8 categories.
48 blindspots.
The AI Blindspot Framework is Velinor's proprietary methodology. It is how we classify real-world AI failures, score your portfolio, and tell your board where to look next.
A board-grade taxonomy of where AI fails.
Every Velinor audit, advisory engagement, and published case study is classified against the same taxonomy: 8 stable categories, 48 individual blindspots, mapped to the parent R3AI framework (Reliable, Resilient, Responsible) and cross-walked to NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
Each blindspot is a question a board can ask
Not a checklist. Every blindspot carries an executive question, a precise definition of the failure mode, a representative case study, stakeholders, and the regulatory references it engages.
Stable categories, dynamic blindspots
The 8 categories are stable. Individual blindspots are added, merged, split, or deprecated as new failure patterns emerge from the live case base. Every classified case records the taxonomy version it was classified under.
Published methodology
The framework, the scoring model, and the editorial standards are openly published. Citability is the credibility asset — any decision informed by Velinor can be defended in front of a board, an auditor, or a regulator.
The 8 categories
Every active AI use case in your portfolio fits somewhere on this map. Most fail at the intersections.
Business & Strategic BUS
Blindspots in business strategy, ROI, market positioning, customer value, and investment prioritisation.
Operational Management OPS
Blindspots in monitoring, incident response, performance, scalability, integration, and business continuity.
Human Factors HUM
Blindspots in change management, skills, human-AI collaboration, trust, workforce, and culture.
Governance & Compliance GOV
Blindspots in accountability, regulatory compliance, ethics, risk management, data governance, and audit.
Technical Implementation TEC
Blindspots in integration architecture, deployment, performance, data pipelines, security architecture, and maintenance.
Data Management DAT
Blindspots in data quality, privacy, bias, lineage, lifecycle, and third-party data dependencies.
Security & Privacy SEC
Blindspots in model security, data poisoning, privacy leakage, infrastructure, model theft, and incident response.
Environmental Factors ENV
Blindspots in organisational culture, stakeholder expectations, resource allocation, market pressure, regulatory environment, and external partnerships.
Cross-walked to the standards your regulators read
Every blindspot carries explicit references to the established AI standards a regulator, auditor, or customer security team will use to assess you.
NIST AI Risk Management Framework
The US National Institute of Standards and Technology framework. The de facto reference for AI governance in US regulated industries.
ISO/IEC 42001
The international management-system standard for AI. Certifiable, and increasingly required by enterprise procurement.
EU AI Act
In-force since 2024. High-risk classifications drive obligations for any organisation deploying AI inside the EU or its supply chain.
Backed by a live case base
The framework is not a theoretical model. It is calibrated against real failures.
Run your portfolio against the framework.
A Velinor AI Audit maps every active AI use case in your organisation to the 48 blindspots, benchmarks against documented sector failures, and hands your board a 90-day roadmap.