AI in Cybersecurity: The Solution for Threat Detection?

Artificial intelligence (AI) is everywhere, rapidly shaping industries from healthcare to finance — and cybersecurity is no exception. With countless vendors claiming their AI-powered malware detection can autonomously protect your systems from evolving cyber threats, it’s easy to be convinced that AI is a silver bullet solution. But is AI cybersecurity really the miracle it’s marketed as?

An AI Thought Experiment

Before diving deeper, let’s engage in a brief thought experiment. If we assume that we, the defenders or the “good guys,” have access to AI but the enemy, the cybercriminals, do not, then yes, using AI as part of an overall malware detection strategy might be a good idea.

However, consider this: It would be the epitome of hubris, of unmitigated gall, and of arrogance to assume that only we have access to AI, or even to assume that our AI is superior to that of the cybercriminals. Indeed, the enemy has access to the same technology, the same research, and probably superior cyber resources than we do. They certainly have access to far superior funding than we do.

Therefore, when we embark upon the cybersecurity “AI shall save us” route, we are embarking on an AI arms race. It is a race against a well-funded, highly-motivated, well-equipped, and well-educated enemy. It is a race in which we must win every battle, every confrontation, while, at the same time, the enemy can afford to lose a thousand battles and win only one.

Does that sound like a race we really want to enter?

Understanding AI in Cybersecurity

And yet, if you read the popular press, or even the most respected tech journals, you might think AI is the long-awaited savior of cybersecurity. So, let’s suppose that even after our “thought experiment,” you’re still not convinced the cybersecurity AI emperor is, in fact, naked. After all, how could so many wise and esteemed experts praise the emperor’s magnificent attire as unmatched?

To dig deeper, we need clarity on what “AI in cybersecurity” actually means. It’s important to understand that AI is a vast umbrella — one that spans many different disciplines.

Pattern Recognition

Pattern recognition, a branch of AI, excels at identifying patterns — and just as importantly, anomalies — within massive datasets. It can spot similarities and deviations far faster than any human. One practical application is in detecting polymorphic malware: malicious code that constantly changes its form (its “virus signature”) as it moves across systems. Even though the malware shifts shape, AI-powered pattern recognition can still identify the underlying structure — the core pattern embedded in the code.

The same applies to attack patterns and malicious behavior. If malware can be detected by its distinctive attack patterns and behaviors, then even malware that varies its patterns and behaviors may be discerned by sophisticated pattern recognition software.

There is still a downside to this powerful technology. It is not an exact science. It relies heavily upon probability and statistics. Therefore, it is “probabilistic” not “deterministic.” In other words, it makes mistakes. It sometimes sees malware where none is present (also known as “false positives”). Sometimes, the false positive rate is so high that entire teams of skilled cybersecurity professionals are needed just to sift through the AI-generated alerts and separate the noise from the real threats.

The false positive problem has become so severe that new AI systems are now being used just to help cybersecurity professionals sort through the overload. SentinelOne’s Purple AI is often cited as one of the more effective tools for this task. But even with AI-assisted triage, there’s a bigger issue: all that winnowing takes time. And when it comes to malware, time is everything. The delay in responding — while alerts are being filtered — can be the difference between a quick containment and being too late to the party.

Predictive and Projected Malware Models

Of all the recent AI advancements, it’s generative AI that has captured the most interest in both the press and the industry. Tools like ChatGPT can be used to analyze trends in malware development and even predict future attack types. Some researchers have even used these tools to write new malware as a way to better understand potential threats.

While predictive models can offer useful foresight, they’re far from foolproof. It’s inevitable that cybercriminals will also leverage AI to create sophisticated AI-generated malware with alarming efficiency and in unprecedented quantities. AI-powered tools like ChatGPT are not limited to benign content generation — they are actively being used to produce advanced, hyper-polymorphic malware that continuously alters its own code to evade detection.

Just as our earlier “thought experiment” pointed out, cybercriminals will most certainly produce AI-generated malware that (1) our models have not produced, and (2) are designed to thwart all of our AI-enhanced detection methods.

The Right Role for AI in Cybersecurity

Given the hard reality of AI’s shortcomings, evaluating AI in cybersecurity becomes more critical and complex than ever. Although it has a role to play, can anyone seriously believe that it is the panacea that so many experts and cybersecurity vendors are proclaiming it to be?

