Govern AI across your organisation, with confidence.

AI Governance and Lifecycle Management

You will know what AI is running, who owns it, and nothing new will go live without passing through a defined front door.

Automated asset discovery and inventory

Scanning across every environment, mapping AI models, APIs, datasets and notebooks. Identification of AI running outside any approval path. Comprehensive AIBOM generation, with dependencies and versions tracked.

Process flow analysis and weakness identification

Mapping actual workflows against documented ones. Identifying approval bypasses, testing change management controls, validating stage gate enforcement and measuring process adherence.

Model registry and tracking validation

Auditing model metadata completeness, verifying lineage tracking, testing version control integrity, validating ownership assignment and checking decommissioning procedures.

Governance, front door and change management

Designing the route by which AI gets registered, risk classified, approved and exception managed, built into your existing GRC rather than bolted alongside it. Governance model, policy framework and clear ownership across security, GRC, data and engineering.

Adversarial Defence and Robustness

You will know how your AI behaves under attack, with guardrails measured rather than assumed.

Prompt injection attack simulation

Executing OWASP LLM01 attacks, testing direct and indirect injection vectors, validating input sanitisation effectiveness and measuring guardrail bypass success rate.

Model evasion testing

Crafting adversarial examples, testing model decision boundaries, measuring robustness to perturbations and validating defensive mechanisms.

Jailbreak resistance validation

Testing known jailbreak techniques, measuring policy enforcement, validating context window protection and testing multi-turn conversation attacks.

Secure AI Systems Design and Development

You will know your AI systems, and your AI-assisted development, are held to the same engineering standard as everything else you ship.

Code security and vulnerability analysis

SAST and DAST scanning of AI code, manual code review for AI-specific vulnerabilities, API security testing, secret exposure detection and dependency vulnerability assessment (SCA). Controls for AI-generated code, so what an assistant suggests is tested before it is trusted.

MLOps pipeline security assessment

CI/CD configuration review, pipeline privilege escalation testing, build process integrity validation, container security scanning and model signing verification.

Infrastructure security validation

Network segmentation testing, access control effectiveness, encryption validation at rest and in transit, infrastructure-as-code security review and cloud security posture assessment.

AI Model Governance and Assurance

You will know the models you build or fine-tune behave fairly, predictably and within tolerance, and you will have the evidence to show it.

Bias and fairness technical testing

Statistical fairness analysis, protected attribute testing, disparate impact measurement, subgroup performance analysis and fairness metrics calculation including demographic parity and equalised odds.

Model explainability validation

SHAP and LIME analysis execution, feature importance verification, decision boundary mapping, counterfactual explanation testing and explainability coverage measurement.

Performance and drift monitoring assessment

Real-time accuracy tracking, data distribution shift detection, concept drift identification, model degradation measurement and alerting mechanism validation.