Enterprise AI Storage Security: A Comprehensive Framework for Secure AI Data Management
Prabu Arjunan · Journal of Artificial Intelligence Machine Learning and Data Science · 2023
The rapid adoption of artificial intelligence in enterprise environments has ushered in unparalleled challenges regarding the security of data storage.This paper presents a comprehensive framework for securing AI storage systems, addressing the unique requirements of machine learning models, training data and inference results.We propose a multilayered security architecture that ensures data integrity, confidentiality and availability while meeting the performance demands of AI workloads.The proposed framework leverages the latest encryption methodologies, state-of-the-art access control mechanisms and the integration of real-time threat detection systems tailored for an AI-driven context.Experimental results prove that the approach secures 99.99% assurance while keeping system performance within 5% of baseline measurements.