Background: Modern digital infrastructures face a dual challenge: maintaining high-velocity data processing in distributed databases while defending against increasingly sophisticated, AI-driven cyber threats. Traditional security overlays often create throughput bottlenecks, forcing organizations to trade performance for protection.

Methods: This study presents "Autonomous Resilience," a unified framework that integrates a Generative Adversarial Network (GAN)-based Intrusion Detection System (IDS) with a machine learning-driven query optimizer. Utilizing a hybrid architecture, the system dynamically adjusts database partitioning and query routing based on real-time threat probability scores. We evaluated the framework using a synthetic environment mimicking Industry 5.0 protocols, measuring both threat detection accuracy and query latency under heavy load.

Results: The proposed GAN-based model achieved a 98.4% detection rate for zero-day anomalies, outperforming traditional supervised learning methods. Simultaneously, the adaptive query optimizer maintained a 15% reduction in latency during active scanning periods compared to static baselines.

Conclusion: The integration of generative AI for security and adaptive algorithms for data management offers a viable path toward self-healing, resilient digital ecosystems. However, the implementation requires careful consideration of uncertainty quantification and the evolving role of human operators in the loop.