Machine-Reasoned Assurance for Self-Governing Data Centers
Keywords:
Intelligent Data Center; AI-Driven Risk Management; Compliance Governance; Risk Owners; Assurance Committee.Abstract
The future success of data center operations will increasingly hinge on how effectively automation-specific risks are managed. While data center management platforms already collect and process vast volumes of data generated by equipment and automation scripts, risk management capabilities within these platforms remain underdeveloped. Organizations need a governance structure capable of identifying, monitoring, and mitigating risks tied to automated and AI-driven processes — one that brings together data sources, methodologies, alerting mechanisms, and clearly assigned responsibilities. Establishing such a structure begins with a clear mandate, underpinned by a defined risk taxonomy and a risk appetite calibrated to broader organizational objectives. Governance outcomes can then be aligned with existing regulatory requirements.
Sustaining this governance model requires continuous, AI-enabled risk identification and assessment. This starts with identifying relevant data sources — whether third-party services or internal models — and automating feature selection. From there, organizations must ensure both solution quality and quality of service, detect significant deviations from established behavior, and continuously monitor data quality. Together, these capabilities enable ongoing risk identification and assessment that translates into continuous, tailored, and data-driven remediation recommendations.
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Copyright (c) 2024 Dimitris Plexousakis (Author)
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