Securing Metadata in Digital Platforms: Symmetric, Asymmetric, and AI-Based Approaches

Abstract

With the spread of digital platforms in the industries, the security of what is called metadata--data about data-- has become an issue of extreme concern. Metadata, unlike content data, can expose sensitive trends, behaviours, and user relations when the primary data is encrypted. In this paper, three major metadata securing methods have been revealed, and their effectiveness is discussed: symmetric encryption, asymmetric encryption, and new artificial intelligence (AI) approaches. Asymmetric and symmetric cryptographic methods are the basic building blocks of metadata protection with different performances in terms of scale, performance, and key management. However, as the sophistication and depth of digital interactions increase, conventional methods are no longer effective due to their limited adaptability and concept of threat computation. Machine-learning and anomaly-based AI solutions, in general, have dynamic solutions that can detect and prevent metadata breaches in real-time. This paper will contrast the three paradigms in terms of efficiency, resistance to threats, and application in the real-life digital ecosystems. The research results highlight the capabilities of hybrid solutions that integrate cryptographic disciplines with intelligent automation, offering a future-proof approach to metadata security. The paper concludes with a set of recommendations for platform architecture and cybersecurity strategy, aiming to introduce comprehensive metadata protection systems.