LLM-FACETS: A Privacy-Preserving Framework for Evaluating LLM Transparency and Accountability

Researchers introduce LLM-FACETS, an open-source framework designed to make LLM auditing accessible to non-technical practitioners while preserving data privacy. The system addresses regulatory compliance needs outlined in the EU AI Act and NIST frameworks by providing browser-based evaluation tools that keep sensitive data on self-hosted servers rather than transmitting it to external services.

LLM-FACETS addresses a critical gap in AI governance infrastructure: most existing LLM evaluation tools require specialized technical expertise and force users to upload proprietary data to cloud platforms, creating friction for compliance officers and domain experts tasked with AI oversight. This framework democratizes access to LLM auditing by offering a browser interface and modular plugin architecture, eliminating both the technical barrier to entry and the data privacy concerns that plague current solutions.

The problem reflects broader industry tension between transparency demands and operational constraints. Regulators increasingly expect organizations to demonstrate that deployed LLMs are factually grounded and reproducible, but existing audit tools either lock expertise behind programming requirements or compromise data sovereignty. The framework's explicit data-flow architecture—keeping deterministic metrics local while making external API calls transparent and credential-controlled—signals growing maturity in how the AI industry handles privacy-compliance tradeoffs.

For organizations managing AI risk, the release strengthens the practical toolkit available for compliance documentation. The multi-judge consensus approach and RAG Triad metrics specifically target hallucination detection, a persistent concern for enterprise deployments. The cross-validation of 18 metric implementations against reference libraries establishes credibility for practitioners skeptical of single-metric assessments.

The open-source approach decouples accountability from vendor lock-in, enabling independent verification of AI system quality across teams and organizations. As regulatory scrutiny intensifies globally, tools that make compliance accessible and data-preserving become infrastructure investments rather than optional overhead.

• →LLM-FACETS enables non-technical practitioners to audit LLMs without programming expertise or cloud data transmission risks.
• →The framework operationalizes EU AI Act and NIST compliance requirements through three stakeholder profiles reflecting actual governance roles.
• →Token-level log-probability visualization and multi-judge consensus directly address hallucination detection and reproducibility concerns.
• →Plugin architecture allows extensibility without modifying core evaluation pipelines, supporting long-term framework evolution.
• →Open-source design with verified metric implementations reduces organizational dependence on vendor-specific audit tools for compliance.