Abstract Autonomous SQL servers represent a paradigm shift in database management, leveraging artificial intelligence and machine learning to automate traditionally manual administrative tasks including query optimization, patching, indexing, and workload prediction. This research examines the evolution from conventional database administration to self-driving database systems, analyzing both the operational advantages and cybersecurity implications of autonomy in data management. The study focuses on enterprise implementations including Oracle Autonomous Database, Azure SQL Managed Instance, and AWS Aurora, investigating how AI-driven automation reduces total cost of ownership while introducing novel attack surfaces. Key cybersecurity concerns identified include adversarial machine learning attacks targeting query optimizers, privilege escalation through misconfigured autonomy, AI poisoning attacks, and model drift affecting threat detection capabilities. This paper proposes the Secure Autonomous SQL Lifecycle (SASL) framework, integrating zero-trust privilege automation, continuous authentication, and AI-based anomaly detection to mitigate emerging threats. Comparative evaluation reveals that autonomous systems achieve 47-63% reduction in administrative overhead but introduce complexity in security governance, particularly regarding GDPR, HIPAA, and PCI-DSS compliance. The findings indicate that while autonomous SQL servers offer significant operational benefits, organizations must implement robust security frameworks addressing the unique vulnerabilities introduced by AI-driven automation in database management systems. Keywords: Autonomous databases, self-tuning SQL servers, AI-driven database security, query optimization, adversarial machine learning, zero-trust database architecture, automated privilege management, cloud database security