Beyond Ticketing: A Governance-Enforced Autonomous Control Plane for Cloud Database Operations at Enterprise Scale

Aim: This study aims to address persistent bottlenecks in enterprise database operations caused by manual, human-in-the-loop workflows. It introduces the Autonomous Database Control Plane (ADCP), a governance-first architecture designed to automate high-volume, deterministic database operations while enforcing policy, maintaining auditability, and preserving human oversight for high-risk or non-deterministic requests. Methods: The study presents the ADCP framework, developed and deployed within a Snowflake environment at a global asset management organization and subsequently validated at a second financial services institution. It introduces the RPEA abstraction model (Request, Policy, Execution, and Audit) as a platform-agnostic approach for structuring database operations. The framework is evaluated based on its ability to automate service requests, enforce execution-layer policies, and maintain a tamper-resistant audit trail within compliance-constrained environments. Results: The implementation of ADCP achieves an 80% autonomous resolution rate across more than 500 monthly service requests. It reduces average resolution latency from 2-3 business days to under 5 minutes. The findings demonstrate that governance-first automation can effectively eliminate manual intervention in deterministic database operations while ensuring policy enforcement and auditability. Conclusion: The study concludes that the ADCP architecture provides a viable solution to the limitations of existing approaches such as DevOps pipelines, ChatOps bots, and self-service portals. Recommendations: The study recommends extending the ADCP model to other infrastructure domains where privileged actions require strict policy enforcement at the execution layer. It also suggests broader adoption of the RPEA abstraction model in cloud database environments that expose programmatic APIs under compliance constraints, to enhance automation, governance, and operational efficiency.