Cyber security is experiencing a paradigm shift driven by rapid digital transformation across industries. Technologies such as cloud computing, Artificial Intelligence (AI), the Internet of Things (IoT), big data analytics, and 5G connectivity have significantly enhanced operational efficiency and innovation. However, this hyperconnectivity has simultaneously expanded the attack surface, creating complex and dynamic threat environments. Traditional perimeter-based security models—often centered around firewalls and isolated defense systems—are no longer sufficient in a borderless digital ecosystem where users, devices, and applications operate beyond conventional network boundaries. The rise of remote work, multi-cloud environments, and edge computing further complicates security management, requiring adaptive and intelligent defense mechanisms. Modern cyber adversaries are increasingly sophisticated, leveraging automation and AI-driven tools to execute highly targeted and scalable attacks. Social engineering tactics, particularly phishing and deepfake-based impersonation, exploit human vulnerabilities rather than technical weaknesses. The commercialization of cybercrime—through models such as ransomware-as-a-service (RaaS)—has lowered the barrier to entry for attackers, enabling even non-technical individuals to conduct complex operations. These developments highlight the urgent need for organizations to transition from reactive security approaches, which focus on incident response after an attack occurs, to proactive and predictive models that anticipate, detect, and neutralize threats before damage is inflicted. One of the most transformative trends in cyber security is AI-driven threat detection and response. Machine learning algorithms analyze vast volumes of network traffic and behavioral data to identify anomalies that may indicate malicious activity.