AI-AUGMENTED SECURITY MODELS FOR SOFTWARE DEVELOPMENT: LEVERAGING MACHINE LEARNING FOR THREAT DETECTION AND MITIGATION

The concept of utilizing AI for security began to gain traction in the early 2000s, primarily in anomaly detection and automated threat response systems. However, significant advancements in AI capabilities, driven by increased computational power and access to large datasets, have accelerated the adoption of AI-augmented security models since 2018. Also, the increasing sophistication of cyber threats poses significant challenges to traditional security models, which often lack the adaptability to mitigate evolving risks, has further necessitated the need to reconsider the methods used in threat detection. The research explored the transformative potential of artificial intelligence (AI) in enhancing software security and identified critical limitations in traditional and current AI-based approaches, including scalability, real-time adaptability, and explainability. The Cross-Industry Standard Process for Data Mining (CRISP-DM) framework was used in this study. This methodology comprises six phasesthat can be adapted, and the methodology integrates diverse datasets and tests the model in dynamic software environments. Through a hybrid framework combining traditional rule-based methods with AI-driven models, this study employs supervised and unsupervised machine learning algorithms to improve anomaly detection, zero-day vulnerability identification, and threat response. Key results demonstrate significant improvement in threat detection accuracy and response efficiency compared to existing models. The combination of rule-based filtering and advanced ML algorithms resulted in a 30% increase in the detection of known threats, and the unsupervised models successfully identified several anomalies that were later confirmed as zero-day vulnerabilities, thereby demonstrating the frameworks adaptability. The automated threat response mechanisms reduced the average incident response time by 40%, improving the overall system resilience. Furthermore, the findings underscore the potential of AI to realize proactive and scalable security solutions, thereby addressing gaps in traditional systems while mitigating adversarial risks. This research contributes to software engineering by providing an adaptive security framework, which has implications for developing secure-by-design software and advancing cybersecurity paradigms in an era of increasing technological complexity.