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.