There is an old adage about the futility of locking a barn door after the horses have been stolen.
The same principle applies to cybersecurity. If a cybersecurity system does not alert you to an attack, or if it alerts you to an attack only after the damage has already been done, what good is it?
For years, the cybersecurity industry has based the majority of its malware detection systems on seeking and finding what are essentially previously seen, previously documented, attacks.
And after attackers had developed new types of attacks, such as “polymorphic malware” (malware that changes its “signature” as it progresses through time and space), the cybersecurity industry responded with probabilistic systems to try to detect attacks “similar” to those previously documented.
The result has been a serious failure on two counts:
- An inability to detect the truly new and innovative malware attacks.
- A tidal wave of “false positives”, that is, alerts on “detected attacks” that were not really attacks.
The first of these is an obvious shortcoming. As we have said many times, “if you can’t detect, you can’t protect.” However, the second is equally as bad, if not worse, because valuable and expensive resources are then expended chasing phantoms rather than responding to real attacks.
The Growing Cost of Filtering False Positives - Why Filtering False Positives Is No Longer Enough
The response of the cybersecurity industry to its self-induced plague of false positives has been to:
- Set up banks of cybersecurity experts to review all alerts in order to filter out the false positives, before passing them along.
- Use Artificial Intelligence (AI) systems to help the experts filter out the false positives.
Nevertheless, once again these measures have not solved the basic “false positive” problem. Endemic to this “solution” are:
- The significant costs inherent in maintaining a dedicated bank of experts, even if they are supported by AI tools.
- The time that elapses during the transmission of the mass of alerts to the experts, and the time that elapses while those experts filter out the false positives, even if they are supported by AI tools.
Although the high costs of coping with false positives have been with us for quite a while, it is the second of these, the time required to filter alerts, that is rapidly and seriously growing into a significant problem.
The speed of malware attack propagation is increasing precipitously, especially with the aid of AI. Not that long ago, the “breakout time” — the time required for malware to jump from device to device — was around ten hours. Now, a recent posting noted that CrowdStrike’s 2026 Global Threat Report claims that the “Average AI-assisted cyberattack breakout time in 2025 [was]: 29 minutes” and also that in “one documented incident, attackers began pulling data within 4 minutes of entry.”
The inescapable implication of these statistics is that we no longer have the luxury to take the time required to filter out false positives from our malware detection systems. Malware detection must be accurate, and it must be rapid. This is especially true in the OT environment where access to backend systems and backend banks of experts is simply not feasible.
Accurate, “deterministic” detection and the determination of how to react to that detection must be accomplished “out in the field”. Probabilistic, false-positive-plagued systems can have no place in such environments.
The Operational Cost of Delayed Response
In traditional IT environments, delayed detection can lead to data loss, business interruption, financial damage, and reputational harm. These consequences are severe enough.
In critical infrastructure, however, delay carries an additional operational burden because the affected systems do more than store, process, and transmit information.
Cybersecurity failures impact water systems, electric power grids, transportation networks, and industrial operations. These systems affect physical outcomes. Any delayed response can allow an attacker to move from a single unauthorized change to wide-ranging operational manipulation. What begins as an unseen infection inside one device may, within a very brief period of time, become the foundation for a coordinated massively disruptive attack.
The problem is exacerbated because many OT and IoT devices were never designed to host conventional security tools. Limited memory, constrained processing capacity, and intermittent connectivity are not incidental features of these environments. They define the environments. Those security strategies that ignore those constraints begin with a fiction, the fiction that it is possible to monitor a device without being inside of the device.
Today’s sophisticated AI malware and Advanced Persistent Threats (APTs) can, from inside a device, emulate all of the external metrics used to monitor device health. Only an agent that operates inside a device can determine if a device has been infected. “If you are not inside, you cannot detect.”
The purpose of internal rapid detection, therefore, is not to merely satisfy a metric. It is essential to any effective and actionable defense.
Not All Alerts Are Create Equal
Cybersecurity alerts come in a variety of flavors and from a variety of sources. Some of them can even be counterproductive. For example:
- Alerts that appear too late for the defenders to respond to the danger are not the same as “Actionable Alerts”.
- Alerts that are not “high-fidelity” alerts, that is, alerts that are generated by probabilistic and statistical algorithms and may possibly be embedded in streams of alerts that ultimately need to be filtered for false positives are not the same as deterministic, high-fidelity alerts.
For an alert to be of any value, it must be actionable. That is, it must arrive in time for the defenders to react effectively, and it must be “real”. That is, it cannot be a phantom.
The cybersecurity industry has produced no shortage of alerts. Many organizations now receive more warnings than their teams can reasonably investigate. This has resulted in a well-documented phenomenon known as SOC (Security Operations Center) Operator Fatigue.
