Sarcouncil Journal of Engineering and Computer Sciences

Abstract: An integrated frame for independent cloud adaptability combines underpinning literacy and graph neural networks to address mounting security challenges in enterprise surroundings. Traditional security approaches with static rules and homegrown response protocols struggle with the scale and complexity of ultramodern attack operations. The frame deploys RL agents across the cloud mound for independent decision-making while exercising GNN-grounded trouble intelligence to model structure as a miscellaneous dynamic graph. This integration transforms security from reactive to visionary operations through environment-apprehensive trouble discovery and rapid-fire remediation. The armature demonstrates better incident resolution times, optimized resource application, and enhanced security posture by continuously conforming to evolving pitfalls and structure changes. Profitable benefits crop from reduced functional outflow, minimized false cons, and accelerated incident response, eventually establishing adaptability as an essential system property rather than a reactive practice.