When Automation Passes but Configuration Fails: Auditing AI-Generated Cloud Runbooks
DOI:
https://doi.org/10.66687/hvv67b77Keywords:
Artificial Intelligence, Cloud Computing, Information Storage and Retrieval, Algorithms, Computer SecurityAbstract
Cloud runbooks can report successful execution even when their final storage, identity or rollback state violates operating policy. The objective is to develop checks that distinguish a successful command sequence from a correct and recoverable infrastructure state. Relevant methodological and data-quality literature informs the design of the sampling and validation strategy [3–7]. The proposed method is a software-assurance experiment using mutated runbooks, controlled API faults and machine-readable postconditions. Comparators will include human-authored scripts, assistant-generated scripts and scripts with policy verification gates. Related enterprise-storage studies inform the assessment of operational reliability [1] and the deployment assumptions adopted for system-level comparison [2]. The design prioritises precise operational definitions and a straightforward baseline before introducing more complex alternatives. Statistical and computational sources help define the reproducibility checks and comparison procedures [8–12]. The primary outcomes are configuration violations, undetected partial failures, idempotence and time spent diagnosing recovery. Potential biases include privilege boundaries, rollback safety, configuration drift and reviewer-blinded assessment. Primary analyses would be reported by the main deployment or participant subgroup so that averaged results do not conceal systematic failures. Additional research informs the prespecified stress tests and limits on interpretation [13–17]. The intended contribution is an auditable research protocol rather than an unsupported claim of effectiveness; related work frames its transferability limits [18–23].
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