If you run AI in more than one jurisdiction, you do not need a separate evaluation for each regime. You need one independent, contamination-free evaluation whose evidence you map to every framework you answer to. SR 11-7, the EU AI Act, MAS FEAT, OSFI E-23, and the rest ask their questions in different words, but they lean on the same underlying proof. Build the eval once, map it many times.
TL;DR
- The major AI and model-risk regimes worldwide converge on the same core: independent testing, on data the model has not seen, with honest statistics and an audit trail.
- Multinationals waste money by treating each country’s framework as a standalone project with its own corpus, harness, and report. The duplication is organizational, not regulatory.
- A single crosswalk lets one eval artifact answer an obligation in every regime, from US SR 11-7 to the EU AI Act to Singapore’s MAS FEAT.
- Independence and a contamination-free corpus are what make the evidence credible everywhere. Get them right once and the proof holds across borders. Get them wrong and it holds nowhere.
- In-VPC execution keeps weights and data inside your trust boundary while an independent party designs and grades the test.
Why do multinationals duplicate evaluation effort per country?
Because each framework reads as if it wants something unique, and a siloed org answers by starting over.
Your US model risk team reads SR 11-7 and commissions independent validation. A European workstream reads the EU AI Act and begins a fresh accuracy and robustness exercise for the high-risk system. Your India team maps to the RBI’s FREE-AI expectations. Singapore maps to MAS FEAT, Canada to OSFI Guideline E-23, and so on. Each treats its regime as a private project with its own held-out data, its own scoring, and its own document set.
The result is five or six evaluations that measure roughly the same capability, cost several times what one would, and still disagree because no two used the same corpus. That duplication is not required by the rules. It is an artifact of how the work got divided by geography.
What is the common evidence core across every framework?
The core is the bundle a single well-designed evaluation already produces. Run one eval properly and you generate the proof each regime is really asking for.
That bundle has six parts: an independent evaluator separate from the builder, a contamination-free corpus of real tasks the model has not trained on, trajectory and tool-selection scoring for agents, results with error bars instead of a bare average, a reproducible timestamped audit trail, and in-VPC deployment so data never leaves your control.
None of those are framework-specific. They are just good evaluation, and each regime consumes some subset. We describe how we assemble that evidence in our method, and how we separate the evaluator from the builder on our field page.
How do the frameworks compare in a single crosswalk?
Here is the map from regime to what it expects around evaluation, and the evidence that satisfies it. Treat every description as directional and at a principles level, and confirm the exact obligations with your own counsel and supervisors.
| Regime (jurisdiction) | What it expects around AI/model evaluation | Evidence that satisfies it |
|---|---|---|
| SR 11-7 (US, model risk) | Independent validation and effective challenge, separate from model development | Independent evaluator, documented validation methodology, reproducible audit trail |
| EU AI Act (EU, high-risk) | Risk management, testing for accuracy and robustness, technical documentation, post-market monitoring | Contamination-free accuracy and robustness tests with error bars, versioned docs, scheduled re-runs |
| ISO/IEC 42001 (international) | A managed, auditable AI management system | Repeatable performance records, governance separation, audit evidence |
| NIST AI RMF (US, voluntary) | Map, measure, and manage AI risk across the lifecycle | Measurable results tied to identified risks, traceable test records |
| RBI FREE-AI (India, finance) | Governance and oversight for AI in financial services | Independent oversight, fair testing evidence, data control in-region |
| UK principles-based (ICO, FCA, PRA) | Regulator-led expectations, including PRA model risk management principles | Independent challenge, documented model performance, auditable governance |
| MAS FEAT + IMDA GenAI (Singapore) | Fairness, ethics, accountability, transparency, plus GenAI governance principles | Evidence of fair, tested behavior, transparent method, accountable owner |
| OSFI Guideline E-23 (Canada) | Model risk management across the model lifecycle | Independent validation, performance monitoring, reproducible records |
| APRA + proposed AI guardrails (Australia) | Prudential risk standards plus proposed mandatory guardrails for high-risk AI | Tested performance evidence, risk controls, audit trail |
| ADGM / DIFC financial regulators (UAE) | Financial-regulator expectations for governed, tested AI | Independent testing, documented governance, data residency and control |
One argument runs through every row. The left column changes wording by country; the right column barely changes at all. You produce the right-hand evidence once, and it answers a question in each regime.
