THE GRITWORKS BLOG
Working notes on making enterprise data usable for AI.
Perspectives on sensitive data, de-identification, synthetic unstructured content, evaluation and the operating realities of regulated AI.
How Regulated Industries Can Accelerate AI Without Compromising Compliance
Regulated industries — finance, healthcare, insurance, pharmaceuticals, government — are in a paradox. They are under enormous pressure to move fast on AI. Competitive dynamics, cost reduction mandates, and productivity imperatives are pushing every organization toward AI-driven automation and decision support. At the same time, their operating environments are defined by compliance obligations, privacy law, and risk governance frameworks that make moving fast feel impossible.
Synthetic vs. Sanitized — Choosing the Right Data Strategy for Your AI Team
"Just use synthetic data" has become a popular answer to enterprise data access problems. And in many situations, it is the right answer. But synthetic data is not a universal substitute for real data — and treating it as one can introduce its own set of problems.
The Hidden Cost of Weak Test Data
There is a cost that rarely appears in project post-mortems, even when it is the root cause of failure. It does not show up in sprint retrospectives. It rarely gets flagged in architecture reviews. But it quietly derails AI projects at every stage of the development lifecycle: weak test data.
Why AI Teams Don't Have a Data Problem — They Have an Access Problem
Every AI team eventually hits the same wall. The model architecture is solid. The engineering team is talented. The business case is clear. And yet the project stalls — not because of a lack of data, but because the data that exists can't be touched.