EDITOR ’ S QUESTION
Authentication , one of the critical aspects of cybersecurity , remains a constant race between attackers and security professionals . We have seen the industry evolve from passwords to multi-factor authentication , to biometrics . Now one area of growing concern is the exploitation of deepfake technology , which attackers use to bypass even biometric and video-based verifications .
To combat this , modern cybersecurity solutions are integrating advanced AI models that analyse micromovements , pupil responses , and infrared patterns , making it harder for deepfakes to succeed .
Multi-layered approaches that combine facial recognition , voiceprints , and behavioural pattern analysis offer enhanced protection by adding complexity to authentication processes . An emerging best practice is to verify user identity not only during login but throughout active sessions , reducing the risk of account hijacking .
Another pressing challenge is the use of AI to mimic natural conversations for fraudulent purposes , particularly in support scenarios . The ease of access to powerful LLMs makes this an easy to implement and often highly effective option for cyber criminals .
To counter this , security solutions now incorporate context-aware monitoring , which cross-references interaction data with historical patterns to flag inconsistencies . Specialised tools capable of recognising AI-generated voice patterns are critical for identifying fraudulent calls .
Complementing these measures , tracking subtle behaviours , such as typing patterns and navigation , can confirm the legitimacy of users during interactions , ensuring robust security without compromising user experience . processes such as security questions , registered mobile numbers and so on , prompting the need for dynamic alternatives .
Leading solutions now replace traditional security questions with context-based challenges that assess user-specific knowledge , such as recent activities or behavioural patterns . By tailoring account recovery processes to individual risk profiles , organisations can thwart large-scale exploitation attempts .
Social engineering is another powerful tool in the arsenal of attackers and here too , the rise of AIpowered social engineering adds another layer of complexity . Modern tools can counter this by analysing call or message content in real time to detect fraud indicators , such as pressure tactics or inconsistent language . Organisations are also investing in interactive training programmes that educate employees and users on recognising advanced scams , fostering a culture of awareness and preparedness .
Multi-layered approaches that combine facial recognition , voiceprints , behavioural analysis offer enhanced protection by adding complexity to authentication .
Effective cybersecurity solutions should integrate AI-driven threat intelligence , collaborating with global organisations to maintain updated repositories of known fraud patterns . Equally important is the regular testing of defences against AI-generated phishing and spoofing attacks which ensures readiness against evolving threats . p
AI has also exposed vulnerabilities in account recovery workflows . Attackers exploit static verification
SERGEY BELOV , DIRECTOR OF INFORMATION
SECURITY , ACRONIS
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