AI in internal audit: full-population testing, continuous monitoring, where evidence breaks down, and an assurance checklist for the AI your business runs.
AI exclusions now appear in thousands of US policy filings. The renewal questionnaire, not a regulator, will set your first enforceable AI controls.
AI contract review for in-house legal: which tasks to automate, what the evidence shows, where hallucination and privilege risk sit, and who signs off.
Boards ask the CFO what AI returns, and most cannot say. Why hours saved fails, which metrics hold up, and a funding model that kills what does not pay.
A chief AI officer works only if it holds three things: the platform and its controls, the funding gates and risk sign-off. Otherwise it is a demo team.
Where AI helps in hiring and HR, where legal risk concentrates, what NYC, the EU, Colorado, Illinois and California require, and which controls hold up.
AI spend is moving from central IT to business lines. That ties cost to outcomes but breaks controls built for an IT-held purse. The model that works.
Why AI assistants expose overshared data, how data classification fixes it, what to label first, and a 90-day plan for executives.
Agent approvals catch errors only at irreversible actions, with a clear diff, at a pace reviewers can read. Where to put them, and how to tell they work.
Why LLMs hallucinate, the three kinds of AI hallucination, and which controls actually reduce it, ranked by cost and matched to the cost of an error.
When an agent moves money or promises a customer something, the loss lands wherever the contract is silent. That gap is a deployment constraint.
Prompts, completions, embeddings, and logs land in four different places. What a regional endpoint guarantees, what it only implies, and how to decide.
Deep analysis across the systems, strategies, and economics that shape modern technology.
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