Analyzing Cloud Reliability Metrics and Their Influence on Business Continuity: The Role of Autonomous AI Systems
Keywords:
Cloud Reliability Metrics, Enterprise Risk Management, AI Transparency, Operational Resilience, Service Level Objectives (SLOs), Grey Failures, Predictive Recovery, Self-Healing Infrastructure, Autonomous AI Systems, Business Continuity PlanningAbstract
This article examines the important relationship between cloud dependability measurements and modern business continuity, focusing on how autonomous artificial intelligence technologies are changing the game. In order to determine whether or not traditional uptime requirements, such as the five nines, are effective in protecting a company from complicated and partial system failures, our first purpose was to conduct an evaluation. Utilizing secondary data, we were able to compile a decade's worth of research, white papers, and technical reports from major cloud providers that had been subjected to peer review. On the other hand, autonomous artificial intelligence may be able to recognize problems before they signal, thereby lowering recovery times from minutes to milliseconds. Traditional measurements disregard grey failures, which produce enormous volumes of operational friction. On the other hand, our findings indicate that there is a large trust gap with regard to the decision-making process of the AI. As a result, we propose policy implications that move toward standardized AI transparency logs. This will ensure that actions taken by autonomous self-healing systems continue to be auditable and linked with enterprise-level risk management and legal compliance frameworks.
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