Role Overview
The AI Risk Manager has become one of the most in-demand and well-compensated roles in the AI governance landscape, driven overwhelmingly by financial services regulation and the rapid expansion of AI/ML models in banking, insurance, and fintech. The role extends the established discipline of model risk management, governed by Federal Reserve SR 11-7 and OCC 2011-12 guidance, into the frontier territory of AI and machine learning systems, including generative AI and, as of early 2026, agentic AI systems.
If the AI Policy Analyst defines the rules and the AI Ethics Officer defines the values, the AI Risk Manager defines the controls. This role focuses on identifying, measuring, and managing the specific technical, operational, and data-related vulnerabilities that can lead to model failure or regulatory breach. ZipRecruiter lists approximately 783 AI Risk Manager positions, and Indeed shows over 395,000 results for “AI Governance Risk Compliance” jobs, signaling massive and growing demand.
In financial services, this role almost always sits within the second line of defense: the independent risk management function that provides oversight and challenge to the first line (business units and model developers). The three-lines-of-defense model (business units developing AI, risk management overseeing it, internal audit providing independent assurance) is the dominant organizational framework. Reporting lines typically lead to the Chief Risk Officer or Chief Information Security Officer.
Financial services is the dominant employer, accounting for the majority of postings and offering the highest compensation. This is not an entry-level role. Most positions require mid-career experience in risk management, model validation, IT audit, or data science.
Career Compensation Ladder
The verified governance-focused range for AI Risk Managers is $120K to $180K (IAPP Salary Survey 2025-26, ZipRecruiter). The full career ladder, particularly in financial services, extends significantly higher.
Entry-level AI Risk Analyst (1 to 3 years): $90,000 to $120,000. GRC Analyst, Compliance Analyst, or Model Risk Analyst roles at banks or insurance companies. These typically require 1 to 3 years of experience with a bachelor’s degree and foundational risk or compliance exposure.
Mid-career AI Risk Manager (3 to 7 years): $110,000 to $180,000. The core AI Risk Manager tier. Requires 3 to 6 years of risk management, audit, model governance, or technology risk experience, with direct AI/ML exposure preferred. This range aligns with our verified table ceiling.
Senior and VP (7 to 12 years): $160,000 to $245,400. Citi’s AI Risk Specialist SVP posting specifies $163,600 to $245,400 base compensation. Moody’s VP AI Risk Management lists $163,300 to $236,800. These positions require 8+ years in a large, complex financial institution with 4+ years in risk management and direct AI/ML exposure.
Senior Audit Manager and Director (10+ years): $198,000 to $294,900. Bank of America’s Senior Audit Manager for AI Model Risk posts at this range. FINRA’s Director of Model Risk Oversight anchors the regulatory body end. At this level, regulatory interaction experience and a history of managing model risk across enterprise AI portfolios are expected.
Glassdoor data confirms that financial services pays the highest premiums: median total pay for Risk Management Managers in financial services is approximately $184,242. These figures typically exclude bonuses (15 to 30% at senior levels) and equity compensation at technology companies.
What You Will Do Day to Day
The daily work of an AI Risk Manager centers on six primary activities.
AI use case review and approval involves acting as the second line of defense for reviewing and either approving or challenging AI use cases across the firm.
Risk assessment includes conducting evaluations covering model fairness, bias, explainability, data privacy, security, and emerging areas like agentic AI.
Model validation involves leading or supervising independent validation of credit risk, market risk, operational risk, AML, fraud, fair lending, and CECL models.
Framework development means supporting the execution and continuous refinement of the organization’s AI Risk Management Framework.
KPI/KRI monitoring requires defining, tracking, and reporting Key Performance and Risk Indicators for the AI portfolio.
Thematic reviews involve conducting cross-cutting analyses to identify emerging risk trends.
The concept of “credible challenge” is fundamental in banking risk management: providing independent, rigorous pushback to first-line model developers. This is explicitly cited in multiple job postings and is the defining professional competency of a second-line risk manager.
Key deliverables include AI risk assessments (per-use-case and portfolio-level), AI risk registers, model validation reports, risk dashboards with KPIs/KRIs, board and executive reports on AI risk posture, thematic review reports on emerging risks, and training materials on AI risk principles.
Day-to-day tools include GRC platforms (ServiceNow, RSA Archer, OneTrust, AuditBoard), AI-specific governance platforms (Credo AI, Monitaur, FairNow), model monitoring systems (MLflow, custom dashboards), data analytics tools (Python, R, SAS, SQL), and visualization platforms (Power BI, Tableau).
Skills Deep Dive
Technical Skills
The AI Risk Manager role demands significant technical proficiency. Programming skills in Python (dominant), R, SAS, and SQL are expected for risk analytics, model validation, and data analysis. Model validation techniques, including back-testing, benchmarking, sensitivity analysis, and challenger model development, are core competencies, especially for banking roles. Statistical and quantitative methods including regression analysis, time series modeling, and Monte Carlo simulation are expected for quantitative risk roles.
