Abstract

Phishing, a prevalent form of social engineering attack, continues to threaten enterprises by exploiting human vulnerabilities and targeting sensitive information. With the increasing sophistication of phishing schemes, traditional detection methods often fall short in identifying and mitigating these threats. As attackers employ advanced techniques, such as highly personalized spear-phishing emails and malicious links, enterprises require innovative solutions to safeguard their digital ecosystems. This research explores the application of artificial intelligence (AI) in enhancing phishing detection and response, with a specific focus on addressing advanced social engineering tactics. AIpowered phishing detection systems utilize machine learning (ML) and natural language processing (NLP) to analyze communication patterns, detect anomalies, and identify deceptive content in real-time. Unlike conventional rule-based systems, AI approaches adapt dynamically to evolving attack vectors, offering a robust defense against both known and emerging threats. These systems can scrutinize email headers, content, URLs, and attachments to uncover subtle indicators of malicious intent. By leveraging historical datasets and predictive analytics, AI models identify patterns associated with phishing, such as unusual sender behavior, suspicious linguistic cues, and mismatched domain information. This study examines the critical components of AI-driven phishing detection, including feature extraction, algorithm selection, and system deployment within enterprise environments. It highlights the role of NLP in understanding the contextual and linguistic subtleties of phishing messages and how advanced ML models, such as decision trees, support vector machines, and deep neural networks, enhance detection accuracy. Additionally, the research delves into the challenges of implementing AI in phishing detection, such as ensuring data privacy, mitigating adversarial attacks on AI models, and fostering trust through explainable AI. Real-world case studies are analyzed to demonstrate the practical effectiveness of AI-powered phishing detection systems. These examples showcase significant reductions in phishing incidents, improved detection times, and the ability to thwart highly targeted attacks, such as business email compromise (BEC) schemes. The study also emphasizes the importance of integrating AI solutions with existing cybersecurity frameworks and highlights the role of human oversight in complementing AI capabilities. While AI offers promising advancements, its success depends on robust training datasets, continuous model optimization, and alignment with enterprise security goals. The paper concludes by presenting best practices for deploying AI-based phishing detection, including regular model updates, employee awareness programs, and multi-layered security strategies. By leveraging AI, enterprises can proactively defend against advanced social engineering attacks, safeguarding their systems, data, and stakeholders from evolving cyber threats. This research contributes to the growing field of AI applications in cybersecurity, offering actionable insights for organizations seeking to enhance their resilience against sophisticated phishing campaigns.