Incident Types and Direct Loss Categories That Define Real Exposure for AI Projects Here is a question that reveals whether your AI risk program is mature or performative: Can you …
A Practitioner’s Guide to ISO/IEC 25059 A model with 95% accuracy that nobody can explain, nobody can maintain, and nobody trusts is not a quality AI system. It is a liability …
A Practitioner’s Field Guide Most AI threat models are incomplete. Not slightly incomplete. Fundamentally incomplete. Last year I reviewed the threat model for a financial services …
The 15 Cost Categories for AI Budgets (And What They Actually Cost) A Deloitte survey found that 52% of AI projects exceed their original budget. The overage isn’t typically caused …
Why Probabilistic Risk Modeling Matters for GRC Professionals Picture a risk committee meeting. Someone points at a heat map and says, “Vendor concentration risk is High.” Twenty …
How to Choose the Right Use Case Before You Waste Time and Budget Most AI projects go wrong before anyone builds a model. They go wrong in the problem statement. The team says they …
Practical Empirical Risk Minimization for Predictive Risk Models Every predictive risk model makes mistakes. The question that determines whether a model is useful isn’t “Does it …
How to Build a Control Program That Catches Problems After Launch Most AI governance programs are strongest before launch and weakest after it. That is backwards. The real risk …
The Post-How to Keep AI Useful, Safe, and Accountable After Launch Most AI projects spend too much energy getting to deployment and not enough planning what happens next. That is a …
How to Use an AI Project KPI and Metrics That Actually Improves Delivery Most AI projects do not fail because the model is weak. They fail because nobody agrees on what success …