AI Evolution and Cybersecurity Challenges: Security Architecture for Trustworthy AI Systems
Abstract: Artificial intelligence (AI) has progressed substantially beyond its initial programmed systems. Contemporary AI systems interpret language, observe patterns, and autonomously make complex decisions. These progressions present new security challenges, as attackers increasingly target not only credentials but also AI models and their associated training data. Compromising AI systems can lead to rapid and wide-ranging consequences. Key risks encompass data poisoning, hostile inputs, prompt injection, model theft, and supply chain attacks. Traditional cloud security measures are inadequate for dealing with these threats. Consequently, a Secure AI Platform Architecture (SAPA) is necessary. SAPA incorporates best practices, including the NIST AI Risk Management Framework and Zero Trust principles, alongside modern supply-chain security, to safeguard AI systems throughout their lifecycle from training to deployment. This paper presents practical examples showing how layered security controls mitigate prompt injection, prevent data leakage, and boost threat detection. Three case studies are analyzed: a telecom private cloud, an IT help desk employing large language models, and security analytics team responses. These scenarios offer implementable strategies for professionals responsible for building or securing AI systems.
Keywords: Artificial Intelligence, AI Security, Cybersecurity, AI Models, Training Data
How to Cite?: Vigneshbabu Jeyaseelan, "AI Evolution and Cybersecurity Challenges: Security Architecture for Trustworthy AI Systems", Volume 15 Issue 8, August 2026, International Journal of Science and Research (IJSR), Pages: 1163-1166, https://www.ijsr.net/getabstract.php?paperid=SR26814162733, DOI: https://dx.doi.org/10.21275/SR26814162733