignty becomes more than a policy slogan. It becomes an architectural issue. However, I am skeptical of the leap from identifying the problem to assuming that a policy response will produce a cleaner, safer, or even more sovereign market. There is a good chance it may do the opposite.
The motivation behind this effort is clear. Europe wants to reduce its dependence on cloud computing, artificial intelligence, and semiconductors from providers it does not fully control. It wants to ensure that core digital services cannot be switched off, legally constrained, or strategically influenced from outside the region. That is the public policy objective, and from a government standpoint, it makes sense.
The problem is that cloud markets don’t often respond to political intent as policymakers hope. The public cloud market is concentrated among a few big providers because scale matters. The hyperscalers have built global infrastructure, extensive services, ecosystems, and operating models that smaller regional players can’t match. Enterprises chose them for operational advantages, not geopolitical reasons.
Europe now finds itself in a difficult position. It wants sovereignty, but it also wants the benefits of scale, reliability, feature depth, and cost efficiency that usually come from very large cloud providers. Those goals do not always align.
One likely outcome of this push is that European enterprises will become increasingly confused about which public clouds they should select or avoid. That is not a minor point. Most enterprises already struggle with cloud strategy when the drivers are technical, financial, and operational. Add political sovereignty requirements, and the market becomes even harder to navigate.
Enterprises will now need to ask a more complex set of questions. Is a US-based hyperscaler with a localized European operating model acceptable? Is a European-branded sovereign cloud built on American technology better? Is a regional provider safer simply because it is smaller and local, even if it offers fewer services, weaker security tools, less resilience, and a less certain long-term future? At what point does “sovereign enough” become a legal or political judgment rather than a technical one?
This is where the market gets muddy. Policy discussions often imply a binary distinction between foreign and sovereign. In reality, cloud architectures are full of hybrids, partnerships, licensing arrangements, embedded dependencies, and supply chain layers, making neat categorization difficult. Enterprises do not buy cloud services based on a political slogan. They must navigate stacks of contracts, services, support structures, compliance obligations, and technical capabilities. The more politics enters that decision process, the less clarity there may be for buyers trying to make rational platform choices.
There is another reality that I think policymakers underestimate. Increased investment in sovereign cloud providers does not automatically create a durable sovereign cloud sector. In fact, history suggests the opposite.
Governments and enterprises may push investment toward smaller sovereign providers, but those providers still face the same brutal economics of the cloud market. They need capital, scale, customers, engineering depth, and ecosystem gravity. Many smaller providers will struggle to compete over time. Some will fail. Others will narrow their focus. Many will eventually be acquired, directly or indirectly, by larger players, including the very US-based cloud providers Europe is trying to reduce dependence on.
That’s the irony. Political pressure may spark a burst of sovereign cloud activity, but market gravity tends to reward scale. Sovereign cloud investment may create a temporary diversification, but in the long run, it could still end in concentration.
Sovereignty is not a bad goal, but the cloud business is structurally difficult. Running a competitive cloud platform is expensive. Matching hyperscaler capabilities is even harder. Enterprises eventually notice the gaps in services, AI tools, ecosystem support, geographic resilience, and operating maturity. When they do, they drift back toward the biggest and most capable providers.
My concern is that Europe may be entering a period when cloud architecture is driven less by technical fit and more by political signaling. That rarely leads to simplicity. It usually results in fragmented strategies, duplicated platforms, inconsistent governance, and long procurement cycles.
Some organizations will choose a sovereign-first model for political reasons. Others will remain with hyperscalers but add contractual and architectural safeguards. Others will adopt a multicloud approach purely to avoid appearing overly dependent on one provider. Still others will split workloads by regulatory sensitivity, which sounds sensible until integration costs and operational complexity add up.
This is how confusion mounts. The question shifts from “Which platform best supports my workload?” to “Which platform is politically safest this quarter?” That is not a stable architectural framework.
I understand exactly why Europe is raising this issue. No responsible government wants critical digital services held hostage by external dependencies. That concern is rational, but rational concern does not guarantee rational market outcomes. My skepticism is not about whether digital sovereignty matters. It does. But can policy alone produce genuine long-term autonomy or will it create a more confusing procurement environment? At the same time, the market will quietly continue to consolidate around the largest global platforms.
The uncomfortable truth is that sovereignty is easier to announce than to implement. Cloud sovereignty is especially difficult because cloud markets reward scale, capital, ecosystem strength, and breadth of services. Those forces do not disappear simply because a regulator seeks more regional control. I think this issue will become more important over the next few years as cloud, AI, and geopolitical power become even more tightly linked. Europe is right to ask hard questions about dependence. But the answers are likely to be messy, and market outcomes may not look as sovereign as policymakers hope.
I don’t expect to see a clean break from foreign cloud providers. I do expect more hybrid arrangements, more sovereign branding, more enterprise uncertainty, more investment in regional providers, and eventually more consolidation than many people currently anticipate. That is not a failure of the idea. It is just the reality of how cloud markets tend to work.
The real challenge for Europe is not identifying the risk. It is building a response that does not create more confusion than the problem it is trying to solve.