Capabilities and Limitations of AI in Cybersecurity

Beyond AI’s potent potential offensive capabilities as a weapon of the cybercriminals, let’s review the capabilities and limitations of AI cyber defense.

What AI Can Do

Rapid detection of patterns and anomalies: AI can analyze vast amounts of data far faster than human analysts, quickly flagging unusual behaviors or emerging patterns.
Automation of routine tasks: AI can reduce manual workloads significantly, meaning human experts can spend more time on sophisticated threats rather than drowning in mundane alerts.
Adaptive analysis: Properly trained AI systems can continually learn from ongoing threats, adapting their detection methods accordingly.

Predictive analysis: Properly trained AI systems can continually learn from ongoing threats in order to predict and create new threats, from which the defenders can learn.

What AI Cannot Do

Autonomous zero-day threat detection: Despite marketing claims, AI by itself struggles to reliably detect newly developed malware, including zero-day threats and advanced persistent threats (APTs) — exactly the kind of malware we routinely face.
Immunity to adversarial attacks: AI systems themselves can be targeted by sophisticated adversaries using purposely manipulated data to deceive detection algorithms. In short, AI outsmarting AI.
Guarantee accuracy without high-quality data: AI's performance depends on the quality of its training data. Put simply, feed it poor data and you’ll get poor results.

Sift through false positives: AI's performance depends on statistical or probabilistic algorithms. Such algorithms will always produce a quantity of false positive results and also miss some real attacks. That is in the nature of statistics and statistical methods. Their results will never have the consistent accuracy that effective deterministic algorithms do.

Smart Ways to Leverage AI for Cybersecurity

AI is not without merit; the problem is how it's marketed and applied. Instead of treating AI as an all-encompassing solution, a smarter approach recognizes AI as one component of a broader cybersecurity strategy. Consider these ways below that AI can support cybersecurity efforts.

Accelerated response time: AI can rapidly correlate and analyze large datasets to pinpoint the source and spread of an incident. By immediately surfacing actionable insights, teams can respond quicker and limit damage. This could include using AI to intelligently filter through the flood of alerts that traditional systems often generate.
User behavior analysis: An AI-powered system analyzes user behaviors and network patterns. It can swiftly identify deviations that may signal insider threats or compromised accounts, enhancing overall security posture.
Predictive risk assessment: AI's predictive modeling capabilities enable proactive security by forecasting potential vulnerabilities or attack vectors before exploitation, which allows teams to hone and prioritize defense strategies.

How Crytica Leverages AI

At Crytica, we take a pragmatic approach to AI in cybersecurity. Rather than placing AI on the front lines — where it is vulnerable to manipulation by adversarial AI — we use it in critical support roles to strengthen security posture in our Rapid Detection and Alert (RDA) system.

For example, let’s review how RDA manages its distributed intelligence topology. By distributing its “intelligence” across operational components, the RDA system is both more difficult to attack and more adaptable to diverse environments.

This architecture isn't static. RDA’s mutually monitoring components can be deployed — and dynamically redeployed — in topologies optimized for the environments they protect. Determining and continuously improving this distributed structure is one of the ways Crytica strategically applies AI.

At Crytica Security, we believe AI has a role in modern cybersecurity. However, we advocate for finding creative, effective, and pragmatic ways to leverage AI into cybersecurity solutions — not desperately holding onto AI as a miracle cure. Because when it comes to cybersecurity, real results beat hype every single time.

More About Crytica’s RDA

Crytica’s Rapid Detection and Alert (RDA) system offers a transformative approach to malware detection. Unlike traditional solutions that rely on historical data or probabilistic models, RDA employs deterministic algorithms to identify threats at the moment of injection — before execution begins. RDA’s autonomous self-healing system also prevents being disabled by malware or insider threats.

By integrating seamlessly with existing cybersecurity infrastructures, RDA provides real-time, actionable insights, enabling swift responses to emerging threats. Its lightweight probes operate continuously with minimal impact on system performance, making RDA ideal for environments like operational technology (OT).

In an age of AI-generated malware, RDA is perfectly situated to effectively counter evolving cyber threats. Ready to see RDA in action for yourself? Book a demo today!

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