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 …
Implementation Tips for GRC and AI Professionals Make the AI Policy Operational, Not Decorative Most AI policies fail before they get printed. A paragraph about “responsible AI” …
Why a Lifecycle RACI Matrix Matters Most AI governance failures trace back to one root cause: nobody owned the problem at the moment it mattered. A model drifts in production and …
Why You Need a Dedicated AI Compliance Register Most organizations track regulatory obligations in a general compliance register or a GRC tool that wasn’t designed for the …
Most AI Impact Assessments Ignore Fundamental Rights Here’s the Category Taxonomy to Fix That Last year, I reviewed an AI impact assessment for a financial services firm deploying …