Kernel-Embedded Blockchain Architecture for Transparent AI Decision Auditing

Abstract

Modern operating systems increasingly rely on AI for critical functions like resource allocation and user interaction optimization yet lack mechanisms to ensure transparent, auditable decision-making. Current logging systems, vulnerable to tampering and manual audits fail to meet regulatory demands in sectors like healthcare for example Aidoc’s aiOS faces scrutiny over untraceable diagnostic suggestions. Existing blockchain-AI integrations operate at application layers introducing latency (2–5 minutes/transaction) and storage inefficiencies (60–70% capacity use). This work proposes a kernel-embedded blockchain architecture that immutably logs AI decisions at the OS level combining Merkle tree hashing with hybrid Proof-of-Stake consensus. Empirical tests across 1,000 tamper scenarios demonstrated 100% detection accuracy with a median transaction latency of 57 seconds and 95% storage efficiency-outperforming traditional systems by 35% in audit readiness. The framework processes 10,000+ daily AI decisions in enterprise simulations, reducing audit preparation time from 120+ hours to real-time verification. While addressing critical gaps in GDPR/HIPAA compliance and bias mitigation (98% accuracy in identifying skewed training data) challenges remain in scaling consensus mechanisms for sub-10-second latency. This architecture establishes a foundational model for trustworthy AI-integrated operating systems enabling regulatory compliance without sacrificing performance and paves the way for future work in energy-efficient decentralized validation.