Insights into the governance and execution of AI in regulated industries
AI adoption in regulated industries turns on two questions: what the rules require, and what it takes to operate. The first is a matter of governance — the statutes, rules, and supervisory bulletins that define specific obligations and penalties. The second is a matter of execution — the operational discipline that determines which AI workflows reach production and which remain pilots. The analysis below addresses both.
The two questions every operator is asking in 2026
One asks where the boundary holds when AI touches regulated data. The other asks which workflows actually reach production. Below, the flagship piece on each.
Sending Sensitive Data to ChatGPT, Claude, or Gemini: A US Compliance Analysis
A practical walkthrough of the legal landscape governing the transmission of protected health information, material non-public information, claimant records, privileged content, and student data to third-party AI providers. Covers HIPAA, HITECH, SEC Reg S-P, SEC Rule 17a-4, GDPR, ABA Model Rules, FERPA, the FTC COPPA Rule, and the EU AI Act — with concrete guidance on what the boundary actually requires.
Where 2026 AI Budgets Land: A Back-Office Automation Demand Map
An evidence-based 2026 map of where back-office AI automation budgets are actually landing, where pilots stall, and the five characteristics that distinguish the workflows that reach production. Sourced from Gartner, Deloitte, McKinsey, Hackett Group, Forrester, Menlo Ventures, and PYMNTS — every figure dated and traced to its primary.
Explainers and deep dives
The pieces below cover the topics CISOs, Chief Compliance Officers, General Counsel, and CFOs of regulated mid-market organizations regularly ask us about. New articles publish on a rolling cadence; the trust page’s regulatory calendar drives much of the publication schedule.
What is AI governance?
A plain-language definition of AI governance as the operating system of policies, ownership, controls, evidence, and review that lets an organization use AI responsibly in production.
What is the evidence layer for regulated AI?
The part of an AI system that produces independently verifiable proof of what the AI did with sensitive data and which controls applied — proof an auditor can check without trusting the vendor.
What is regulated AI?
A practical definition of regulated AI: AI used where the workflow touches regulated data, regulated decisions, regulated records, or regulated professional duties.
The Five Characteristics of AI Workflows That Reach Production
Most enterprise AI pilots never reach production, and the reason is rarely the model. A deep-dive on the five architecture and governance characteristics that separate the workflows that ship from the ones that stall — with verified 2025–2026 evidence from MIT, Gartner, Forrester, Menlo Ventures, McKinsey, and METR.
The Network Tab Test: What a Vendor’s Website Actually Loads
Every third party a vendor’s website calls is visible in the browser’s network tab in about sixty seconds. How to run the test, how to read the five categories of what you find — and our own full inventory, published so you can verify it on the article itself.
Tools and artifacts you can use today
Three browser-only tools
A regulated-AI readiness assessment, an AI workflow ROI calculator, and a workflow fit finder — each answers a real buyer question in minutes, with the logic published and nothing transmitted unless you choose to send it.
See all tools→Verifiable evidence artifacts
A sample evidence package you download and verify offline with the MIT-licensed verifier, including a one-page receipt-to-framework crosswalk mapping evidence fields to AIUC-1, NIST AI RMF, ISO 42001, EU AI Act, HIPAA, SOC 2, and Reg S-P.
Download and verify→When the article raises a specific question, the discovery conversation is the next step
Every article is informed by practitioner build work and primary sources. If a piece raises a question specific to your environment, the next step is a discovery conversation scoped to that question.