Development of Reliable Access Control Mechanisms Using Artificial Intelligence for Corporate Data Protection

The study aims to analyse the vulnerabilities of traditional access control methods and define optimization objectives, constraints, and decision-making processes based on data for the effective implementation of artificial intelligence to enhance corporate data protection. The research methodology addressed various approaches, including machine learning, user behaviour analysis and neural networks, and data protection methods such as anonymisation, encryption and federated learning. Traditional access control methods, such as passwords, biometrics and multi-factor authentication, were discussed, as well as their shortcomings, including vulnerability to data breaches, phishing attacks and infrastructure threats. The use of artificial intelligence to strengthen access control mechanisms, such as machine learning, user behaviour analysis and neural networks, was emphasised. Artificial intelligence significantly improves security by enabling the analysis and processing of large amounts of data, detecting anomalies and predicting threats based on the analysis of user behaviour and biometric data. The study also examined methods of protecting data used to train artificial intelligence, including anonymisation, differential privacy, encryption and federated learning. Privacy issues the risks of data leakage when using artificial intelligence and the need to comply with ethical norms and standards were addressed. The successful integration of AI-oriented solutions into corporate security systems in various industries, including the financial sector, healthcare, and retail, is presented. Evaluating the effectiveness of artificial intelligence in access control systems is based on indicators such as the speed of the system’s response to changes in user behaviour, the number of false positives and successfully prevented incidents. The study also developed recommendations for improving access control mechanisms using artificial intelligence, including the introduction of machine learning-based systems to detect anomalies in user behaviour, and the integration of AI with multi-factor authentication to create flexible and reliable data protection mechanisms.