An alert that cannot be trusted slows response because it demands verification before action. Analysts must investigate it. Supervisors must validate it. Operators must weigh the risk of acting against the risk of waiting.
If a tool has a history of false positives, hesitation becomes routine. Eventually, even serious alerts may be treated as part of the background noise.
This problem is especially dangerous in operational environments. Shutting down a process, isolating a device, or interrupting a control system may have real-world consequences. An operator cannot treat every ambiguous warning as a confirmed attack. Nor can an operator safely ignore a true compromise.
The entire value of an alert lies in whether it can support responsible judgment under pressure. An actionable alert should be the result of accurate, deterministic, detection.
Trusted Detection Enables Faster Decision-Making
Response speed depends less on how quickly a tool produces an alert than on how quickly it can be acted upon.
This distinction is often overlooked. A system may generate a warning very quickly and still fail to accelerate response. This is notably true in environments where warnings are vague and/or untrusted.
Deterministic detection changes this sequence. It begins, for example, with a binary question: did an unauthorized change to an instruction set occur? The answer to this question can be either “yes” or “no”. There is no middle ground, no gray area, no room for false positives.
This is especially important in OT and critical infrastructure. A false alarm can disrupt operations while a missed alarm can allow an attacker to proceed. A powerful and effective defense can rely upon neither panic nor complacency. It functions best when trustworthy information is delivered rapidly enough to support reasonable and appropriate action.
Trusted detection significantly reduces the time required to react and to remediate. It reduces the ambiguity that attackers exploit, and it gives defenses a firm basis for appropriate actions.
Detection Begins Inside of a Monitored Device
No cybersecurity system can respond to what it cannot see. In many OT and IoT environments, the devices that must be protected are small, resource-constrained devices. These devices were built for reliability and continuity, not for modern cybersecurity instrumentation. Traditional IT cybersecurity agents require far more memory and processing capacity than are available in these devices.
Because most cybersecurity tools cannot operate inside of the monitored devices, the industry has turned to a reliance upon external observation. External observation has its value, but it also has its limits. A compromised device may continue to appear normal from the outside. Its network traffic may look as expected. Its logs may appear legitimate. Its responses to status request queries may be those of an uncompromised device.
In situations that lack internal monitoring, security teams may be watching a chimera, a phantom image of the device rather than the device itself. If the tool cannot operate from inside the device, true visibility into what is happening inside of the device is impossible. Only monitoring from inside of a device can provide the required efficacy of detection.
Rapid Detection, Alerting, and Isolation Change the Response Window
Critical infrastructure operators should evaluate cybersecurity systems according to first principles. A viable system must be able to accomplish these three things:
- Deterministically detect threats, rapidly and unambiguously
- Alert quickly enough for defenders to respond before compromise becomes disruption
- Support isolation before the attacker can spread, persist, or prepare a coordinated attack
Together, these capabilities change the defender’s position. The goal is to acquire knowledge of a threat with sufficient time and certainty to act, not merely to learn that something has happened after the fact.
Crytica Security’s Rapid Detection, Alert, and Isolation system (RDAi™) is designed around this principle. It does not rely on virus signatures, AI pattern matching, behavioral scoring, or historical databases. It detects threats through deterministic logic.
Crytica’s tiny Probe is small enough to operate inside many resource constrained OT and IT devices where conventional tools cannot fit nor operate internally without disruption. That internal presence is essential because it gives defenders direct visibility into devices rather than forcing them to infer compromise from external and ambiguous symptoms.
When RDAi™ detects a threat, it generates trusted alerts that can support rapid responses and, if warranted, isolation. It also works alongside existing EDR, MDR, and XDR systems, strengthening them by reducing the time between infection and awareness.
By moving trusted detection closer to the moment and location of compromise, this approach can strengthen any existing cybersecurity stack.
Resilience Belongs to Those Who Can Act in Time
Cyber resilience is measured by whether defenses retain the ability to act in time. Otherwise, late detection becomes a postmortem analysis.
Response without device-level visibility begins from a weaker position. Security that cannot identify threats quickly enough and reliably enough to support action is security theater wearing the costume of sophistication.
The path forward requires a return to first principles: threats must be detected where they occur, and when they first appear, alerts must provide enough clarity to support action, and isolation must begin before the attacker has time to turn compromise into disruption.
Crytica Security purpose built RDAi™ to fit inside constrained devices, to rapidly detect unauthorized changes to instruction sets and performance anomalies, to generate trusted alerts, and to facilitate faster responses across OT, IoT, and critical infrastructure environments.
To learn how Crytica Security approaches cyber resilience through rapid detection, trusted alerting, and decisive isolation, please contact our team. We can provide a closer look at how RDAi™ helps defenders reduce dwell time and act with greater confidence when unauthorized change to instruction sets occur.
