AGENTIC AI FRAMEWORKS FOR AUTONOMOUS RISK DETECTION AND COMPLIANCE REMEDIATION IN ENTERPRISE DATA CENTER OPERATIONS

Povzetek

Enterprise data centers are tasked with managing large-scale applications and data for multiple businesses, making them private distributed systems in the cloud computing paradigm. Data center operations are inherently risk-prone, and commissions and omissions often expose the data center and hosting customers to record fines for non-compliance with sector-specific security, privacy, and operational continuity standards. Failing to detect and fix issues in time can also lead to operational infractions that directly affect business continuity, security breaches, data leaks, and a multitude of other service disruptions. Therefore, risk detection and compliance remediation are missions conducted by specialized teams, following formal processes and often using dedicated tools.

Although risk detection and compliance remediation are traditionally viewed as human-centric, autonomous solutions exist and bring associated advantages. Nevertheless, they rarely address standalone data center needs, either operating as unsupported external tools (e.g., vulnerability scanners) or targeting only some types of issues (e.g., anomaly detection, fault diagnosis, etc.). A new class of general-purpose, self-driving data center risk detectors conforms to a sensing-perception-reasoning-design-execution action and feedback loop and, endowed with proper control mechanisms, can automatically contain, remediate, and restore. Such detectors generally require either ad-hoc policy engines for compliance mandates or proactive prevention workflows. In agentic AI frameworks, supervisory activities such as auditing, verifying, and explaining detected incidents also benefit from automation.