Operate
Prove your AI is accurate, fair and compliant.
Independent testing, compliance assessments and audit reports that show boards, regulators and customers your AI systems perform as intended and meet their obligations.
Overview
AI Compliance, Testing & Auditing
Saying an AI system is safe is no longer enough. Boards, regulators, insurers and enterprise customers increasingly want evidence: how the system was tested, how it performs for different groups of people, how it handles misuse, and how it complies with privacy and sector rules. Without that evidence, AI projects stall in procurement, approvals and due diligence.
We provide that evidence. Our assurance team combines PhD-level expertise in machine learning evaluation with decades of board-level governance experience, so testing is technically rigorous and findings are framed in terms your directors and regulators understand.
We test AI you have built, AI you have bought and AI embedded in the software you already use, before launch and continuously after it. Every engagement ends with clear findings, practical remediation and audit-ready documentation.
Problems we solve
- No independent evidence that an AI system works as claimed
- Board, regulator or customer due diligence asking questions you cannot yet answer
- Concern about bias or unfair outcomes for particular groups of people
- Vendors making AI claims you have no way to verify
- Models and prompts changing without anyone re-testing them
- Uncertainty about where you stand against privacy law, sector rules and AI standards
What’s included
A complete engagement, end to end.
Compliance gap assessment
A structured review of each AI system against the privacy, sector and AI-specific obligations and standards that apply to it.
Accuracy & performance testing
Rigorous testing against representative real-world data, with metrics agreed up front and tied to business outcomes.
Bias & fairness testing
Analysis of outcomes across demographic and customer groups to detect and reduce unfair or discriminatory results.
Robustness & adversarial testing
Stress testing with edge cases, unexpected inputs, prompt injection and jailbreak attempts to find failure points before others do.
LLM & generative AI evaluation
Measurement of hallucination, groundedness, harmful content and data leakage in chatbots, assistants and agents.
Privacy & data protection review
Checks for personal information exposure, data retention and cross-border data flows in AI pipelines and vendor tools.
Third-party & vendor AI audits
Independent verification of the claims, controls and contracts behind AI products you buy or plan to buy.
Audit reports & certification readiness
Board-ready assurance reports, remediation plans and evidence packs, including readiness for ISO/IEC 42001 certification.
Continuous assurance
Automated test suites and scheduled re-audits that catch regressions whenever models, prompts or data change.
Process
How the work unfolds.
Phase 1
Scope
Identify the AI systems in scope, their risk level and the obligations and standards that apply to each.
Phase 2
Test
Run technical testing for accuracy, fairness, robustness, privacy and security using realistic data and adversarial scenarios.
Phase 3
Report
Deliver clear findings, risk ratings and a prioritised remediation plan, presented to management and the board.
Phase 4
Monitor
Re-test after fixes and set up continuous assurance so compliance holds as your systems evolve.
Technology
Tools we use, chosen on merit
Standards & frameworks
- ISO/IEC 42001
- ISO/IEC 23894
- Australia's Voluntary AI Safety Standard
- NIST AI RMF
- OWASP Top 10 for LLMs
Regulation & obligations
- Privacy Act & Australian Privacy Principles
- APRA CPS 230 & CPS 234
- TGA software as a medical device
- EU AI Act
- GDPR
Testing methods
- Golden datasets
- Fairness metrics
- Adversarial & red-team testing
- LLM evaluation harnesses
- Explainability analysis
Sample outcomes
What clients typically see
- 3–6 wks
- From scoping to a board-ready assurance report for a single AI system
- 40+
- Test scenarios per system covering accuracy, fairness, robustness and privacy
- 100%
- Of findings delivered with a prioritised, practical remediation plan
Illustrative results from past engagements. Outcomes vary by context.
FAQ
Common questions
Are your audits independent?
Yes. When we audit a system, the testing team is separate from anyone who designed or built it, and we do not audit systems where that independence would be compromised. Our findings are our own professional assessment.
Can you certify our AI system or organisation?
Formal certification, for example against ISO/IEC 42001, is issued by accredited certification bodies. We prepare you for it: we assess readiness, close gaps and assemble the evidence an external certifier will look for.
Which regulations do you test against?
It depends on your industry and markets. Common examples include the Privacy Act and Australian Privacy Principles, APRA prudential standards for financial services, TGA requirements for software as a medical device, and the EU AI Act and GDPR for organisations operating in Europe. We work alongside your legal advisers, who remain responsible for legal interpretation.
Can you audit AI we bought from a vendor?
Yes. We assess vendor claims, contracts, data handling and real-world performance on your data, so you know what you are actually buying and where the risks sit.
How often should AI systems be re-tested?
Whenever the model, prompts, data or use case change materially, and at least annually for higher-risk systems. Continuous automated testing catches most regressions between formal audits.
Let’s discuss your project.
Tell us what you want to achieve with AI compliance, testing & auditing. A senior consultant will reply within one business day.