AI Systems Assurance
We provide independent assessment of AI/ML systems in production or pre-production as control objects: data lineage, model lifecycle, evaluation evidence, transparency and human oversight — with residual risk you can show to risk and supervisors. Hardening, monitoring design and remediation support are offered only as a separate advisory engagement; when an independent assurance opinion is required, assessment and advisory roles are segregated.
What this covers
- System boundary, data-flow and oversight mapping
- Controls across data, model lifecycle and evaluation evidence
- AI/ML risk assessment and gap analysis
- Prioritised remediation recommendations for client-owned action
- Optional advisory hardening or monitoring design under a separate engagement
- Optional independent verification after remediation
Typical outcomes
- Documented AI risk picture tied to operating context
- Supervisory-ready control and oversight evidence
- Clear separation between assurance findings and advisory work when required
When to engage
- Independent assessment of production AI/ML with transparency or safety expectations
- Closing the evidence gap before go-live or after an internal risk review
- Advisory hardening only where role segregation is agreed
Engagement model
- Agree assurance scope, system boundary and evidence expectations.
- Assess controls across data, model lifecycle and human oversight.
- Deliver risk assessment, gap analysis and prioritised remediation recommendations.
- Optional separate advisory scope for hardening or monitoring design — with role segregation when independence is required.
Describe the AI system and its operating context. We will propose an assurance approach and, if needed, a separate advisory scope that preserves independence.