CISOs Turn to Behavioral Biometrics to Detect AI Threat Actors

Artificial intelligence is changing cybersecurity by enabling attacks to operate with unprecedented speed and autonomy. Advanced AI models can now identify software vulnerabilities and execute attacks with little or no human involvement.

As these capabilities mature, distinguishing automated threat actors from legitimate users is becoming far more difficult, creating new challenges for enterprise identity security.

Behavioral biometrics is becoming an additional layer of defense that continuously evaluates how users interact with devices and applications throughout an active session. Instead of relying only on credentials or one-time authentication, the technology measures whether user behavior remains consistent over time.

When activity begins to deviate from an established behavioral profile, security teams gain another opportunity to detect suspicious activity before an attack progresses further. This continuous approach also helps strengthen identity verification without creating additional friction for legitimate users

Why It Matters: Identity has become one of the most important fronts in enterprise security as AI enables attackers to imitate legitimate users with greater accuracy. Organizations are placing greater emphasis on validating behavior throughout a session instead of relying solely on access granted at login. Behavioral biometrics supports this approach by helping security teams identify suspicious activity that static authentication methods may never see once a session is underway.

• AI Changes the Threat Model: AI is making familiar attacks more effective by removing much of the manual effort required to carry them out. A phishing campaign that once took days to build can now be generated in minutes. Vulnerabilities can also be identified and exploited much faster, allowing attackers to move through environments with greater speed. The result is a threat environment where automated attacks can adapt more quickly than many traditional security controls were designed to address.

• Continuous Identity Verification: Traditional authentication confirms a user’s identity at a single moment in time. Behavioral biometrics continues evaluating activity after access has been granted, looking for changes that no longer match a user’s established behavior. This ongoing analysis provides another layer of visibility throughout the session, improving the chances of identifying suspicious activity before access is expanded or sensitive data is exposed.

• Behavior Plus Context: Behavior alone does not tell the full story. Platforms also evaluate contextual information such as device type, login location, or previous access history. Combining these behavioral patterns gives security teams more confidence when evaluating user identity.

• Enterprise Adoption Grows: Financial institutions are reducing payment fraud, while retailers are using behavioral biometrics to combat bots and account abuse. Manufacturers and other enterprises are also adopting the technology to protect intellectual property and identify AI-enabled insider threats. As adoption expands across industries, behavioral biometrics is becoming a more common component of enterprise identity and access strategies.

• Continuous Improvement: Behavior changes over time, so detection models must change with it. The article recommends starting with measurable use cases, expanding deployments in phases, and regularly refining behavioral models. Organizations that continually evaluate performance and adjust detection thresholds are more likely to maintain accuracy as user behavior and AI-enabled attacks continue to change.

Go Deeper -> Behavioral biometrics: How to detect nonhuman threat actors – TechTarget