Artificial Intelligence (AI) has become an important part of modern cybersecurity. It can process large amounts of information, compare activity across systems, recognize patterns, and help security teams review potential threats more quickly.
Those capabilities are useful, especially in environments where analysts are already managing more alerts than they can reasonably investigate. AI can reduce some of that burden by organizing information and drawing attention to activity that deserves a closer look.
But faster analysis of uncertainty is still analysis of uncertainty.
When an AI-supported security tool evaluates behavior, historical similarities, anomaly scores, or patterns associated with previous attacks, it is still making an inference. It may reach that inference faster than a human analyst could, but the conclusion remains probabilistic.
That distinction becomes especially important in Operational Technology (OT) environments, where security teams often have less visibility inside the devices they are responsible for protecting. A fast alert may still be difficult to trust, and acting on the wrong signal can interrupt a physical process or disrupt an essential service.
Why AI-Based Cybersecurity Detection Remains Probabilistic
Most AI-supported cybersecurity tools begin with evidence that must be interpreted.
A system may compare newly observed code with malware that has already been documented. It may look for activity that falls outside an established baseline or combine several signals to calculate the likelihood that an attack is underway.
These methods can help security teams find activity that deserves attention. They can also provide useful context when analysts are trying to understand a complicated incident.
But the questions they answer are still inferential.
- Does this code resemble malware that has already been seen?
- Is this behavior unusual enough to indicate compromise?
- Do these signals, when viewed together, suggest that malicious activity may be taking place?
AI can answer those questions quickly, but it does not remove the uncertainty built into them. The system is interpreting signs that may point to a compromise rather than directly detecting the unauthorized change itself.
If a detection threshold is set too low, the system may produce more alerts than the team can investigate. If the threshold is set too high, meaningful activity may pass unnoticed. Behavioral systems also must account for the fact that legitimate environments change over time, which means the definition of “normal” must continue to evolve. The AI systems must continue to learn, and in so doing, they must be able to distinguish between natural evolution and unnatural compromise.
The result is often another layer of analysis before action can begin. Analysts review the alert, compare it with other information, and decide whether the event is credible enough to justify intervention.
AI may accelerate the first step, but the operator still inherits the uncertainty.
Why Inference Creates a Larger Detection Gap in OT Environments
Many OT environments rely on embedded devices that support industrial processes, public utilities, transportation systems, and other critical operations.
These devices often have limited memory and processing capacity, and traditional endpoint security tools are often far too large and/or resource-intensive to operate inside them without negatively affecting performance.
When the security technology cannot fit inside the device, detection usually moves outside of it. Security teams may rely on network traffic, system logs, status responses, or behavior observed elsewhere in the environment.
AI can analyze those external signals and look for indicators that something may be wrong. But external observation has limits.
A compromised device may continue generating “expected” traffic. Its logs may appear legitimate. It may respond normally when queried even though unauthorized instructions are already present inside it. From the outside, the device can look stable while its instruction set has already changed.
In that situation, AI may analyze the available evidence faster, but it is still analyzing what the device appears to be doing. It is not necessarily detecting what has changed inside the device.
This is why speed alone does not solve the OT detection problem. A cybersecurity tool first must be small enough to operate where the change occurs. Without that internal presence, even a sophisticated analysis may begin from incomplete, erroneous, and even maliciously planted evidence.
Deterministic Detection Starts with the Change Itself
Crytica’s Rapid Detection, Alert, and Isolation system, RDAi™, takes a different approach to malware detection.
Every computing device operates through defined instruction sets. When instructions are added, modified, or deleted outside of an authorized change management process, the device has been altered in a way that security teams need to understand.
RDAi™ focuses directly on change, and specifically, on unauthorized change. Rather than asking whether activity resembles known malware or whether behavior appears suspicious, it determines whether the instruction set has changed and whether that change was authorized.
If an unauthorized change occurs, RDAi™ detects it and generates an alert. It does not need to predict what the code may do, compare it with previously documented malware, or wait for malicious behavior to begin.
That gives cyber defense teams a concrete event to investigate. The change may be malicious, it may point to a failed software update, it may be the result of an unauthorized user action, or another operational problem. In each case, the device has changed outside the approved process and therefore requires attention.
This is the practical difference between probabilistic inference and deterministic detection. One estimates whether a compromise may have occurred. RDAi™ detects that an unauthorized change did occur.
How RDAi™ Brings Deterministic Detection Inside the Device
RDAi™ was designed to operate inside constrained OT systems, IoT devices, and embedded devices where traditional endpoint security tools may not fit.
Its lightweight Probe scans the device’s instruction sets and sends the scan information to the RDAi™ Detector. The Detector compares successive scans and detects unauthorized additions, modifications, deletions, or other relevant changes.
Because the Probe operates inside the device, RDAi™ does not have to infer the change from external symptoms. Its small footprint makes device-level detection possible in environments where conventional cybersecurity agents are typically far too large and/or far too resource-intensive to run non-disruptively.
That combination is essential. Deterministic detection cannot close the OT detection gap if the agent cannot operate inside the device. A small footprint is also not enough if the system still relies on uncertain behavioral interpretation.
RDAi™ addresses both problems by fitting inside constrained devices and detecting unauthorized change where and when it occurs.
How RDAi™ Strengthens AI and Existing Security Systems
AI still has an important role in cybersecurity. Once a detected, alert-precipitating event is recognized, it can help teams understand how that event connects to the broader environment and where response should begin.
The important distinction is where AI enters the process.
RDAi™ provides the concrete detection event. AI and the broader cybersecurity stack can then help analyze what happened, determine what may be affected, and support the appropriate response.
RDAi™ is not designed to replace existing EDR, MDR, or XDR systems. Rather, it strengthens them by adding device-level detection internal to the location of the compromise.
When RDAi™ detects an unauthorized change, existing security systems receive a clearer signal. Instead of beginning with a probability that something suspicious may have happened, analysts begin with a confirmed change in a specific device.
That can shorten the path from detection to response because the team is not starting with a broad behavioral concern or an unexplained anomaly. They have a specific change to investigate and can direct their existing tools toward understanding its scope.
This can also improve operator trust. In sensitive OT environments, teams need a reliable basis for action before they isolate a device, interrupt production, or affect service delivery.
Faster Inference Does Not Replace Deterministic Detection
AI will continue to improve the speed of cybersecurity analysis. It can process more information, find relationships faster, and reduce some of the manual burden placed on security teams.
But when the underlying method still depends on behavior, similarity, patterns, or probability scores, the result remains an inference. In OT and IoT environments, that limitation is compounded when the cybersecurity tool is too large to operate inside the device.
RDAi™ changes that starting point.
It is small enough to operate inside constrained devices and uses deterministic logic to detect unauthorized changes to instruction sets. And it does so without relying on behavioral inference, historical resemblance, or probability scores.
That gives cyber defense teams more than a faster assessment of uncertainty. It gives them a specific event that they can investigate and act upon.
Book a demonstration to see how RDAi™ detects unauthorized changes to instruction sets inside OT, IoT, and IT devices and strengthens your existing cybersecurity systems.
C. Lloyd Mahaffey
