Role of Artificial Intelligence in the Detection of Social Engineering Attacks
DOI:
https://doi.org/10.47672/ejt.2790Abstract
Purpose: Social engineering attacks are a major concern in cybersecurity, leveraging human psychology to access sensitive information or systems without authorization. Phishing, Chief Executive Officer (CEO) scams, and deep-fake impersonation have resulted in enormous financial and reputational loss to organizations globally. All these have proved conventional security systems to be inadequate in countering the highly developed and targeted methods used by cybercriminals. This paper therefore highlights the potentials of Artificial Intelligence (AI) to improve the detection and prevention of social engineering attacks.
Materials and Methods: Popular real-life cases were subjected to critical analysis together with AI tools such as Predictive analytics, AI powered voice, in addition to Multi-modal detection and Natural Language Processing based (NLP-based) fraud detection.
Findings: AI tools were seen to have prevented and provided complete defense against social engineering attacks. Predictive analytics permits pre-emptive detection of threats, with the potential to anticipate attacks and eliminate them before they are launched. Multi-modal detection systems, including NLP to analyze email phishing and voice forensics to detect synthesized voices by probing several communication avenues together.
Unique Contribution to Theory, Practice and Policy: This paper explores how integrating behavioral science with AI-driven detection systems can help organizations identify psychologically targeted threats, implement adaptive threat detection and strengthen security frameworks through intelligent preventive strategies.
This paper also illustrates how the integration of AI in cybersecurity systems enables organizations adopt more adaptive and proactive security postures, thereby countering social engineering threats and enhancing overall security resilience.
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Copyright (c) 2025 Oluwatosin Temitope Ogunlade
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