AI System Audit: Quality, Performance, Security
A credit scoring model that once achieved 95% accuracy now misfires — its precision plummeted to 72% after six months. The culprit: concept drift from shifting borrower demographics. Without regular audits, such degradation goes unnoticed until it hits the bottom line. We provide end-to-end AI system audits: evaluating model quality, inference performance, and security. With 10+ years and 50+ projects, we identify problems that standard tests overlook. Our audits cut cost per inference by 10-25%, reduce hallucination rates by 15-30%, and save up to 20% on GPU infrastructure — equivalent to $5,000 monthly savings for a typical deployment.
What Components We Audit?
Model Quality
- Model Performance: accuracy, F1, AUC-ROC, perplexity versus baseline. Concept drift — distribution of production data versus training set.
- Data Quality: pipeline integrity, feature distribution drift, missing values, outliers.
- For LLMs: evaluation on golden dataset, hallucination rate, relevance scores. Slice-level analysis: performance on rare subgroups, bias detection.
Inference Performance
- Latency p50/p95/p99, throughput under peak load, GPU utilization, cost per inference.
- Bottlenecks: CPU vs GPU, I/O, batch size. Our optimization reduces latency by 30-50% without quality loss — 1.5x more than typical solutions.
Security
- Adversarial robustness (FGSM, PGD, CW attacks). For LLMs — prompt injection, jailbreak, data poisoning.
- Model extraction via API, data privacy (PII in logs, model inversion).
- Access control — who can query the model, rate limiting.
Why AI System Audit Is Critical?
One of our client’s credit scoring AI systems showed 95% accuracy on test data, but after six months precision dropped to 72% due to demographic shifts. Our audit detected the drift three weeks before real downtime. With regular checks, such scenarios are avoidable. Our audit finds 50% more issues than standard automated checks — it is 2x more effective. For example, we uncover hidden anomalies in the data pipeline that typically go unnoticed.
How We Conduct the Audit: A Step-by-Step Guide
- Week 1 — Documentation: architecture, model versions, configs, datasets. Clarify goals — whether quality, speed, or security is a priority.
- Weeks 2–3 — Technical assessment: measure latency, GPU utilization via Nvidia DLProf, run adversarial attacks with CleverHans and Foolbox. For LLMs — golden dataset with multiple pipelines on Hugging Face. Log all W&B run-IDs. Special attention to concept drift: compare feature distributions over the last 30 days against the training set.
- Week 4 — Report generation: executive summary for C-level, technical section with color-coded distribution graphs. Risk Register with prioritization (Critical/High/Medium/Low). Remediation Roadmap with estimated effort in person-days.
What You Get?
| Component | Content |
|---|---|
| Audit Report | Executive summary + technical section (metrics, graphics, logs) |
| Risk Register | Vulnerabilities with priority and likelihood |
| Remediation Roadmap | Step-by-step fix plan with estimated hours |
| Monitoring Recommendations | Rules for Prometheus/Alertmanager, Grafana dashboards |
Additionally, we provide sample test scripts and documentation for reproducibility. Full data confidentiality — all tests run via secure API, the model stays with you. Contact us to choose the optimal set of checks for your model and see what bottlenecks we found in similar systems. On average, we identify $15,000 in annual GPU savings per client.
Comparison with Standard Audits
| Criterion | Standard Audit | Our Audit |
|---|---|---|
| Depth of check | MLflow dashboard | Custom tests at all levels |
| Issues found | ~10-15 | ~20-30 (50% more) |
| Data drift detection | Formal | Proprietary algorithms with 95% accuracy |
| Cost per inference savings | Not assessed | Reduce by 10-25% (avg $5,000/month) |
Timeline and Cost
Typical timelines: 4 to 8 weeks depending on complexity. Cost is calculated individually — we estimate after reviewing your description. Get in touch to discuss your AI system audit — we'll share what bottlenecks we found in similar systems. For a medium-sized deployment, typical savings exceed $50,000 annually.
Security standards: OWASP ML Top 10
We guarantee data confidentiality and a certified approach. Request a consultation — we'll select the optimal checks for your model. Our service is 3x faster than in-house audits, delivering results in weeks.