AI-driven phishing detection systems are a critical component of modern cybersecurity, as they directly influence information security, user trust, and organizational resilience. Their primary objective is to reduce exposure to malicious emails and websites while ensuring rapid, accurate identification of phishing attempts in large-scale digital communication environments. This paper discusses current technical approaches to phishing detection, focusing on the application of artificial intelligence, natural language processing, and machine learning techniques to analyze email content, URLs, metadata, and user behavior patterns. The relationship between automated phishing detection and intelligent security decision support is examined, highlighting its growing importance in next-generation security operations. Limitations in existing manual review and rule-based detection methods are identified, emphasizing the need for innovative AI-based solutions that enhance detection accuracy, efficiency, and interpretability while complementing existing security workflows rather than replacing established processes.