INTELLIGENT RISK ASSESSMENT FRAMEWORK FOR SOFTWARE SECURITY COMPLIANCE USING AI

Abstract

As software systems become increasingly integral to critical infrastructure, ensuring their security is crucial. Traditional security measures can be reactive and resource-intensive, prompting the need for more efficient solutions. This paper introduces the "Intelligent Risk Assessment Framework for Software Security Compliance Using AI," which combines AI-driven risk assessment with actionable compliance recommendations. The framework utilizes a deep learning model to evaluate security risks in real-time, based on a dataset of software attributes and historical vulnerabilities. The model achieved an accuracy of 92.5%, with an AUC-ROC score of 0.95, indicating strong predictive capability.

In addition to accurate risk prediction, the framework includes a rule-based system that offers practical compliance measures, such as access control improvements and secure coding practices. The system significantly reduces the time required for risk identification from three days to one day and increases resource utilization efficiency from 65% to 85%. The proposed framework provides a comprehensive approach to software security, integrating advanced AI techniques with practical compliance strategies. Future work could focus on integrating real-time threat intelligence and developing specialized compliance modules for various industries.