Secure AI: A Comprehensive Review on Security and Privacy Challenges and the Potential of Decentralized Approaches

Abstract

Background: Artificial Intelligence (AI) is transforming diverse sectors, yet its rapid deployment brings significant concerns regarding data security and user privacy. Centralized AI architectures, which rely on aggregating data into singular locations, are inherently vulnerable to breaches, surveillance, and manipulation. These limitations call for a shift toward more secure, privacy-preserving alternatives.

Objective: This review explores the major security and privacy challenges inherent in current AI systems and evaluates the potential of decentralized approaches, such as Federated Learning, Decentralized AI, and Zero-Knowledge Proofs, to address these limitations effectively.

Scope: The paper surveys key AI vulnerabilities, including data leakage, adversarial attacks, and trust issues in centralized models. It then discusses how decentralized architectures can improve resilience, confidentiality, and trustworthiness in AI systems. Special attention is given to privacy-preserving computation, distributed trust models, and cryptographic enhancements.

Findings: Decentralized AI approaches show strong potential to mitigate risks without compromising performance. Federated Learning enables collaborative model training without raw data sharing. Decentralized AI systems, often built on blockchain or peer-to-peer networks, eliminate single points of failure. Zero-Knowledge Proofs and similar cryptographic tools add verifiability without compromising sensitive data. However, practical adoption faces obstacles in terms of communication overhead, scalability, and interoperability.

Conclusion: Decentralized approaches represent a foundational shift in designing secure AI systems. While they are not a panacea, they provide powerful tools to build AI architectures that are robust, transparent, and privacy-aware. Future work should focus on enhancing scalability, standardizing protocols, and integrating multiple privacy-preserving techniques for real-world deployment