How do independence and a contamination-free corpus underpin all of them?
Because they are the two properties that decide whether anyone outside your team should believe the result, and every framework cares about exactly that.
Independence is the thread. SR 11-7 makes it explicit through effective challenge by a function separate from development. OSFI E-23 and the UK’s PRA model-risk principles carry the same expectation. The EU AI Act, MAS FEAT, and the rest each want assurance that the claim is not the builder grading the builder, and a self-scored eval cannot supply that. We cover why for regulated lenders in independent model validation for banks.
A contamination-free corpus is the other load-bearing property. If the model has seen the test, the score measures memorization, not capability, and no framework is satisfied by a number that means the wrong thing. Detection after the fact is unreliable, so the durable path is testing on tasks created after the model’s training cutoff, drawn from your own workflows, and refreshed as new cutoffs arrive. Get these two right once and the evidence holds up in every jurisdiction on the list.
Which framework applies where? A region-by-region map
Start from where you operate, then reuse the same evidence next door. Each pointer below goes to a deeper post.
- United States and banking: model risk under SR 11-7, with independence at its center. See independent model validation for banks.
- European Union: high-risk obligations under the EU AI Act. See EU AI Act model evaluation.
- United Kingdom: principles-based and regulator-led, including PRA model risk. See UK AI model validation.
- India: the RBI’s FREE-AI direction for AI in finance. See the RBI FREE-AI compliance checklist.
- Singapore: MAS FEAT principles plus the IMDA GenAI framework. See Singapore MAS FEAT AI evaluation.
- Canada: model risk under OSFI Guideline E-23. See Canada OSFI E-23 model risk for AI.
- Australia: APRA prudential standards and proposed mandatory AI guardrails. See Australia AI model evaluation under APRA.
- UAE: ADGM and DIFC financial-regulator expectations. See UAE AI governance and evaluation.
Once you see the overlap, the reuse pattern is obvious. We walk through it end to end in one eval, four frameworks.
Why does the audit trail matter as much as the score?
Because a result nobody can reconstruct later is worth little to a supervisor, and every regime eventually asks you to show your work.
The audit trail turns a passing grade into evidence. It records the corpus version and how it was held out, the tasks and rubric, the model and harness under test, who ran it and when, and the statistics behind the decision. When a supervisor in any jurisdiction asks how you know the system works, you hand them a record, not a memory. This is also where per-country evaluation quietly fails: six partial trails that do not line up, versus one coherent record every framework can read.
FAQ
Is SR 11-7 really comparable to the EU AI Act and MAS FEAT? At a principles level, yes. SR 11-7 centers on independent validation, the EU AI Act on tested accuracy and robustness with documentation, and MAS FEAT on fairness, ethics, accountability, and transparency. They use different vocabularies, but each rests on independent, well-documented, reproducible testing. The evidence core is shared even though the legal texts are not.
Can one evaluation satisfy every country we operate in? It replaces the duplicated evaluation work, not your legal analysis. You still confirm which obligations apply per jurisdiction and how each supervisor treats them. But the corpus, the independent testing, the statistics, and the audit trail are produced once and mapped, not rebuilt per region.
Do we have to send our model and data to an external evaluator? No. Independence is about who designs and grades the test, not where it runs. The evaluation can execute inside your own VPC so weights and production data stay within your trust boundary, which also meets the data control and residency expectations that recur across these regimes.
Which framework should we map to first? Start with whichever has the nearest deadline or the sharpest teeth for your use case. Because the evidence core is shared, satisfying one rigorously gets you most of the way to the others.
Comparing these frameworks side by side makes the duplication visible: the same evidence, asked for in ten different accents. Build that evidence once, independently and contamination-free, and map it to every regime you answer to. To see which of your obligations a single evaluation could cover, book a free eval diagnostic on our pricing page.