Meta-Sealing: A Revolutionizing Integrity Assurance Protocol for Transparent, Tamper-Proof, and Trustworthy AI System

However, this growth has also introduced new challenges in ensuring the integrity, traceability, and verifiability of AI systems throughout their lifecycle [1]. As AI increasingly influences critical decision-making processes, the need for robust mechanisms to guarantee the trustworthiness of these systems has become paramount. Traditional approaches to data integrity and system verification fall short when applied to the complex, often opaque nature of AI systems. The dynamic nature of AI models, the vast amounts of data they process, and the intricate relationships between different stages of their lifecycle demand a more comprehensive and AI-specific approach to integrity assurance. This paper introduces Meta-Sealing, a novel integrity protocol designed specifically for AI systems. Meta-Sealing provides a cryptographic framework for sealing and verifying each stage of the AI lifecycle, from data collection to model retirement. By doing so, it addresses critical needs in enterprise AI deployment, including: 1. Ensuring the integrity of training data and model artifacts 2. Creating verifiable audit trails of AI development and deployment processes 3. Enhancing the reproducibility of AI experiments and results 4. Facilitating compliance with emerging AI regulations and governance frameworks 5. Building trust in AI systems among stakeholders and end-users We present a detailed architecture for implementing Meta-Sealing, including core components, cryptographic operations, and integration strategies. Furthermore, we discuss performance optimizations and security considerations crucial for enterprise-grade deployments.