We’re seeing an interesting infrastructure tug of war today where GPU clouds are being pulled in two directions. For the economics of AI to work, the enterprise market needs to carve expensive hardware into smaller, shareable units and hand it to customers on demand, similar to how CPUs are doled in public cloud infrastructure. But the more the providers push GPUs to behave like elastic cloud infrastructure, the more they run into the reality that this GPU hardware was never built for safe multitenant use, fast fault recovery, or clean isolation between workloads. That tension is becoming one of the defining operational problems of the AI infrastructure market.
When a gamer launches Steam or the Epic Games Store on their laptop, they don’t have to worry about which GPU is being scheduled, how memory is going to be divided, or really any of the security boundaries or hardware assignment issues on their PC. For consumer PCs, these issues are not just hidden from view, they are irrelevant.
But for today’s IT teams managing GPU-driven AI workloads across distributed systems, those types of allocations and partitions need to be managed manually and carefully. This includes deciding which GPU to assign to each workload, how to divide memory, how to isolate tasks, and how to maximize utilization of this very expensive hardware. That’s why you heard so much about “Day 2” AI infrastructure themes around Nvidia’s GTC event this year.
GPUs were originally developed to speed up graphic rendering and to perform local compute via shaders in service of graphical rendering. Their design assumes a trusted computing environment in which a single application controls the device. When a user runs an application on a GPU, it accelerates that particular workload.
But while GPUs are optimized for throughput, and therefore have thousands of simple cores designed to execute the same instruction over large datasets, this design paradigm creates several major technical limitations regarding context switching and memory isolation. GPUs were designed to produce pixels, not to run sensitive AI applications from multiple tenants using the same hardware.
As a result, GPU infrastructure today behaves less like elastic cloud infrastructure and more like carefully managed hardware appliances.
Today’s AI infrastructure requires GPUs to behave like shared, elastic cloud resources. As inference workloads begin to outstrip large-scale training runs, the ability to slice and share expensive hardware among multiple tenants in real time while maintaining acceptable fault tolerance is no longer optional.
Hardware vendors have introduced new approaches to dividing GPUs into multiple isolated compute slices. Other frameworks approach partitioning through schedulers and container runtimes. But resource partitioning is just one slice of the overall GPU operations pie.
There is currently no widely adopted, cross-vendor operating model for achieving this safely at scale. Most providers are faced with either dedicating a single customer to a physical machine (thus wasting available capacity), or accepting the multi-tenancy security risks currently without a known solution. The current engineering challenge has moved from beneath the model layer and into the infrastructure layer. Success now relies on the ability to quickly launch new workloads and to rapidly contain hardware faults so that a single GPU failure does not bring down all workloads running on the server.
The vast majority of current GPU programming models rely on the idea that the driver has complete control over memory protection and that no user will act maliciously. Unfortunately, that assumption completely falls apart in a cloud environment where one VM or container can leave behind data remnants in memory that another VM or container may access. Especially considering that how GPUs execute code is often completely opaque.
A single faulty workload or a single faulty driver failure in a shared GPU environment can also take down every workload (job) that was being run on the same server, increasing the amount of damage caused by an operational failure.
Currently, there are immature options for runtime inspection or behavioral auditing, limiting both visibility and control for security teams. GPU drivers provide a large attack surface and generally limited telemetry from the hardware. In these shared environments, embeddings, weights, prompts, and tokens are now all sensitive data points, creating significant blind spots for those attempting to protect intellectual property.
The real constraint in many GPU clouds is not model performance but operational efficiency. Right now GPU operations looks like 30 minute tenant spin-up times, 70% idle rates, and engineers continuously debugging infrastructure stacks. Today’s GPU clouds are stalled not due to inferior models, but because the infrastructure layer underpinning these clouds was never designed to support such a high degree of scale.
A 30 minute cold start is a fundamental limitation on the modern AI business model. Those GPU clouds that can spin up workloads in seconds will ultimately win against those that do not. Multitenancy is the only viable means of producing sufficient unit economics to make this very expensive hardware viable for the long term.
Platform teams are beginning to recognize that GPUs require a specialized operating layer between the hardware and workloads. Operators need a unified operating model that supports multiple hardware vendors and GPU models. Cloud providers need a method to safely slice and share servers among tenants, and to prevent cascading failures, while rapidly launching new workloads.
Enterprises are increasingly seeking to run sensitive AI applications with stronger isolation guarantees, and thus are turning to newer categories of software designed specifically to manage the “dirty work” of hardware orchestration.
Prior to Kubernetes standardizing container orchestration, the industry was constantly debating the efficiency of container scheduling and bin packing across clusters. Those operational concerns were eventually automated and incorporated into infrastructure layers that made the complexities invisible to the end user.
A similar evolution is occurring around GPUs today. While platform teams continue to argue over placement strategies and memory tuning, these decisions will likely be automated within five to 10 years. As AI infrastructure evolves, the most valuable layer may not be the GPUs themselves but the operating layers that make them secure, elastic, and efficiently sharable. So the winners in the AI race won’t necessarily be just those with the most silicon, but those who have the best operating models for making that silicon secure and elastic.
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New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to doug_dineley@foundryco.com.
Cloud computing has reached a crossroads. The high cost and data sensitivity of AI workloads are raising the appeal of private clouds, even as neoclouds and sovereign clouds shake up the cloud provider landscape. New cyberthreats, shifting compute requirements, and management complexity are adding to cloud complications.
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