Phishing Threat Landscape: An Overview of Attack Patterns and Preventive Measures

Phishing remains a dominant cybersecurity challenge, exploiting both human psychology and technical vulnerabilities to compromise sensitive data. This paper presents a structured analysis of the evolving phishing threat landscape, identifying key attack vectors and proposing a layered, AI-augmented detection framework aimed at enhancing early threat identification and mitigation. Our research introduces a pattern recognition model based on machine learning and behavioral analytics to improve the accuracy of phishing detection while minimizing false positives. We further evaluate the effectiveness of defense mechanisms such as browser-based filtering, multi-factor authentication (MFA), and user-centric education modules. The study fills critical gaps in existing phishing countermeasures by offering a security model validated using real-world phishing datasets and performance metrics such as precision, recall, and F1-score. Finally, we outline future research directions including blockchain for decentralized identity verification and behavioral biometrics for enhanced user authentication. This work contributes a novel, integrated approach to phishing prevention and detection, tailored to modern digital ecosystems.