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Aurora Analytics, a cutting-edge firm specializing in predictive maintenance solutions for the aerospace industry, is implementing ISO 42001:2023. They\'ve developed an AI-powered system called \"Skynet\" (not related to any AI Armageddon) to forecast potential engine failures in commercial aircraft. This system has undergone rigorous testing and is now entering its deployment phase. However, the lead data scientist, Dr. Anya Sharma, is concerned about the long-term maintainability and adaptability of Skynet, especially considering the rapid advancements in AI technology and the evolving regulatory landscape. The company\'s current focus is heavily weighted towards immediate performance gains and cost reduction, with less emphasis on comprehensive lifecycle management.\n\nGiven this scenario, which of the following aspects of AI Lifecycle Management, as defined by ISO 42001:2023, should Aurora Analytics prioritize to ensure the long-term success and responsible operation of Skynet?
Establishing robust documentation and traceability practices across all stages of Skynet's lifecycle, coupled with a proactive change management framework to address future updates, modifications, and eventual retirement, ensuring continuous monitoring and adaptation to evolving industry standards and regulations.
Focusing primarily on optimizing Skynet's performance metrics (e.g., prediction accuracy, computational efficiency) and minimizing operational costs during the initial deployment phase, while deferring detailed documentation and change management planning until a later stage.
Prioritizing the immediate integration of Skynet into existing maintenance workflows and training all personnel on its usage, without necessarily establishing comprehensive documentation or traceability procedures, as long as the system demonstrates satisfactory performance in the short term.
Concentrating on securing intellectual property rights for Skynet's algorithms and code, and limiting access to its internal workings to a select group of senior engineers, thereby minimizing the risk of unauthorized modifications or reverse engineering, regardless of lifecycle considerations.

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