Cybersecurity · AI Governance · Data Science
8 years building AI systems that regulators can audit and executives can trust, focused on financial compliance, model risk, and responsible AI.
The Federal Reserve's November 2025 Statement of Supervisory Operating Principles signals a seismic shift — from checkbox compliance to material risk. Here's what changed and why it matters.
Data ScienceFast.ai uncovered something strange in LLM fine-tuning: training loss dropped suddenly after just one pass through the data — suggesting models can memorize inputs almost immediately. Here's what it means.
AI GovernanceA comprehensive framework for analyzing open-source GenAI across near, mid, and long-term development stages — and why the benefits generally outweigh the risks when governance keeps pace.
AI GovernanceLLMs used as evaluators show an average 40% bias in their outputs and a 49.6% RBO score misalignment with human preferences. The COBBLER benchmark quantifies exactly how and where these biases emerge.
Data ScienceLLM agents hit 94% success on basic web tasks — but drop to 25% on compositional tasks that combine multiple steps. The CompWoB benchmark exposes exactly where and why this happens.
Case StudiesAn EDA of 2007–2011 lending data to identify the driving factors behind loan defaults — amount-to-income ratios, revolving utilisation, derogatory records, and loan purpose all tell a story.
Everyone is excited about AI agents right now. An AI system that can plan, use tools, call APIs, make decisions, and complete tasks sounds powerful. But the more autonomy we give these systems, the more we need to think about control.…
Something that comes up a lot in financial services AI: the gap between running an LLM eval and producing something regulators can actually work with. ROUGE scores don't tell you which SR 11-7 clause you're satisfying.…
Anthropic's recent report on "Eval Awareness" is more than just a cool AI story. For Model Risk Management (MRM) and Cybersecurity teams, it is a massive red flag.…
The Federal Reserve's November 2025 Statement of Supervisory Operating Principles signals a seismic shift — from checkbox compliance to material risk. Here's what changed and why it matters.
Fast.ai uncovered something strange in LLM fine-tuning: training loss dropped suddenly after just one pass through the data — suggesting models can memorize inputs almost immediately. Here's what it means.
A comprehensive framework for analyzing open-source GenAI across near, mid, and long-term development stages — and why the benefits generally outweigh the risks when governance keeps pace.
Re-training LLMs from scratch when new data arrives is prohibitively expensive. Three simple strategies — LR re-warming, LR re-decaying, and minimal data replay — match the performance of full re-training at a fraction of the cost.
Traditional ensemble methods fail when correct answers are in the minority. AoR introduces hierarchical reasoning chain evaluation and dynamic sampling to fix this — and consistently outperforms standard approaches.
