Annotation: The rapid development of digital technologies has significantly increased the importance of ensuring data security. In this context, the study of the interaction between cryptography and artificial intelligence has become one of the most relevant research areas. This article examines the opportunities, risks, and future prospects of applying artificial intelligence technologies in cryptographic systems. The main objective of the study is to perform a comparative analysis of the efficiency of traditional cryptographic algorithms and algorithms optimized using artificial intelligence techniques. The research is based on experimental modeling, during which the encryption time for different data volumes was measured. The obtained results demonstrate that AI-optimized algorithms provide higher performance compared to traditional methods. In addition, artificial intelligence can automate the process of identifying vulnerabilities in cryptographic systems and improve the adaptability of security mechanisms. The findings indicate that the integration of cryptography and artificial intelligence has significant potential for enhancing modern information security systems.

Annotation: Today, access control systems that rely only on passwords and PIN codes can no longer keep up with growing security requirements. These traditional methods are easy to steal, guess, or share with someone else, which turns them into the weakest link in the protection chain. In this context, solutions that use biometrics and can also “understand” user behavior, rather than just verify entered data, are becoming especially relevant. This study develops a software system that makes decisions not only based on a person’s biometric features, but also on how they behave when trying to log in. The aim is to obtain a tool that can detect suspicious activity in time and automatically strengthen access restrictions when necessary. As a technical foundation, computer vision and deep learning methods are used to recognize faces in a video stream. In addition, a behavioral analysis module is implemented that tracks the sequence and frequency of login attempts, as well as the time and context of access. The system is structured into several interconnected modules: face recognition, time- and zone-based access verification, and risk level assessment. The prototype shows that combining biometric and behavioral features can significantly improve the quality of access control. When the number of errors or atypical actions exceeds a predefined threshold, the system automatically blocks the user and triggers an alarm scenario. In this way, the probability of unauthorized access is reduced, while part of the routine work of the security administrator is offloaded to the system itself. The scientific novelty of the proposed approach lies in integrating face recognition and behavioral analysis not as two separate layers of protection, but as components of a single decision-making architecture. This makes it possible to consider both the user’s biometric characteristics and the context of their actions-such as time, location, and patterns of failed attemptssimultaneously when deciding whether to grant access. Compared to classical single-factor solutions, such an integrated approach forms a more flexible and intelligent security perimeter

Annotation: In arid regions, food security is constrained by land degradation, unstable water resources, and localized environmental pollution, which reduce the resilience of agri-food systems and negatively affect product quality and processing potential. The purpose of the study was to assess the impact of environmental factors on food security in the Zhambyl region and to substantiate the role of food technologies in improving product quality and safety. The study applies a systems approach, comparative geographical analysis, and environmental monitoring data on soils, air, and surface waters for 2023-2024, followed by scientific interpretation of the results. Land degradation, including humus decline and deflation processes, is identified as the main limiting factor for agricultural productivity. Heavy metal concentrations in soils remain within permissible limits. Air quality is generally characterized by low pollution levels, although local exceedances of maximum allowable concentrations are observed. Surface water quality shows a positive trend with no cases of extreme pollution. The findings emphasize the importance of implementing resource-efficient and treatment technologies and can be used in developing regional food security policies.