Assurance & Validation
AI Assurance & Validation
Independent validation and assurance for AI systems and workflows — integrating model validation, adversarial testing, and governance-aligned resilience assessment into a single operational discipline.
Modern AI systems require continuous assurance that goes well beyond point-in-time testing. Verydion treats AI assurance as an operational governance capability — structured, evidence-based, and integrated with the broader risk and governance frameworks that regulated organizations depend on. Our assurance work spans the full lifecycle: from conceptual soundness and pre-deployment validation through to ongoing reliability monitoring, hallucination risk analysis, and adversarial testing. AI red teaming, where appropriate, is one component of this broader assurance discipline — not a standalone exercise. We connect OWASP LLM threat modeling, adversarial testing, and governance-aligned resilience assessments into a coherent operational narrative. The result is assurance that is executive-grade, regulator-ready, and built to sustain trust in AI systems over time.
Regulatory Context
The EU AI Act establishes mandatory assurance obligations for high-risk AI systems — including requirements for accuracy, robustness, and cybersecurity, with ongoing post-market monitoring. The ECB's supervisory expectations on internal models and EBA guidelines on internal governance require independent model validation for models used in material risk decisions. DORA extends operational resilience requirements to AI systems in financial services. Verydion's assurance methodology is aligned with these frameworks and produces evidence packages suitable for regulatory review, model risk committee reporting, and internal audit.
All Services
Scope of Engagement
What We Deliver
AI Model Validation
Independent validation of model design, performance, fairness, robustness, and explainability — assessing conceptual soundness, out-of-sample testing, and alignment with intended use across the full model risk spectrum.
Reliability & Evaluation Frameworks
Design and implementation of structured evaluation frameworks for AI systems — establishing repeatable, governance-aligned processes for ongoing reliability assessment and performance monitoring.
AI System Assurance
End-to-end assurance reviews of AI systems and workflows — evaluating implementation integrity, data pipeline quality, operational controls, and alignment between model design and production deployment.
Hallucination & Output Risk Analysis
Structured assessment of generative AI output risk — identifying hallucination patterns, factual reliability gaps, and output governance controls proportionate to the operational and regulatory context.
Governance-Aligned Testing Methodologies
Development of testing methodologies that connect technical assurance to governance obligations — producing evidence suitable for regulatory submission, audit review, and model risk committee reporting.
Adversarial Testing & Selective AI Red Teaming
OWASP LLM-aligned adversarial testing and selective red teaming — integrated into broader operational governance and resilience frameworks, not conducted as isolated security theater. Covers prompt injection, model inversion, data poisoning, and supply chain threat scenarios.
AI Operational Risk Assessment
Structured assessment of AI operational risk across the deployment lifecycle — identifying governance gaps, monitoring deficiencies, and resilience weaknesses that create regulatory or operational exposure.
Ideal For
Who Is This For?
- Banks and financial institutions requiring independent model validation under ECB, EBA, or national regulatory expectations
- Insurance companies validating actuarial and underwriting models under Solvency II
- Healthcare organizations validating clinical AI models prior to deployment
- Organizations deploying generative AI in regulated or knowledge-intensive environments
- Organizations preparing AI systems for EU AI Act conformity assessment
- Internal audit and model risk functions seeking independent technical assurance capacity
- Organizations that have received regulatory findings related to model risk or AI governance
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