Safe Autonomous Remediation of Cloud Infrastructure Using Large Language Models

  • Authors

    • Uday Allala Lead SRE Engineer, USA. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2020PI2P7V

    Published 01-08-2020

  • Large Language Models, Cloud Infrastructure, Autonomous Remediation, Cloud Security, AIOps, Incident Response, Risk-Aware Automation, Infrastructure as Code, Explainable AI

    Issue

    Vol. 3 No. 1 (2020)

    Section

    Articles

  • Abstract

    The large-scale cloud infrastructures are becoming more complex and dynamic, and the ability to rapidly and reliably remediate operational failures, configuration issues, security breaches, and service downtimes is essential. Traditional remediation strategies are often based on a rule set, a run book or a generic set of operations, often resulting in being unable to handle new conditions, multiple dependent services or changing infrastructure states. This paper suggests a safe autonomous remediation framework, leveraging Large Language Models (LLMs) to analyze cloud observability data, diagnose incidents, identify potential root causes, and produce context-specific remediation plans. It combines information from the cloud monitoring system, operational knowledge, retrieval, reasoning using LLM, and infrastructure management, allowing for automatic decision making while preserving explicit safety constraints. Risk-aware action assessment, policy-based validation, least-privilege execution, pre-execution checks, post-remediation verification, human approval of high-risk operations and automated rollback mechanisms are key components of proposed approach to prevent any inadvertent and possibly harmful autonomous actions. A framework is assessed through representative failure, configuration, availability and security related scenarios for cloud infrastructure, and contrasted with conventional remediation using rules and automation. Evaluation factors include unsafe action rate, rollback effectiveness, human intervention need, remediation latency, diagnosis accuracy, and success rate of remediation. The results illustrate the power of LLM-based remediation to streamline recovery efforts, boost flexibility, and maintain safety and control over infrastructure modifications, all without compromising critical recovery objectives.

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