Trust-Aware Database Intelligence (TADI): Embedding Explainable AI into DB Management Decisions with Resilient Dataflow Intelligence

Modern database management systems increasingly rely on artificial intelligence to carry out automated processing within query optimisation and resource allocation, as well as system tuning. However, the obscurity of such AI-based choices artificially also entails significant resistance to trust among database administrators, creating a pane in the wall to system trust and reliability. This paper suggests introducing a new framework called Trust-Aware Database Intelligence (TADI), which combines explainable AI (XAI) and robust data-flow administration, enabling transparent, reliable, and fault-tolerant database processes. We explore the intersection of XAI methods, learned components of a database, and failure-transparent streaming systems, thus coming up with an all-inclusive approach to building both intelligible and resilient database systems. The framework deals with the major issues in automated database tuning, query optimisation, and stateful information-flow recovery without eliminating human control by understandable decision processes. We define architectural patterns, metrics of trust, quantification of metrics, and pragmatic implementation plans, and show how TADI can enhance operational trust and system resilience in production database settings.