AI Governance Built for Financial Services Risk and Compliance Teams

SR 26-2, the revised guidance on model risk management that replaced SR 11-7 and SR 21-8 in April 2026, is the Federal Reserve and OCC supervisory anchor for model risk. It is non-binding guidance, most relevant to banks above $30 billion in assets, and it expects every traditional model that informs a business decision to be inventoried, validated by an independent party, documented, and monitored over its life. OCC Bulletin 2026-13 carries the same revised guidance into the OCC's own supervision. Layered on top are third-party and vendor risk obligations, because most banks consume AI through a vendor rather than building it, and fair lending law under ECOA, which does not care whether a denial came from a scorecard or a neural network: a disparate-impact problem is a disparate-impact problem. For globally-active institutions, the EU AI Act adds risk-tiered obligations on top of the US supervisory stack.

Here is the core challenge, and it is the reason this page exists. SR 26-2 makes the boundary explicit: it characterizes generative and agentic AI as novel and rapidly evolving and leaves them outside model-risk scope, governed instead by other risk-management practices. That carve-out does not make those systems ungoverned; it moves them into a gap your program now owns. The carve-out exists because LLM and GenAI systems break the assumptions traditional model risk was built on. SR 11-7 and the conventional model-risk playbook assume a model is documented, validated, and reproducible. You can read its inputs, re-run it, and get the same answer twice. LLM and GenAI systems are the opposite: they are non-deterministic, so the same prompt can return different output on two runs. They are opaque, so the reasoning behind an answer is not legible the way a regression coefficient is. And they are updated by a third party, often silently, so the model you validated in March is not the model in production in June. That combination is why the revised guidance set generative and agentic AI aside, and an examiner who asks how this system is governed is asking a question your existing MRM process was never built to answer.

The institutions feeling this most acutely are the ones already running Microsoft 365 Copilot, Azure OpenAI, or Databricks in production, often before model risk, compliance, or the board had a chance to weigh in. The pressure shows up as an examiner finding, a procurement questionnaire from an enterprise customer, or a risk committee that wants to know what the firm's AI exposure actually is. If any of that sounds familiar, our AI governance readiness engagement for banks is built to close exactly that gap.

The fastest way to fail an AI governance engagement is to stand up a second, parallel compliance program that nobody maintains. We do the opposite. We map AI governance onto the bank's existing SOC 2 or ISO 27001 control program with a crosswalk, so one control set answers multiple frameworks. Document once, tag twice. The evidence your security team already produces gets reused, and your auditors see continuity rather than a bolt-on.

On that foundation we build the things an examiner, a board, and a procurement team all ask for: a defensible AI inventory that names every LLM and GenAI system in use, including the shadow-AI deployments nobody registered; a risk classification that tiers each system by impact and regulatory exposure; an audit-readiness gap assessment against SR 26-2, third-party risk, fair lending, and NIST AI RMF; and a remediation roadmap that sequences the work so the highest-risk gaps close first. The emphasis throughout is control-mapping and readiness, not certification. We make you audit-ready and assemble the evidence. We do not issue certificates, and we do not claim to guarantee that you pass an audit or avoid an enforcement action, because no honest advisor can.

If the more urgent question is whether a deployed system can be broken rather than whether it can be governed, that is a different engagement: AI red teaming and LLM security testing finds prompt injection, data leakage, and agent abuse before an attacker or an examiner does.

Engagements run on a senior-only bench. There is no junior hand-off and no rented dashboard: the person who scopes the work is the person doing the work, and the person in the room with your risk committee is the person who wrote the gap assessment. That matters in a regulated setting, where the quality of the answer to a hard examiner question depends entirely on who is answering it.

The firm also ships authored open-source IP. mcp-warden is DSE's public MCP supply-chain integrity gate, the same kind of integrity check we bring to a bank's AI vendor surface. We govern AI by building the controls that govern AI, not by reselling someone else's framework. Established 2026, operator-led, and accountable on paper under a signed SOW or MSA.

Not sure you are ready for a paid engagement yet? Start with the free AI governance audit-readiness checklist: 14 self-scored items across NIST AI RMF, mapped to ISO 42001 and US supervisory expectations. It will tell you, in plain terms, where the gaps are before anyone else asks.