Enterprise AI Agents: Where Accountability Sits
TL;DR An enterprise AI agent clears procurement, gets a budget line, and goes live inside a real workflow. Somewhere in that process, a project owner signs off on the deployment. Once the agent starts acting on accounts, that signature stops answering the question that matters day to day: who is accountable for this decision, made […]
Low-Code AI Agents Are Still Agents. Most Governance Programs Cannot See Them Yet
TL;DR A low-code AI agent gets built the same way a spreadsheet macro used to. Someone in operations needs a task handled, finds a tool that lets them wire it up without writing code, and has it running by the end of the week. The agent reads a queue, makes a decision, and takes an […]
Agentic AI Platforms and the Control Gap: What to Check Before You Sign
TL;DR A platform’s product page can list audit trails, maker-checker approval, and rollback, and none of that tells you whether those controls constrain what an agent does once it is running in production. The two are not the same claim. This piece names the three places that gap opens, gives you three questions that expose […]
Autonomous AI Agents Under Supervision: What Real Oversight Requires
TL;DR A bank deploying an autonomous AI agent today runs into a specific gap. SR 26-2, the Federal Reserve’s replacement for SR 11-7, names generative and agentic AI in a footnote and excludes both from its scope, calling them “novel and rapidly evolving.” That does not mean an agent goes unexamined. On 16 September 2026, […]
AI Governance in Financial Services: A Complete Guide for US Banks and Credit Unions
TL;DR A bank searching “AI governance” today finds two kinds of answers. One is broad and generic, written for any enterprise in any industry, citing the EU AI Act and calling it done. The other is banking-specific but built around European instruments, DORA, BaFin, the EU AI Act’s risk tiers, with nothing to say about […]
Data Governance for AI: The Inputs Nobody Audits
TL;DR A bank that passed its last data governance audit with no findings has good reason to trust its data. That confidence covers less ground than it used to. Most data governance programs were built to audit the data a model was trained on: sourced once, documented, approved. An AI model keeps consuming data long […]
AI Model Governance in Financial Services: Closing the Gap SR 26-2 Leaves Open
TL;DR A bank that recently finished mapping its model governance program to SR 26-2 has good reason to feel current. It should not feel finished. The guidance that replaced SR 11-7 in April 2026 is explicit that generative and agentic AI models sit outside its scope for now, deferred to guidance the agencies have promised […]
Enterprise AI Governance: How to Run One Program Across Every Business Unit
TL;DR A bank that governs its first AI model well, usually in collections or fraud, tends to assume the hard part is behind it. It is not. The hard part starts when a second business unit, underwriting or deposits, builds its own model with its own team, its own timeline, and no reason to know […]
Data Governance Models for AI-Driven Institutions: Which Structure Survives Continuous AI Demand
TL;DR A bank redesigning its data governance around AI is answering a narrower question than the general one. It already knows which structure, centralized, decentralized, or federated, fits its size and culture in the abstract. What it needs to know now is whether that structure holds up when dozens of models request data around the […]
AI Governance Platforms: Build, Buy, or Extend What You Already Run
TL;DR A bank evaluating AI governance platforms is usually further along than the search term suggests. The real question underneath “which platform” is “do we even need one,” and that question splits three ways. Build the capability with an internal team. Buy a platform built for it. Or extend the governance features that already ship […]
