AI is now being used in roles where mistakes can have serious consequences. AI is being used on factory floors, in office systems, across vehicle fleets, and in field operations without the direct supervision of an IT team. In such environments, security is not only about protecting data, but also about keeping systems running safely and reliably.
In a November 2025 McKinsey’s survey, 78 percent of respondents said their organizations use AI in at least one business function, up from 55 percent in 2023.
As usage grows, the number of things that can go wrong increases. The risk now extends to the data used to train AI, the systems that deploy it, the devices that run it, and the operational processes around it. With ordinary enterprise software, teams can often patch things quickly, monitor centrally, and respond when issues appear. That does not always work in OT and edge environments. So, companies need to rethink security.
Secure-by-design AI means security is built into the system from the start. It shapes how data is handled, how models are trained, how access is managed, and how systems respond when something goes wrong. Taking this into account, the Cybersecurity & Infrastructure Security Agency (CISA) has stated that AI systems should be secure throughout their lifecycle and safe to use out of the box.
This is important because AI introduces risks that many traditional systems were not built to handle. Training data can be poisoned, and models can be copied, tampered with, or pushed toward bad outputs. Distributed AI setups often span edge devices, on-premise systems, and cloud services, which creates more openings for attackers.
Let’s take a real world example: consider a manufacturer using AI to predict when a machine is likely to fail. If the data feeding that model is corrupted, the system may miss early warning signs, maintenance is delayed, equipment breaks, and a production line stops. This weak point in the data pipeline can easily lead to lost output and real cost.
The same logic applies to vehicle fleets. A company can use AI to optimize routes, flag risky driving, or schedule repairs. Those decisions depend on sensors, mobile networks, software updates, and cloud services working together. If the system accepts bad inputs or runs on poorly controlled devices, the result may be delays, poor routing decisions, or unsafe behavior.
To address these concerns, organizations should start by securing the data pipeline. Many AI problems begin before deployment. If training data or operational data is manipulated, the model may produce poor results even when the rest of the system is healthy. Teams should know what data entered the system, where it came from, how it changed along the way, etc.
Training environments also need tighter control. Models are trained using large datasets, third-party libraries, and development tools. Training environments should be isolated, scanned for vulnerable dependencies, and segmented from production systems.
In operational environments, an important question to ask is whether the deployment process itself can be trusted. If a team cannot verify where a model came from, how it was packaged, and what changed between testing and production, it is carrying risk that may only become visible when something fails. Signed artifacts, secure containers, immutable infrastructure, and centralized secret management all help reduce that risk.
Inference-time security is also important. Once a model is live, it begins responding to real inputs, and that is often where exposure is highest. Input validation, rate limiting, and runtime monitoring are practical controls that help stop malformed or abusive requests from turning into bad outputs.
Monitoring should also be treated as a core component of a product. AI systems in OT and edge settings need continuous monitoring because attacks and failures develop gradually. Sudden changes in input patterns, unusual outputs, or strange activity across devices can be early signs of trouble. Teams also need rollback plans and response playbooks that work in real operating conditions.
Zero Trust matters: One must not assume that a user, device, or connection is trustworthy just because it sits inside the environment. Verify access, limit permissions, and separate systems so one compromise does not spread further than it has to.
Companies also need to think carefully about the security trade-offs between cloud and edge deployments. Cloud AI offers centralization and broader visibility. Edge AI offers lower latency and the ability to keep working when connectivity is limited. Most organizations end up using both, but the same security model can’t be applied to each without adjustment.
U.S. organizations must realize that if AI is deployed in places where uptime, safety, and maintenance windows are constrained, security has to be built for those conditions. It has to account for aging systems, patching limits, intermittent connectivity, and the fact that an operational failure can quickly become a business failure.
That is why secure-by-design AI is a useful way to frame the issue. It shifts the conversation away from last-minute fixes and toward architecture, access, containment, and recovery.