The EU AI Act, along with other emerging global AI regulations, establishes clear requirements for auditing high-risk AI systems such as Large Language Models (LLMs). A robust audit process not only supports regulatory compliance but also mitigates organizational risk and enhances stakeholder trust.

The foundation of any audit process starts with clearly defining the intended use cases, data inputs, and operational constraints of the model. This helps auditors understand the scope and limitations of the evaluation. Model documentation should cover training data provenance, development methodology, model architecture, and any pre-processing or fine-tuning techniques applied.

Comprehensive testing must be conducted to assess fairness, robustness, accuracy, safety, and explainability. Bias audits should test the model across diverse demographic, linguistic, and cultural groups. Robustness testing involves exposing the model to edge cases and adversarial inputs. Accuracy benchmarks should be industry-specific, ranging from medical data interpretation to legal document generation.

Using standardized checklists and well-documented testing protocols promotes consistency across audits. Industry frameworks, such as NIST AI Risk Management Framework and ISO/IEC 23894, provide valuable reference points. Both internal experts and qualified external auditors should be involved to ensure impartiality.

The audit process must also document all findings, including detected risks and applied mitigations. A strong record of corrective actions taken demonstrates proactive risk management and is critical for defending compliance during external regulatory inspections.

Continuous post-deployment monitoring and re-auditing are also essential. LLM performance may degrade over time due to model drift or evolving use cases. Establishing a governance framework that schedules periodic audits and integrates real-world user feedback helps sustain long-term model quality.

A mature auditing program strengthens an organization’s position as a responsible AI provider and reduces the risk of costly penalties or reputational damage from AI failures.