Social engineering attacks represent a main attack vector in cybersecurity because cyber villains now focus on exploiting human weaknesses instead of technical ones to carry out their exploits. Through psychological trickery, attackers get people to reveal secret information they should not disclose. SOC-2 compliance is analyzed in this paper in terms of its critical position in minimizing social engineering security risks. The research examines prevalent social engineering assault forms that combine phishing with pretexting and baiting together with AI-powered deepfake scams because of their damaging effect on organizational security operations. The fight against these threats uses detection systems powered by artificial intelligence to track fraudulent moves through Natural Language Processing (NLP) and Sentiment Analysis and Behavioral Analytics approaches. The study shows that behavioural analytics reaches a detection accuracy of 92%, proving superior to NLP and sentiment analysis techniques. The results of our study show that security training led to a fundamental reduction in phishing success rates because employees fell from 60% to 25% skilled afterwards. This proves the importance of sustained cybersecurity education for employees. Modern organizations successfully deploy the Zero Trust Model (ZTM) because it reduces unauthorized access events, lowering security vulnerability occurrences during extended periods. The development of social engineering techniques demands firms to combine Artificial Intelligence defenses with human-based security policies in their protection strategies. The research paper also includes a projection of upcoming security threats that combine AI-powered phishing and deepfake-enabled social engineering attacks as entirely new risks for digital security. Combining AI threat detection technology, Zero Trust principles, and security instruction programs, grants organizations elevated protection against social engineering attacks. The research demonstrates why organizations must adapt their security strategies because human factors represent the biggest weakness in defending against modern cyber threats.

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Mitigating Social Engineering Attacks Through AI and Behavioral Analytics in Human-Centric Cybersecurity

  • Abhishek Sharma,
  • Ratan Sarkar,
  • Monica Bhutani,
  • Ranjeeta Saini,
  • V. Manimegalai,
  • G. Deepak Kumar

摘要

Social engineering attacks represent a main attack vector in cybersecurity because cyber villains now focus on exploiting human weaknesses instead of technical ones to carry out their exploits. Through psychological trickery, attackers get people to reveal secret information they should not disclose. SOC-2 compliance is analyzed in this paper in terms of its critical position in minimizing social engineering security risks. The research examines prevalent social engineering assault forms that combine phishing with pretexting and baiting together with AI-powered deepfake scams because of their damaging effect on organizational security operations. The fight against these threats uses detection systems powered by artificial intelligence to track fraudulent moves through Natural Language Processing (NLP) and Sentiment Analysis and Behavioral Analytics approaches. The study shows that behavioural analytics reaches a detection accuracy of 92%, proving superior to NLP and sentiment analysis techniques. The results of our study show that security training led to a fundamental reduction in phishing success rates because employees fell from 60% to 25% skilled afterwards. This proves the importance of sustained cybersecurity education for employees. Modern organizations successfully deploy the Zero Trust Model (ZTM) because it reduces unauthorized access events, lowering security vulnerability occurrences during extended periods. The development of social engineering techniques demands firms to combine Artificial Intelligence defenses with human-based security policies in their protection strategies. The research paper also includes a projection of upcoming security threats that combine AI-powered phishing and deepfake-enabled social engineering attacks as entirely new risks for digital security. Combining AI threat detection technology, Zero Trust principles, and security instruction programs, grants organizations elevated protection against social engineering attacks. The research demonstrates why organizations must adapt their security strategies because human factors represent the biggest weakness in defending against modern cyber threats.