SR 11-7
The Federal Reserve and OCC's "Supervisory Guidance on Model Risk Management" (issued April 4, 2011), which requires banks to independently validate any model that drives a decision, across its lifecycle (conceptual soundness, ongoing monitoring, outcomes analysis), including vendor and AI models.
Defined in 2 GAGE programs, which carry 8 distinct definitions of it. The wording above is taught in Certified AI Transformation Professional (CATP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
The federal banking regulators' long-standing supervisory guidance on model risk management, requiring a bank to validate a model's data inputs and performance before and during deployment, and the existing institutional channel this topic points to for testing a vendor's synthetic-data and model-collapse claims.
The Federal Reserve and OCC's "Supervisory Guidance on Model Risk Management" (issued April 4, 2011), which requires banks to independently validate any model that drives a decision, across its lifecycle (conceptual soundness, ongoing monitoring, outcomes analysis), including vendor and AI models.
The Federal Reserve and Office of the Comptroller of the Currency's joint supervisory guidance, "Guidance on Model Risk Management," issued April 2011, requiring banks to validate, document, test, and independently review any model used in a material business decision.
The Federal Reserve's model-risk-management supervisory letter, enforced in practice primarily by bank examiners rather than by a Federal Reserve Governor directly on any single institution's specific AI model.
The Federal Reserve's supervisory guidance requiring banks to document, validate, and govern the models, including AI-driven models, they use for lending, underwriting, and risk decisions, creating a prudential regulatory exposure layer distinct from private tort liability.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Sector-Specific Economic Analysis: Compliance Cost and Competitive Advantage · AI Economics and Impact Modeling, The AI Lobbyist: Certified AI Policy Strategist
- Mapping AI Influence Networks in Government: Formal and Informal Power · Stakeholder Architecture and Relationship Building, The AI Lobbyist: Certified AI Policy Strategist
- AI Liability: Product Liability, Section 230, and Tort Evolution · Legal Frameworks and Liability Navigation, The AI Lobbyist: Certified AI Policy Strategist
- The Liability Risk Assessment and Mitigation Strategy · Legal Frameworks and Liability Navigation, The AI Lobbyist: Certified AI Policy Strategist
- The Emerging Frontier: Agents, Synthetic Data, Edge AI, and What Comes Next · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
- Financial Services and Insurance: NAIC, SR 11-7, CFPB, and the State Map · Sector Playbooks, The AI Lobbyist: Certified AI Policy Strategist
- The Compliance-First Transformation Playbook · Lab: Regulated Industries, Certified AI Transformation Professional (CATP)
- Building the Audit-Ready AI Program · Lab: Regulated Industries, Certified AI Transformation Professional (CATP)
Where it shows up at work
A definition is where SR 11-7 starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Reviews purpose, data, affected people, accuracy, bias, security, oversight, vendors and law for a use case, scores likelihood and impact, and documents residual risk.
One lesson on this page teach it as a primary outcome.
Classifies AI incidents by severity, runs containment, preserves evidence, manages notification, and closes the loop with lessons learned.
One lesson on this page teach it as a primary outcome.
Collects, labels and preserves the evidence that a control operated, a decision was made, and a claim can be defended to an auditor or regulator.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- Chief Privacy Officer, AI privacy editionneeds it at depth
- Chief Risk Officer, AI risk editionneeds it at depth
- AI Product Manager, responsible product editionneeds it at depth · 3 open postings this week
- AI Risk Managerneeds it at depth · 2 open postings this week
- AI Governance Analystneeds working knowledge · 3 open postings this week
- Governance Analystneeds working knowledge · 6 open postings this week
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: NIST AI RMF MAP and MEASURE; ISO/IEC 42005; EU AI Act Article 27; EU AI Act Article 73.
One interview question about it
Take me through an AI risk and impact assessment you would run for a hiring tool. What do you assess, and who signs?
Asked of candidates for ai risk and impact assessment. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Privacy and data protection carries it as work you can show: A DPIA is an impact assessment; the AI version adds accuracy, bias and oversight.
- Cybersecurity and IT carries it as work you can show: Incident playbooks and post-incident reviews are your artifacts.
- A cybersecurity or information assurance degree carries it as a claim to back up: Incident response is a course; AI incidents add model failure to the playbook.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.