AI-driven cybersecurity framework for software development based on the ANN-ISM paradigm - Scientific Reports
Summary
AI is critical in fighting phishing and social engineering defense via natural language processing (NLP) and machine learning (ML) methods by which message contents are evaluated, while URL and sender credibility are checked15. Lastly, AI technologies also aid organizations in vulnerability management by creating risk ratings for prioritized assessments, facilitating targeted remediation actions, and leveraging predictive models to anticipate potential exploits by cybercriminals20. NIST deals with risk management and setting up controls, and CMMI covers the process maturity to a large extent without fully incorporating predictive technology (CTIS) and continuous learning of security infrastructure. The research questions formulated in this study are intended to deepen the understanding of these critical issues: What are the potential vulnerabilities and cybersecurity risks identified through a literature review and in real-world industry experiences that need to be addressed for secure software coding? The hybrid ANN-ISM model’s effectiveness stems from the synergy between the ANN’s ability to predict cybersecurity risks based on large datasets and ISM’s capacity to reveal the structural relationships between secure software coding.