Adaptive AI Governance in Regulated Enterprise Data Platforms: A Trust-Calibrated Automation Framework

Authors

  • Suman Reddy Gaddam

Keywords:

AI Governance Framework, Algorithmic Risk Management, Explainable AI Compliance, Autonomous Decision Systems, Regulatory AI Automation

Abstract

Artificial intelligence (AI) has become foundational to enterprise data platforms in regulated industries, including financial services, healthcare, and compliance-sensitive digital ecosystems. While AI automation improves spotting unusual patterns, making predictions, and scaling operations, giving more decision-making power to algorithms adds challenges in governance, regulatory risks, and overall system safety. Traditional governance methods that depend on fixed rules or after-the-fact checks are not enough for environments where AI is making decisions, as they fail to account for the dynamic nature of AI systems and the need for real-time oversight and adaptability to changing circumstances, particularly in light of the complex challenges posed by algorithmic bias and regulatory compliance in sectors like healthcare and finance. The Trust-Calibrated Automation (TCA) Framework provides a clear method for handling AI that changes how much automation is used based on the specific risks, rules, and financial importance of different decision-making situations. The framework has various control levels, a method to assess overall risks, systems that focus on important issues based on trust, and elements that make sure the design fixes known problems in AI systems, like algorithmic bias that led to a 50% lower identification of high-need Black patients compared to equally sick White patients in healthcare risk prediction.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8126

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Published

14.02.2026

How to Cite

Suman Reddy Gaddam. (2026). Adaptive AI Governance in Regulated Enterprise Data Platforms: A Trust-Calibrated Automation Framework. International Journal of Intelligent Systems and Applications in Engineering, 14(1s), 96 105. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8126

Issue

Vol. 14 No. 1s (2026)

Section

Research Article

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