Background: The contemporary Security Operations Center (SOC) faces an existential crisis driven by exponential data growth and sophisticated, multi-vector cyber threats. Traditional Security Information and Event Management (SIEM) systems are increasingly insufficient, leading to alert fatigue and delayed response times.

Methods: This study proposes a novel "Cognitive SOC" framework that integrates Generative Artificial Intelligence (GenAI), Security Orchestration, Automation, and Response (SOAR), and emerging computing architectures. We employ a comparative analysis utilizing recent econometric syntheses and productivity studies to model the efficiency gains of AI-augmented security analysts. Furthermore, we evaluate the theoretical integration of neuromorphic computing and quantum algorithms for edge-based threat detection.

Results: Our analysis indicates that GenAI integration is associated with a significant reduction in investigation timelines, mirroring productivity gains observed in software development. Theoretical modeling suggests that neuromorphic architectures could reduce transaction processing latency in edge databases to near-zero levels, enhancing real-time anomaly detection.

 Conclusion: The transition to an AI-driven, potentially quantum-ready SOC is not merely an upgrade but a necessary evolution. While automation offers substantial efficiency improvements, it introduces new risks regarding privacy and operator complacency that must be managed through rigorous governance.