The Importance of Behavioral Analytics in AI-Enabled Cyber Attacks

Source: The Hacker News
Published: Fri, 20 Mar 2026 10:00:00 GMT

Artificial Intelligence (AI) is changing how individuals and organizations conduct many activities, including how cybercriminals carry out phishing attacks and iterate on malware. Now, cybercriminals are using AI to generate personalized phishing emails, deepfakes and malware that evade traditional detection by impersonating normal user activity and bypassing legacy security models. As a result, rule-based models alone are often insufficient for identity security against AI-enabled threats. Behavioral analytics must evolve beyond monitoring suspicious activity patterns over time into dynamic, identity-based risk modeling capable of identifying inconsistencies in real time.

AI-enabled cyber attacks introduce very different security risks compared to traditional cyber threats. By relying on automation and mimicking legitimate behavior, AI allows cybercriminals to scale their attacks while reducing obvious signals to remain undetected.

Unlike traditional phishing attacks that use generic messaging, AI enables personalized phishing messages at scale using public data, impersonating the writing styles of executives or creating context-aware messages referencing real events. These AI-powered attacks can reduce obvious red flags, slip past some filtering approaches and rely on psychological manipulation instead of malware delivery, significantly increasing the risk of credential theft and financial fraud.

AI-enhanced credential abuse can optimize login attempts while avoiding triggering lockout thresholds, mimicking human-like timing between authentication attempts and targeting privileged accounts based on context. Since these attacks use compromised credentials, they often appear valid and blend into normal login activity, making identity security a crucial component of modern security strategies.

Before cybercriminals could use AI to accelerate malware development and deployment, they had to manually modify code signatures and spend copious time creating new variants. AI can further speed up variation, scripting and adaptation. With modern adaptive malware, cybercriminals can automatically modify code to avoid detection, change behavior based on the environment and generate new exploit variants with little to no manual effort. Since traditional signature-based detection models struggle against continuously evolving code, organizations must start relying on behavioral patterns rather than static indicators.

Traditional monitoring was designed to detect cyber threats driven by malware, known security vulnerabilities and visible behavioral anomalies. Here are some of the ways traditional behavioral monitoring falls short against AI-enabled attacks:

The shift to modern behavioral analytics requires an evolution from simple threat detection into dynamic, context-aware risk modeling capable of identifying subtle privilege misuse.

To appear normal, AI-driven cybercriminals often use credentials compromised through phishing or credential abuse, work from known devices or networks and conduct malicious activity over time to avoid detection. Modern behavioral analytics must evaluate whether even the slightest change in behavior is consistent with a user’s typical behavioral patterns. Advanced behavioral models establish baselines, assess real-time activity and combine identity, device and session context.

Once cybercriminals gain access to systems through compromised, weak or reused credentials, they focus on gradually expanding their access. Behavioral visibility needs to cover the full security stack, including privileged access, cloud infrastructure, endpoints, applications and administrative accounts. For behavioral analytics to be more effective against AI-based cyber attacks, organizations must enforce zero-trust security and assume that no user or device should have implicit trust or automatic authentication based on network location.

AI tools not only empower external cybercriminals but also make it easier for malicious insiders to act within an organization’s network. Malicious insiders can use AI to automate credential harvesting, identify sensitive information or generate believable phishing content. Since insiders often operate with legitimate permissions, detecting privilege misuse requires identifying behavioral anomalies like access beyond defined responsibilities, activity outside normal business hours and repeated activity within critical systems. Eliminating standing access by enforcing Just-in-Time (JIT) access, session monitoring and session recording helps organizations limit exposure and reduce the impact of compromised accounts and insider misuse.

At a time when AI agents can create convincing social engineering campaigns, test credentials at scale and reduce the hands-on effort required to run attacks, AI-enabled cyber attacks are becoming increasingly automated. Protecting both human and Non-Human Identities (NHIs) now requires more than authentication; organizations must im

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Original report: https://thehackernews.com/2026/03/the-importance-of-behavioral-analytics.html