Assurance Platform
AI risk expressed in the language of risk committees
Quantified models that place AI risk on the same page as credit, operational, and market risk — because that is the page the risk committee reads.
Capabilities
What this module provides
Materiality tiering
Classification of systems by the consequence of failure, driving how much assurance each one warrants.
Exposure modelling
Quantification of financial, regulatory, and operational exposure per system and across the portfolio.
Control effectiveness
Assessment of whether controls reduce the risk they are claimed to, tested rather than assumed.
Aggregation
Portfolio views that surface concentration — the same base model, vendor, or dataset behind many critical systems.
Honest quantification
We quantify where quantification is honest
A number carries authority a paragraph does not, which is exactly why an unfounded number is dangerous. Where the evidence supports a figure, we give one. Where it does not, we say so rather than manufacture false precision.
- Stated confidence intervals
- Documented assumptions
- Sensitivity analysis
- Named data sources
- Explicit unknowns
- No composite vanity scores
Standards
Anchored to published criteria
- ISO/IEC 23894:2023
- Guidance on AI risk management, aligning AI-specific risk to the ISO 31000 process.
- ISO 31000:2018
- The enterprise risk management framework AI risk must fold into rather than sit beside.
- NIST AI RMF 1.0
- The Govern, Map, Measure, Manage functions, plus the Generative AI Profile (NIST AI 600-1).
Other platform modules
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Start with an independent assessment of your highest-stakes AI system.