"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systems

2025-10-30

AI Summary

The widespread deployment of AI systems in high-stakes domains raises critical challenges for trustworthiness and regulatory compliance auditing. Regulations—such as the EU AI Act—mandate high verifiability, yet conventional software auditing techniques are ill-suited to AI’s “black-box” nature, and transparency requirements fundamentally conflict with model and data privacy preservation. Method: This paper introduces ZKMLOps, the first framework to systematically integrate zero-knowledge proofs (ZKPs) into the end-to-end MLOps pipeline, enabling modular, reusable cryptographic compliance verification. Contribution/Results: ZKMLOps supports auditable AI behavior without exposing sensitive models or data. We empirically validate it in a financial risk auditing use case, benchmarking mainstream ZKP protocols and quantifying efficiency trade-offs across ML model complexities. Our work establishes a novel paradigm for trustworthy AI governance that simultaneously ensures accountability and privacy protection.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Machine Learning: Efficient ML / Green AI
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

The increasing exploitation of Artificial Intelligence (AI) enabled systems in critical domains has made trustworthiness concerns a paramount showstopper, requiring verifiable accountability, often by regulation (e.g., the EU AI Act). Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the mechanisms used to achieve this. However, these methods are either expensive or heavily manual and ill-suited for the opaque, "black box" nature of most AI models. An intractable conflict emerges: high auditability and verifiability are required by law, but such transparency conflicts with the need to protect assets being audited-e.g., confidential data and proprietary models-leading to weakened accountability. To address this challenge, this paper introduces ZKMLOps, a novel MLOps verification framework that operationalizes Zero-Knowledge Proofs (ZKPs)-cryptographic protocols allowing a prover to convince a verifier that a statement is true without revealing additional information-within Machine-Learning Operations lifecycles. By integrating ZKPs with established software engineering patterns, ZKMLOps provides a modular and repeatable process for generating verifiable cryptographic proof of compliance. We evaluate the framework's practicality through a study of regulatory compliance in financial risk auditing and assess feasibility through an empirical evaluation of top ZKP protocols, analyzing performance trade-offs for ML models of increasing complexity.

Problem

• Resolving AI system audit conflicts between transparency and confidentiality requirements
• Providing verifiable compliance proofs without revealing proprietary model details
• Addressing limitations of traditional verification methods for black-box AI models

Innovation

• Uses Zero-Knowledge Proofs for compliance verification
• Integrates cryptographic proofs with MLOps lifecycle
• Provides modular process for generating audit proofs