Improving Access Control in Cloud Environments Using Context Aware Security

In today's digital era, big data systems are now a part of most sectors of industry, helping organizations to make sense of enormous amounts of data for better decision-making. With their growing scale and complexity, however, the security vulnerabilities of these systems grow as well-largely as pertains to access control. This report discusses the common issue of static and ineffective access control in big data systems. Based on the case study of the 2024 Snowflake data breach attack, we illustrate how attackers took advantage of unencrypted credentials and the lack of enforced multi-factor authentication (MFA) to access several client accounts without authorization. Building on this, we propose a dynamic access control solution in the form of the Adaptive Context-Aware Security Framework (ACASF), based on the principles of Zero Trust Architecture. Our framework considers contextual data such as device type, IP address, access time, and geolocation to evaluate risk in real-time. It consists of five major components: context collector, risk engine, policy decision point, policy enforcement point, and audit system. Compared to the traditional RBAC and ABAC models, our proposed framework improves flexibility, security responsiveness, and fine-grained control. We believe this solution more appropriate to today's modern big data platforms and effective in preventing such security breaches in the future.