Technical AI Audit

A technical audit examines the inner workings of an AI system: model performance, data quality, security posture, infrastructure reliability, and the development pipeline that produced the system.

Model Performance Audit

1. Reproduce Reported Metrics

   Independently verify the performance metrics claimed by the development team. Use the same test data and evaluation methodology. Discrepancies may indicate data leakage, overfitting, or reporting errors.

2. Evaluate on Additional Test Sets

   Test the model on held-out data not used during development. Include edge cases, adversarial examples, and data representing deployment conditions that may differ from training conditions.

3. Assess Robustness

   Test model behavior under input perturbations, distribution shifts, and adversarial conditions. A model that performs well on clean data but fails under realistic variations is a significant risk.

4. Check for Concept Drift

   Compare model performance on recent data versus training-era data. Measure feature distributions over time. Flag significant drift that may indicate the model's decision boundaries are no longer aligned with reality.

Data Quality Audit

Security Audit

• Access controls: Verify that model endpoints, training data, and model artifacts are protected by appropriate authentication and authorization
• Input validation: Test that the system properly validates and sanitizes inputs. Check for adversarial input handling and injection resistance
• Model confidentiality: Assess whether model internals (weights, architecture, training data) can be extracted through API access or side channels
• Data protection: Verify encryption at rest and in transit, access logging, and data retention compliance
• Supply chain: Audit third-party components including pretrained models, libraries, and data sources for known vulnerabilities and trustworthiness

Infrastructure Audit