AI-Augmented Secure Software Engineering: Leveraging Deep Learning for Autonomous Threat Detection and Mitigation

Innovative approaches are needed to deliver secure software, as software systems grow in complexity and cyber threats become more sophisticated. Most of the traditional security mechanisms cannot keep track of dynamic attacks, exposing vulnerabilities that a hacker can use. Deep learning models of artificial intelligence (AI) provide an effective solution through their autonomy in the detection and mitigation of threats. In this paper, we present an approach to AI-augmented secure software engineering, demonstrating the ways in which deep learning techniques can augment security practices in each stage of the software development lifecycle. First, we focus on the constraints of traditional security architectures based on static rulebased systems and human decision-making power. Next, we discuss AI-based approaches like DNNs/CNNs/RNNs to support real-time anomaly detection, code vulnerability examinations, and intelligent threat predictions. By analyzing large volumes of data and identifying patterns that may suggest cyber threats, these models can respond to security incidents automatically, minimizing human effort and increasing accuracy in detection. Additionally, it highlights how deep learning is being leveraged in the DevSecOps pipeline to proactively enforce security through continuous monitoring and automated patching. We examine practical instances among which are adversarial learning methods for malware detection, reinforcement learningbased techniques for intrusion prevention, and large volume threat intelligence collection and analysis. While AI has the potential to bring about incredible change, ongoing challenges in areas like model explainability, adversarial AI, and ethical and regulatory concerns must be addressed. Through the use of AI-based technologies, software engineering can evolve from passive (reactive) security paradigms to self-adaptive (proactive) architectures that bolster robustness against cyberattacks. Through this work, we advance the state-of-the-art in AIbased security systems by introducing a framework that can robustly cross-focus on safety aspects, highlighting the commonalities between deep learning approaches and secure software engineering that can aid in building stronger, self-governing countermeasures.