AI Risk Governance in Data Centers
Keywords:
Risk management, Compliance governance, Data centers, Anomaly detection, Predictive analytics .Abstract
Governance, risk, and compliance form the foundation of responsible operations for any enterprise. The increasing business-aligned service delivery for data centers, accompanied by their growing adoption of AI-based technologies, such as predictive and anomaly detection analytics, necessitate further investigation into compliance and risk management frameworks in the context of intelligent data center operations. An AI-driven enterprise risk and compliance governance framework is proposed to support intelligent operations by establishing end-to-end AI service delivery and violation detection for externally imposed regulations and enterprise-defined standards. The risk management framework identifies key risk sources-classified as operational, cybersecurity, reliability, regulatory, and vendor-and proposes a scoring mechanism based on impact, likelihood, and detectability. The outcome of the risk-management framework aids development of a corresponding regulatory and compliance landscape covering data sovereignty, privacy, and protection; data quality, safety and security; industrial safety; and service and products. By capturing end-to-end AI service delivery requirements and integrating the necessary governance signal-processing technologies, the AI-driven enterprise risk and compliance governance framework provides a holistic perspective to support intelligent data center operations.
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