Artificial intelligence is transforming cybersecurity. Unfortunately, security professionals aren’t the only ones using it.
Cybercriminals now employ AI-enabled malware, a new breed of cyberattacks that can adapt, learn, and strike faster. This emerging threat creates serious problems for organizations relying on traditional security measures. To stay ahead, they need to understand AI-enabled malware and how to defend against it.
The Rise of AI-Enabled Malware
“AI-driven malware uses machine learning to dynamically alter its code, behavior, communication methods to avoid detection. It can analyze the target environment in real-time and adapt its tactics to bypass specific security controls,” explains Stephen Kowski, Field CTO with SlashNext. “Some advanced AI malware can even predict defensive responses and preemptively evolve to maintain persistence.”
According to a 2024 Gartner survey, AI-enhanced malicious attacks are now the top emerging risk for enterprises. The survey highlights how easy access to AI tools makes it easier for cybercriminals to carry out more damaging attacks.
AI-Powered Cyberattacks
AI-enabled malware can take many forms, each using artificial intelligence to enhance its capabilities. For example, AI can make phishing attacks more effective by analyzing social media and other public data to help craft personalized emails. It can also expedite the identification of system vulnerabilities, uncovering unpatched software and misconfigurations.
AI is also removing many of the barriers to successful ransomware attacks by automating and refining key processes. Cybercriminals can leverage it to streamline research on potential targets, pulling data from multiple sites to create tailored, high-volume phishing campaigns. This precision extends to writing phishing emails, even in non-native languages, with AI minimizing the errors that often expose them. Additionally, AI helps uncover vulnerabilities and even automates code writing, allowing attackers to act quickly and scale their operations with fewer experienced engineers. Once inside a system, AI allows attackers to hide malicious activities, such data exfiltration, within normal traffic, making detection much harder.
Automating Traditional Malware Techniques
AI doesn’t just make malware smarter—it makes it faster. Automation allows cybercriminals to conduct reconnaissance, exploit vulnerabilities, and launch attacks at scale. Tasks that once took days or weeks can now be executed in minutes with AI.
For instance, AI-enabled malware can automate the process of scanning networks for weaknesses, identifying the best points of entry without any human intervention. Once inside, it can adapt its tactics to the specific defenses in place, modifying its behavior to avoid detection.
Traditional security tools are no match for this type of threat. AI-enabled malware doesn’t have a predictable pattern that can be easily identified. Instead, it learns from its environment and alters its behavior accordingly. This is why many experts are warning that AI-driven cyberattacks will become the norm in the coming years.
Defensive Strategies: Fighting AI with AI
The good news is that AI isn’t just a weapon for cybercriminals—security professionals can use it too. In fact, the best way to combat AI-enabled malware is by leveraging AI in defense.
Nicole Carignan, Vice President of Strategic Cyber AI at Darktrace, concurs. “As threat actors adopt new techniques, traditional approaches to cybersecurity fall short. To combat emerging challenges from AI-driven attacks, organizations must leverage AI-powered tools that can provide granular real-time environment visibility and alerting to augment security teams. Where appropriate, organizations should get ahead of new threats by integrating machine-driven response, either in autonomous or human-in-the loop modes, to accelerate security team response. Through this approach, the adoption of AI technologies—such as solutions with anomaly-based detection capabilities that can detect and respond to never-before-seen threats—can be instrumental in keeping organizations secure.”
Adopting these methods is a good start.
Behavior-Based Detection
Behavior-based detection doesn’t rely on identifying known threats. Instead, it monitors for suspicious activity and anomalies in real time. By analyzing patterns of behavior within a network, AI-powered systems can detect malware that’s trying to blend in by mimicking legitimate activity.
For example, if malware suddenly begins accessing sensitive data at unusual hours or from an unexpected location, an AI-driven defense system can flag that behavior as suspicious, even if it’s never seen this specific malware before. The system can then take immediate action, such as isolating the infected device or blocking the unauthorized access attempt.
This proactive approach gives defenders an edge over AI-enabled malware. By monitoring behavior instead of just looking for known threats, security teams can identify attacks early, often before they cause significant damage.
AI-Driven Response
AI-driven response systems automatically detect and counter cyber threats in real time. When they identify suspicious activity, they can take immediate action. This might include isolating compromised systems, redirecting network traffic, or shutting down affected devices. Unlike traditional responses, which often rely on manual input, AI-driven response systems can act quickly, minimizing the damage.
Machine Learning
Machine learning takes the fight further. Beyond immediate responses, ML systems learn from past data to spot new patterns or behaviors. This continuous learning makes future detection and responses even more precise, allowing cybersecurity defenses to stay a step ahead of evolving threats.
Preparing for the Future of AI in Cybercrime
AI-enabled malware represents the next frontier in cybercrime. It’s faster, more adaptable, and more dangerous than traditional threats. But organizations aren’t powerless. By taking a fight-fire-with-fire approach, companies can protect themselves against this evolving threat.
"I see great promise in behavior-based detection, where AI excels due to its ability to understand complex contexts and relationships,” summed up Brandon Torio, Senior Product Manager and AI Expert at Synack. “Unlike rigid, rule-based systems, AI can analyze vast amounts of data to identify patterns and deviations that might indicate malicious behavior, even when the attack vectors are novel or obfuscated. You can see the power in AI's contextual understandings in conversational experiences - this expertise will extend to analysis of malicious behavior and patterns, which is where it must excel against AI-powered threats."