GRC platforms are central to daily work. The established platforms include ServiceNow GRC, RSA Archer, OneTrust, AuditBoard, Riskonnect, SAP GRC, and IBM OpenPages. Purpose-built AI GRC tools are emerging: Credo AI (the first purpose-built GRC platform for AI), Monitaur, and FairNow represent this new category and familiarity with them is a differentiator.
Knowledge Architecture
Five knowledge domains are absolutely essential. Risk management frameworks: enterprise risk management (ERM), the three-lines-of-defense model, and risk appetite frameworks. The NIST AI RMF 1.0 with its four functions (Govern, Map, Measure, Manage) is increasingly cited in federal and private sector implementations. ISO/IEC 42001 represents the international compliance benchmark. In banking, SR 11-7 and OCC 2011-12 model risk management guidance is explicitly required in the majority of postings. AI/ML fundamentals: understanding of machine learning models, neural networks, LLMs, generative AI, and model lifecycle management.
Specialized differentiators include quantitative risk modeling (statistical methods, econometric models, stress testing) for the most technical and highest-paying roles, deep financial regulation expertise (Basel frameworks, CECL, AML/BSA, fair lending), and generative AI risk assessment (LLM-specific risks including hallucination, prompt injection, IP leakage, and data exfiltration). Citi’s January 2026 posting explicitly mentions agentic AI risk assessment, signaling the newest frontier.
Soft Skills
Risk communication, translating complex AI risks for non-technical stakeholders and board-level audiences, is the most critical interpersonal skill. Executive reporting skills (preparing risk dashboards, board reports, regulatory submissions), cross-functional influence across data science, legal, compliance, and business units, and escalation management (knowing when and how to raise risk issues) are consistently required.
Certifications That Move the Needle
IAPP AIGP (Cross-Cutting Standard)
The IAPP AIGP has become the universal AI governance credential. Exam costs $799 ($649 for IAPP members), 100 questions over 2 hours and 45 minutes, no experience prerequisites. Renewal requires 20 CPE credits every 2 years.
ISACA CRISC (Premier IT Risk Certification)
The ISACA CRISC (Certified in Risk and Information Systems Control) is the premier IT risk certification. The exam costs $575 for ISACA members ($760 for non-members) with a $50 application fee. Certification requires 3+ years of professional experience across at least 2 of 4 domains, though candidates can sit the exam first and certify within 5 years. ISACA data shows average CRISC-holder compensation exceeding $151,000, with over 30,000 certified professionals globally. Renewal requires 20 CPE hours per year.
GARP FRM (Financial Risk Gold Standard)
The GARP FRM (Financial Risk Manager) is the gold standard for banking AI risk roles. It consists of two parts (100 questions/4 hours and 80 questions/4 hours), with a $400 one-time enrollment plus $600 to $800 per exam. Total investment including preparation materials ranges from $2,150 to $3,600. Study time is approximately 250 hours per part (500 total), and most candidates take 1 to 2 years. Pass rates are challenging at 42 to 50%. Certification requires passing both parts plus 2 years of full-time risk management experience. Over 96,000 FRM holders exist globally, and UK ENIC benchmarking rates it equivalent to a master’s degree in difficulty.
GARP RAI (Newest Purpose-Built Credential)
The GARP RAI (Risk and AI Certificate), launched in 2024, was built specifically for risk professionals managing AI risk. The exam consists of 80 multiple-choice questions over 4 hours, offered twice yearly (April and October). Cost ranges from $525 to $750 depending on registration timing. Study time is estimated at 100 to 130 hours. The first full-cycle pass rate was 66% (April 2025). The curriculum covers five modules: AI and Risk Introduction, Tools and Techniques, Risks and Risk Factors, Responsible and Ethical AI, and Data and AI Model Governance. This credential offers significant early-adopter advantage given its recency.
Supporting Certifications
ISO 42001 Lead Auditor/Lead Implementer ($1,500 to $3,000 through PECB or BSI) demonstrates ability to audit AI management systems. ISACA CISA ($575 to $760 exam) is valuable for AI audit roles. NIST AI RMF 1.0 Architect, listed on the NICCS (CISA) catalog, validates NIST AI RMF implementation competence ($1,000 to $2,500 for training and exam packages). Cloud AI certifications from AWS, Azure, or GCP provide technical credibility.
Which certifications matter most by sector: For financial services, FRM, CRISC, GARP RAI, and AIGP (in roughly that order of traditional value, with AIGP and RAI gaining rapidly). For technology, AIGP, CRISC, NIST AI RMF, and cloud AI certifications. For cross-sector AI governance, AIGP is the emerging standard, complemented by GARP RAI and NIST AI RMF.
Learning Roadmap
Must-Read Standards and Publications
The NIST AI RMF 1.0 and its companion Playbook (free at nist.gov) are essential, along with the NIST AI RMF Generative AI Profile extending the framework to GenAI risks. ISO/IEC 42001:2023 is the certifiable international AI management system standard. For banking professionals, SR 11-7/OCC 2011-12 supervisory guidance and the 2021 Interagency Statement on Model Risk Management for BSA/AML are non-negotiable reading. The EU AI Act is essential for anyone in organizations with global operations. The OECD AI Principles provide the international policy foundation.
