RISK MANAGEMENT SOFTWARE BASED ON AI AND CPS PREDICTION
RISK MANAGEMENT SOFTWARE BASED ON AI AND CPS PREDICTION

Summary/Abstract: This paper focuses on the use of machine learning and the use of dedicated AI databases to create solutions based on an improved algorithm for preventive risk management, and real-time risk prediction. The paper analyses the existing standard, its shortcomings and solutions for improvement, and the structure and algorithmic basis of these systems, as well as their integration into existing security architectures and platforms. The work includes the detection of threats based on anomalies and the analysis of established user behavior according to given patterns, risk assessment and proactive detection of attacks. Timely identification and management of risks are becoming key factors in corporate sustainability and security of business and information systems. Predictive analytics, based on artificial intelligence, machine learning and big data analytics, bring transformational opportunities in areas such as industry, finance, healthcare, which in the modern era are connected by cybersecurity and risk prediction that help decision makers to manage systems more efficiently and protect them. An integrative approach to harmonizing these technologies, especially considering the organizational structure and legal framework, includes issues of reliability and transparency of models, as well as accountability for automated decisions, privacy protection and compliance with legislation. The aim of the paper is to provide a comprehensive overview of technological and methodological innovations in predictive protection against cyber risks, and to identify directions for future development with a special focus on the security, ethics and reliability of AI systems.

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