MAIF: Enforcing AI Trust and Provenance with an Artifact-Centric Agentic Paradigm

TL;DR

This paper introduces MAIF, a novel artifact-centric AI paradigm with a new file format that enhances trust, provenance, and security in AI systems, enabling regulatory compliance and secure deployment.

Contribution

It proposes the MAIF format and artifact-centric paradigm, transforming AI data management to improve trustworthiness, provenance tracking, and security at the architecture level.

Findings

Achieves ultra-high-speed streaming of 2,720.7 MB/s

Provides optimized video processing at 1,342 MB/s

Implements cryptographic provenance and granular access controls

Abstract

The AI trustworthiness crisis threatens to derail the artificial intelligence revolution, with regulatory barriers, security vulnerabilities, and accountability gaps preventing deployment in critical domains. Current AI systems operate on opaque data structures that lack the audit trails, provenance tracking, or explainability required by emerging regulations like the EU AI Act. We propose an artifact-centric AI agent paradigm where behavior is driven by persistent, verifiable data artifacts rather than ephemeral tasks, solving the trustworthiness problem at the data architecture level. Central to this approach is the Multimodal Artifact File Format (MAIF), an AI-native container embedding semantic representations, cryptographic provenance, and granular access controls. MAIF transforms data from passive storage into active trust enforcement, making every AI operation inherently auditable. Our production-ready implementation demonstrates ultra-high-speed streaming (2,720.7 MB/s), optimized video processing (1,342 MB/s), and enterprise-grade security. Novel algorithms for cross-modal attention, semantic compression, and cryptographic binding achieve up to 225 compression while maintaining semantic fidelity. Advanced security features include stream-level access control, real-time tamper detection, and behavioral anomaly analysis with minimal overhead. This approach directly addresses the regulatory, security, and accountability challenges preventing AI deployment in sensitive domains, offering a viable path toward trustworthy AI systems at scale.