INTEGRATION OF GENERAL ARTIFICIAL INTELLIGENCE SAFETY ASSESSMENT METHODS INTO A TARGETED SOFTWARE DEVELOPMENT RISK MANAGEMENT MODEL

Abstract

The subject of this article is the comprehensive study and development of approaches to integrating General Artificial Intelligence safety assessment methods into the risk management process in software development. Particular attention is paid to analyzing potential threats that arise when using Artificial General Intelligence in modern software products and examining methods for minimizing these risks. The article presents practical solutions, including the use of the Analytical Hierarchy Process to prioritize risks and genetic algorithms to find optimal risk mitigation strategies. The main principles of Analytical Hierarchy Process application in the context of software risk management are described in detail, including the construction of hierarchies, risk assessment, and priority matrix formation. Genetic algorithms are used to identify optimal solutions based on Analytical Hierarchy Process data, providing adaptability and precision in creating secure software. Calculation examples demonstrate how combining these methods contributes to more effective risk management. The aim of the article is to develop tools for assessing and managing risks associated with the use of Artificial General Intelligence in software development processes and to ensure the safety of these processes by integrating advanced algorithmic solutions. Conclusions. The integration of the Analytical Hierarchy Process and genetic algorithms enables the creation of an effective risk management model in software development involving Artificial General Intelligence, minimizing threats and ensuring high system reliability.