Training and Courses
The IAPP AIGP training (official self-paced or live online, approximately $995) provides the most direct certification preparation. GARP RAI curriculum materials are included with exam registration. ISACA offers CRISC self-paced courses and bootcamps ($500 to $2,000). Coursera offers free foundational AI Governance courses from the University of Pennsylvania. A Smart Online Course on Responsible AI Risk Management using NIST AI Framework (9 hours) provides focused, practical training.
Professional Communities
GARP (Global Association of Risk Professionals) anchors the financial risk community with 96,000+ FRM holders, regular events, and webcasts. ISACA provides 145,000+ members across 188 countries. IAPP manages the largest privacy and AI governance community and hosts the Global Privacy Summit (March 30 to April 1, 2026, Washington, D.C.). RIMS focuses on enterprise risk management and holds the annual RISKWORLD conference. AISafety.com maintains a specialized AI risk job board.
Career Pathways
Starting from Zero
The most natural educational foundations are bachelor’s or master’s degrees in finance, computer science, data science, mathematics, statistics, operations research, engineering, business, law, or economics. Advanced degrees are preferred by most employers.
The from-zero roadmap involves five stages.
Stage 1 (months 1 to 6): Build foundational knowledge in risk management principles and AI fundamentals through courses (GARP Academy, Coursera AI courses).
Stage 2 (months 3 to 9): Study SR 11-7/OCC guidance and the NIST AI RMF; begin CRISC or AIGP certification preparation.
Stage 3 (months 6 to 12): Earn your first certification (AIGP is fastest with no experience prerequisite); develop practical skills with GRC platforms and Python-based risk analytics.
Stage 4 (months 9 to 18): Target entry positions: GRC Analyst, Compliance Analyst, or Model Risk Analyst roles at banks or insurance companies.
Stage 5 (years 2 to 4): Build toward AI Risk Manager through demonstrated model validation or AI governance project work.
Transitioning from Adjacent Roles
Traditional risk management professionals (operational risk managers, credit risk analysts) have approximately 65% readiness for AI Risk Manager roles and can transition in 6 to 9 months with targeted upskilling in AI fundamentals and AI-specific governance frameworks.
IT auditors (especially CISA-certified) transition through AI audit work and need to add AI/ML technical knowledge. Model validation analysts at banks have the most direct pathway, requiring primarily AI governance framework knowledge (AIGP, NIST AI RMF) and generative AI risk expertise.
Data scientists and ML engineers bring the strongest technical foundations but need risk management framework knowledge, regulatory understanding, and risk communication skills.
Cybersecurity professionals can pivot through AI security risk into broader AI risk management.
Actuaries in the insurance sector translate quantitative skills and regulatory knowledge into AI risk roles naturally.
Where This Role Leads
The typical trajectory moves from entry/junior (0 to 3 years: AI Risk Analyst, GRC Analyst) to mid-level (3 to 7 years: AI Risk Manager, Model Risk Manager) to senior/VP (7 to 12 years: Senior AI Risk Manager, VP AI Risk Management) to executive (12+ years: SVP/Head of AI Risk, Chief Risk Officer, Chief AI Officer). Lateral moves into CISO for AI, enterprise-wide CRO roles, or consulting practice leadership are well-established.
Market Context
Who Is Hiring
Major banks dominate: Citi, Goldman Sachs, JPMorgan Chase, Bank of America, Wells Fargo, Huntington Bank, Capital One, and others are actively hiring. Regulatory bodies (FINRA) and financial infrastructure companies (Early Warning/Zelle, LPL Financial) also hire. Insurance companies (The Hartford, MetLife, USAA, Nationwide) represent the second largest segment. Big Tech and AI companies (OpenAI, xAI, Character.AI, Visa) hire in smaller numbers but at premium compensation. Consulting firms (Deloitte, EY) have growing AI risk practices. Fintech companies (Credit Karma, Mission Lane, Gusto) are emerging employers.
What Employers Expect on Your Resume
Entry and junior positions require 1 to 3 years with a bachelor’s degree. Mid-level positions require 3 to 6 years of risk management, audit, or technology risk experience. Senior and VP positions require 7 to 10+ years. Director and SVP roles require 12+ years with a track record of leading large AI/technology initiatives and regulatory interaction experience.
Model validation experience is the single strongest credential for banking roles. Regulatory compliance experience in financial services (specifically SR 11-7 knowledge) is essential for the dominant employer segment. Experience in large, complex financial institutions is repeatedly cited as either required or strongly preferred. Financial services experience is strongly preferred and often explicitly required for the highest-paying positions, though technology companies, insurance firms, and consulting practices offer alternative entry points.
Related Roles
Professionals interested in AI Risk Manager roles may also explore:
- AI Auditor (third-line assurance, independent model and process auditing)
- AI Compliance Manager (operational compliance program management)
- AI Security Specialist (AI-specific cybersecurity, adversarial ML, red teaming)
- AI Model Validator (specialized model validation and performance testing)
