A system administrator uses a public large language model (LLM) to generate a complex Bash script intended to audit user permissions and automatically remediate non-compliant configurations on a production server. The administrator notes the script appears logical and well-commented. According to responsible AI best practices, what is the most critical action the administrator must take before executing the script?
Immediately run the script on the production server to quickly address compliance issues.
Add the script to the company's version control system and document its purpose.
Thoroughly test the script in an isolated, non-production environment that mirrors the production setup.
Refine the AI prompt to ask for a more optimized or efficient version of the script.
The correct answer is to thoroughly test the script in an isolated, non-production environment. AI-generated code, especially from public models, can contain subtle errors, security vulnerabilities, or logic flaws that are not immediately apparent. Executing an untested script with remediation capabilities on a production server poses a significant risk of causing unintended system damage, data loss, or security breaches. While documenting the script and using version control is a good practice, it is not the most critical step before ensuring the script is safe and effective. Refining the prompt is part of the generation process, but it does not replace the need for rigorous verification of the final output. Running the script immediately on a production server is reckless and contrary to all IT best practices. The most critical, immediate action is always to review, qualify, and test.
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Why is it critical to test scripts in an isolated environment before running them in production?
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What are common risks involved in running untested AI-generated scripts on production servers?
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How can a system administrator effectively mirror the production environment for safe testing?
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CompTIA Linux+ XK0-006 (V8)
Automation, Orchestration, and Scripting
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