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

arXiv: https://arxiv.org/abs/2510.26576 · HTML: https://mlsanity.com/paper/2510.26576/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2025.

## Built on by (most cited first, 6 of 6)

- [Computational Compliance for AI Regulation: Blueprint for a New Research Domain](https://mlsanity.com/paper/2601.04474/) (2026)
- [Verifier-Bound Communication for LLM Agents: Certified Bounds on Covert Signaling](https://mlsanity.com/paper/2603.00381/) (2026)
- [RCTs for Frontier AI Governance: Methodological Challenges and Solutions for Human Uplift Studies](https://mlsanity.com/paper/2603.11001/) (2026)
- [QML-PipeGuard: Drift-Aware Behavioral Fingerprinting for Quantum Machine Learning Pipeline Integrity](https://mlsanity.com/paper/2605.25066/) (2026)
- [ZK-SR117: A Chunked Zero-Knowledge Attestation Design for Aggregated Fair-Lending Metrics, with a Control Mapping toward Full SR 11-7 Coverage](https://mlsanity.com/paper/2608.02664/) (2026)
- [When Agentic Trust Crosses Organizational Boundaries: Structural Externalization and a Reference Model for Trust Evidence](https://mlsanity.com/paper/2609.22961/) (2026)

## Abstract (arXiv)

Classical software verification and validation techniques, such as procedural audits, formal methods, or model documentation, are the traditional mechanisms used to achieve the verifiable accountability now required by regulations like the EU AI Act. These methods are either expensive or heavily manual, and ill-suited for the opaque, "black box" nature of most Artificial Intelligence (AI) models. A conflict arises: high auditability and verifiability are required by law, but such transparency conflicts with the need to protect the assets being audited (e.g., confidential data and proprietary models). This paper introduces ZKMLOps, an acMLOps verification framework that operationalizes Zero-Knowledge Proofs (ZKPs) within Machine-Learning Operations lifecycles; a ZKP allows a prover to convince a verifier that a statement is true without revealing any information about the statement itself. By integrating ZKP with established software engineering patterns, ZKMLOps provides a modular and repeatable process for generating verifiable cryptographic evidence-proofs of well-defined computational statements about the audited model and its inputs-that auditors can use as input to a regulatory compliance determination. We evaluate the framework along two dimensions. First, framework viability: orchestration overhead is bounded and stable across architecturally heterogeneous ZKP backends and models of increasing size. Second, cost-versus-assurance trade-offs: the audit-on-demand setting is the regime in which full zero-knowledge auditing is the appropriate tool, where it provides confidentiality and integrity guarantees that lighter-weight alternatives cannot match.