Cybersecurity risk management strategy for AI and SaaS platforms: A NIST framework approach

Abstract

The rapid growth in Information Technology (IT) and Software industries particularly within Artificial Intelligence (AI) and Software-as-a-Service (SaaS) has accelerated the pace of the fourth industrial innovation, and at the same time, this growth produced complex vulnerabilities to security. The conventional defense mechanisms are becoming less effective over time against the evolving threats like adversarial data poisoning, API exploits and advanced ransomware attacks targeting cloud infrastructures. The primary goals of this paper are to address these issues by developing a comprehensive risk management plan that is based on the NIST Cybersecurity Framework (CSF). Additionally, this study identifies critical vulnerabilities in modern AI and SaaS environments using a qualitative risk assessment approach and a likelihood-versus-impact matrix. The analysis shows that data breaches and API exploitation are the most serious threats, which have significant impact on organization operations and the high likelihood. Moreover, the findings indicate that incorporating the NIST CSF core capabilities such as Identify, Protect, Detect, Respond and Recover is a well-organized framework of minimizing these high-priority threats using layered preventive and detective controls. Ultimately, the results highlight how important it is to embrace standards-based systems to shift organizations from reactive security measures to proactive resilience to ensure the integrity and continuity of the interconnected software ecosystem.