The work analyzes existing approaches to forecasting contract execution, including traditional statistical models and modern methods based on machine learning. A comparative analysis of various machine learning algorithms, such as logistic regression, decision trees, random forest and neural networks, was carried out to identify the most effective forecasting models.An extensive database of information on government contracts was used as initial data, including information about contractors, contract terms, deadlines and other significant factors. A prototype of an intelligent forecasting system was developed, testing was carried out on real data, as well as an assessment of the accuracy and reliability of the resulting forecasts. The results of the study show that the use of machine learning methods can significantly improve the quality of forecasting the execution of government contracts compared to traditional approaches

Keywords: intelligent system, mathematical modeling, government procurement, government contracts, software package, forecasting, machine learning

The article proposes the use of intelligent methods for predicting the reliability of contract execution as a key element of the system for ensuring information security of the critical infrastructure of financial sector organizations. Based on the analysis of historical data and the use of machine learning methods, a comprehensive model for assessing and predicting the risks of failure or poor performance of contracts by suppliers has been developed. It is shown how the use of predictive analytics can improve the efficiency of information security risk management, optimize planning and resource allocation, and make informed decisions when interacting with suppliers of critical services and equipment.

Keywords: intelligent system, predictive analytics, information security, critical infrastructure, financial sector, contract execution, machine learning

The constant growth of cyber attacks on the financial sector requires the construction of a modern protection system based on the use of artificial intelligence or machine learning. The paper provides an analysis of specific products and solutions of the global market based on artificial intelligence technologies that can be used to protect critical information infrastructure.

Keywords: cyber attacks, critical infrastructure, artificial intelligence, information security, machine learning

The paper is devoted to the development of a security concept for the protection of critical information infrastructure of the financial sector. The critical information infrastructure of the financial sector is analyzed, and the main types of cyberattacks are considered in relation to the objects in this area. The concept of security is proposed, including access control, multilevel protection, data encryption, continuous monitoring and other measures. Models of the main threats to the security of information infrastructure objects of the financial sector are given. The question of the importance of cooperation and information exchange between financial institutions, regulators and law enforcement agencies to ensure collective security of the financial sector is raised. The article will be useful for specialists in the field of information security, financial sector and managers of organizations interested in developing and improving the security system of information infrastructure of the enterprise.

Keywords: information security, information infrastructure, financial sector, mathematical modeling, software package