Somewhere right now, a field team is capturing sensitive data on a consumer-grade drive with no encryption, redundancy, or way for anyone back at headquarters to even know it exists. Across industries that have invested heavily in edge compute, this is closer to the standard operating procedure than exception. Billions of dollars are moving toward distributed compute, AI inference at the edge, 5G-enabled deployments, and portable micro data centers that can travel with a field team anywhere. According to Mordor Intelligence, the edge computing market reached $257.76 billion in 2026, with projections to nearly double to $479.97 billion by 2031. And while there’s a clear structural shift in how businesses think about where work happens, there’s also a consistent gap in how that shift gets planned for.
What tends to get left out of the conversation is where the data lives. It needs to be homed in the storage layer, with specific attention given to what happens before it ever touches a network, or reaches the cloud, or lands somewhere an IT team can see it.
Field teams across industries are capturing and processing sensitive data on drives that were never designed for the environments they’re operating in. These consumer-grade SSDs lack the hardware encryption, redundancy, and management interfaces that let operators know what’s on them or whether the data is protected ultimately putting data at risk. When things go wrong with the consumer-grade drives, the consequences are often expensive and irreversible.
The edge AI buildout, however, is only accelerating that risk. The more intelligence gets pushed to the edge, the more valuable the data at the edge becomes, and of course, the more it matters that the storage underneath that intelligence was designed for the job.
Why storage keeps getting treated as an afterthought
Storage has always occupied an awkward position in infrastructure conversations. It doesn’t carry quite the same architectural excitement as compute or connectivity, and it’s easy to treat capacity and price as the primary decision criteria.
This may work in some cases, but for field operations teams, this is critical blind spot.
When a production team’s capturing multi-camera footage in a remote location, or when a government team is running a mission without any cloud connectivity, the storage device needs to be top-tier, and treated as an active part of the workflow.
The Thales 2026 Data Threat Report, based on a global survey of more than 3,100 IT and security professionals, found that just 47% of sensitive cloud data is protected by encryption, meaning the majority is exposed even in managed, monitored environments with dedicated security infrastructure. And the situation at the edge, where data is generated and held before it reaches any managed environment, is even less well-controlled, and measured.
What hardware-level protection actually means in the field
The common assumption is that encryption is something you solve at the software layer. Too often, teams will try to enable a setting, install a tool, or apply a policy, but fail to ensure the integrity of the system running the software is where it needs to be.
Devices get passed between operators, are lost or stolen, or even physically compromised, putting the data at risk. In those situations, software-based encryption can be bypassed in ways that hardware-enforced encryption can’t, because the security in a hardware-based approach lives inside the device itself rather than an OS layer that can be tampered with. Not to mention, for teams handling sensitive footage, classified data, proprietary AI model weights, or anything that would cause real damage this can be deeply problematic in wrong hands.
Redundant Array of Independent Disks (RAID) protection at the edge is also something the industry has consistently treated as an IT-administered feature, while skipping over field deployment. In this case, RAID needs to be built into the device itself so these teams can handle protection autonomously without requiring configuration expertise or infrastructure support. For example, a film crew on a remote set and a government team executing a disconnected mission have different workflows, but they both need redundancy that works, without requiring someone else to manage it.
Throughput is another misrepresented variable. Rated performance in a temperature-controlled lab environment doesn’t translate cleanly to sustained performance under real field conditions, where heat, power fluctuations, and concurrent workflows all create pressure on throughput. For teams ingesting high-resolution footage from multiple cameras or processing sensor data in real time, the difference between rated throughput and actual sustained throughput is the difference between a workflow that functions and one that creates downstream problems.
The fleet management problem is bigger than any individual device
Field operations involve dozens of devices, sometimes hundreds, deployed across locations, disconnected from a central infrastructure, and operating outside any visibility that IT or operations teams have at headquarters.
But it always comes down to whether the individual device is as protected as it needs to be. Or, whether the organization has any reliable way to know the protection standing across all its deployed devices. Or, whether data has been properly handled before drives are shipped back or repurposed, and if there’s an audit trail should something goes wrong.
Teams answering no to those questions should weigh the risks carefully.
In CISA’s 2026 Binding Operational Directive on end-of-life edge devices, the directive flags this exact point for federal agencies, including the risk of hardware that sits outside IT visibility and where misconfigurations and security gaps can go undetected long enough for bad actors to operate inside them. While the directive is scoped to government, the underlying dynamic that’s described applies anywhere organizations are running field operations with storage hardware that wasn’t designed for enterprise management.
Fleet visibility requires storage devices that are built to be managed at scale as a design requirement, moving beyond simply a configuration option. This is an area where consumer-grade storage falls short.
What the AI layer changes
Field teams are increasingly doing more than capturing and transporting data. They’re also deploying proprietary AI models alongside their datasets, running inference at the edge, and working with outputs that carry significant IP value independent of the underlying data.
That means the storage conversation for them needs to account for two categories of sensitive asset, including the data being collected or processed, and the model doing the processing. For teams working with proprietary models, particularly those developed for specialized applications in media, defense, or industrial contexts, the model itself represents years of investment and competitive differentiation. Protecting it in the field requires the same hardware-level security approach as protecting the data around it.
The ability to run a portable, encrypted, high-performance computing and storage environment in the field with the model and the data both protected and recoverable, is an operational necessity, and even a requirement, across industries. Consumer hardware doesn’t meet these needs.
The practical path forward
Teams don’t need to reinvent edge infrastructure from the ground up, but rather treat their storage as a first-class decision in field operations instead of a last-minute procurement choice.
In practical terms, that means specifying hardware-enforced encryption and autonomous RAID as baseline requirements, not optional add-ons. iodyne builds exactly the kind of storage engineered for the actual conditions it’ll operate in rather than the conditions a spec sheet assumes. This means building fleet management into edge storage programs so that IT and operations teams have real visibility across distributed deployments. And it means recognizing that as AI capability moves to the edge, the data and models that capability depends on need protection that moves with them.
The edge AI buildout is well underway, and the investment thesis behind it is sound. But an AI strategy built on storage that wasn’t designed for the field is carrying risk that most organizations haven’t fully accounted for. The smarter the compute gets at the edge, the more it matters that the storage underneath it is smart too.
Jason Williams is the Chief Operating Officer at iodyne, which builds professional-grade, hardware-secured storage for field and edge environments. iodyne serves teams in media and entertainment, defense, government, healthcare, and industrial sectors.
