Graph-based security models for AI-driven data storage: A novel approach to protecting classified documents

Zhuwankinyu, Eliel Kundai and Mupa, Munashe Naphtali and Tafirenyika, Sylvester (2025) Graph-based security models for AI-driven data storage: A novel approach to protecting classified documents. World Journal of Advanced Research and Reviews, 26 (2). pp. 1108-1124. ISSN 2581-9615

Corporate data storage systems are susceptible to cyber threats; thus, securing them is a central problem in Artificial Intelligence (AI). Graph-Based Security Models (GBSM) form a reliable and scalable approach to reinforcing cybersecurity. These models help to map out extended cyber threats comprehensively and facilitate enhanced threat identification, anomaly detection, and cryptographic integrity. Special emphasis has to do with integrating AI with GBSM as it enhances real-time anomaly detection, automated threat response, cryptographic computing, and other approaches that make it a helpful solution for the secured handling of classified documents in fluid technological contexts. This work examines the specific problem of how traditional approaches to implementing information security are ineffective against, for example, zero-day exploits and advanced persistent threats. GBSM, therefore, provides more versatile security measures for defence, which are brought about by the constant analysis of relationships between different entities in different threat vectors. Additionally, advanced elements of cryptography key management and decentralized blockchain frameworks add more strength to the protection of identity and valuables, giving the advantage of a nearly unalterable and transparent access control, which are remedies for modern security needs. The proposed study focuses on integrating GBSM and AI to defend distributed systems and cloud environments. It explains how these models allow organizations to map out and recognize threats and address them before they occur in a decentralized environment. Besides, the application of graph-based methods in quantum-safe cryptography and blockchain applications makes it possible to develop protection against novel threats in quantum computing and adversarial actions. By using programs that utilize artificial intelligence, this article explores a progressive outlook on the issue of cybersecurity. Here, he saves a place for the comprehensiveness of future security frameworks enriched by AI, quantum cryptography, and GBSM, which should be suitable for future increased threats. Furthermore, the study recommends that future works to solve the outlined issues must develop adaptive AI models that include post-quantum cryptographic methods for protecting data when faced with new technological threats.