Description

A focused course, tailored for you

Building Banking Legal AI Governance Capability (AI Inventory + Vendor Contracting + Privilege + Regulator Engagement + Litigation Readiness)

Build the banking legal AI governance capability in 10 weeks. AI inventory + vendor contracting + privilege architecture + regulator engagement + litigation readiness.

Banking legal teams (Chief Counsel, Managing Counsel, Senior Counsel) face a new generation of AI legal issues: AI vendor contracting at scale, AI privilege and work-product issues, AI-related regulator engagement, AI-related litigation discovery, and the legal-to-board AI reporting cadence. Counsel who build the legal AI governance capability take the senior banking-legal work. Here is the 10-week build.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Banks are deploying AI across credit, fraud, AML, marketing, servicing, surveillance, and internal operations. Legal teams are the gating force on most of these deployments. Banking legal teams (Chief Counsel, Managing Counsel, Senior Counsel) now face a new generation of AI legal issues that did not exist three years ago.

AI vendor contracting at scale (model providers, RAG infrastructure, agent platforms, observability tools), AI privilege and work-product issues (when GenAI in legal research touches privileged content), AI-related regulator engagement (Fed, OCC, FDIC, CFPB, state banking departments asking pointed questions), AI-related litigation discovery (preservation of model versions, training data, inference logs), and the legal-to-board AI reporting cadence all require deliberate legal capability build.

This course teaches the 10-week build of banking legal AI governance capability: AI inventory and legal-risk classification, vendor-contracting playbook, privilege architecture, regulator-engagement model, litigation-readiness framework, and the board-reporting cadence. Twelve modules with deliverables. Plus a hand-built implementation playbook for your specific legal team and bank.

What you walk away with

A documented AI inventory with legal-risk classification.
An AI vendor-contracting playbook with redline positions.
A privilege and work-product architecture for AI-augmented legal work.
A regulator-engagement model for AI-related inquiry.
A litigation-readiness framework for AI-related disputes.
A board-reporting cadence on AI legal risk.
A 10-week build plan.

The 12 modules

Module 1. Banking legal AI landscape 2026

Detailed walkthrough of the banking AI legal landscape in 2026: Fed and OCC AI guidance, CFPB UDAAP application to AI, FDIC consumer-protection focus on AI, state attorneys-general AI activity, EU AI Act extraterritoriality, EEOC and state employment-law AI rules where applicable to bank workforce decisions, and the litigation landscape. The legal issues that did not exist three years ago.

Module 2. AI inventory and legal-risk classification

Build the AI inventory and legal-risk classification: inventory scope (in-house, vendor, embedded, shadow), classification framework (consumer-facing, regulator-watched, employment-related, privileged-content), risk-tier assignment, legal-review cadence by tier, and the integration with broader AI inventory (typically owned by Risk and Compliance, but with Legal classification overlay). The base data structure for all subsequent work.

Module 3. AI vendor-contracting playbook

Build the AI vendor-contracting playbook: data-isolation terms (no training on bank data), audit-rights terms, model-disclosure terms (which model, which version), upstream-vendor disclosure (when model provider depends on infrastructure vendor), incident-notification terms (security, performance, bias), termination-for-AI-non-conformance terms, regulator-cooperation terms, and exit terms. Redline positions for OpenAI, Anthropic, Google, Microsoft, AWS, in-house, and open-source. The playbook that standardises vendor negotiations.

Module 4. Privilege and work-product architecture

Build the privilege and work-product architecture: AI tools in legal research and their privilege implications, attorney-client privilege preservation in AI-augmented work, work-product protection in AI-augmented work, third-party-disclosure concerns when AI vendor processes privileged content, in-house deployment patterns that preserve privilege, and the privilege training for legal team.

Module 5. Regulator-engagement model

Build the regulator-engagement model: prepared-position library for Fed/OCC/FDIC/CFPB AI questions, examiner-meeting preparation framework, MRA/MRIA-response framework, formal-investigation handling, and the integration with broader regulator-relationship management. The model that converts AI-related regulator inquiry from threat to managed risk.

Module 6. Litigation-readiness framework

Build the litigation-readiness framework: model-version preservation, training-data preservation, inference-log preservation, retention schedule aligned to litigation hold practice, e-discovery vendor capability assessment for AI evidence, 30(b)(6) deposition preparation framework, expert-witness panel building, and the integration with broader litigation management.

Module 7. Consumer protection and UDAAP

Build the consumer-protection and UDAAP framework for bank AI: CFPB UDAAP application to AI in credit decisions, AI in marketing, AI in servicing, AI in collections, adverse-action notice requirements for AI decisions (ECOA, FCRA), disparate-impact analysis posture, and the consumer-complaint-aggregation feedback loop. The framework that prevents the enforcement event.

Module 8. Employment-law AI considerations

Build the employment-law AI framework: AI in hiring (EEOC + state rules including NYC Local Law 144, Illinois AI Video Interview Act, California ABs), AI in performance evaluation, AI in workforce decisions, AI in compensation analysis, AI in employee monitoring, and the integration with broader employment-law and HR. The framework for bank workforce AI deployments.

Module 9. Privacy and data protection for AI

Build the AI privacy and data-protection framework: GLBA application to AI data flows, state privacy laws (CCPA/CPRA, CDPA, CPA, UCPA, CTDPA, ICDPA, OCPA, TDPSA, FDBR, MTCDPA, etc) and the consumer-rights model for AI, cross-border data flows for bank AI, GDPR for EU operations, PIPEDA for Canadian operations, and the integration with broader privacy. The framework that satisfies multi-jurisdictional privacy review.

Module 10. Board reporting on AI legal risk

Build the board-reporting cadence on AI legal risk: what the board needs (not what the board wants), the standing AI legal-risk dashboard (inventory size by risk tier, vendor-contract posture, regulator-engagement status, litigation status, consumer-complaint trend), the deep-dive cadence by AI use case, and the integration with broader risk-committee reporting. The cadence that meets board expectations.

Module 11. Outside counsel and law-firm AI deployment

Build the outside-counsel AI deployment model: outside-counsel AI use disclosure framework, outside-counsel privilege agreement, outside-counsel cost-modelling for AI-augmented work, panel-counsel selection criteria including AI capability, and the integration with broader outside-counsel management. The framework that captures AI productivity gains in outside spend.

Module 12. Your 10-week build plan

Week-by-week plan with weekly deliverables. Weeks 1-2: banking AI legal landscape + AI inventory and legal-risk classification. Weeks 3-4: vendor-contracting playbook + privilege architecture. Weeks 5-6: regulator-engagement model + litigation-readiness framework. Weeks 7-8: consumer-protection/UDAAP + employment-law AI. Weeks 9-10: privacy + board reporting + outside-counsel AI deployment. Deliverable: shippable banking legal AI governance capability.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers the landscape.

Modules 2 to 6 produce AI inventory, vendor playbook, privilege architecture, regulator engagement, and litigation readiness.

Modules 7 to 9 cover UDAAP, employment-law AI, and privacy.

Module 10 covers board reporting.

Module 11 covers outside-counsel AI deployment.

Module 12 covers the 10-week build plan.

What you get with this course

The 12-module course delivered as text plus downloadable templates.
Templates and redline positions for AI vendor contracts, AI inventory framework, privilege architecture, regulator-engagement playbook, litigation-readiness checklist, UDAAP framework, employment-law AI framework, AI privacy framework, board-reporting dashboard, outside-counsel AI model.
A hand-built implementation playbook generated for your specific legal team and bank.
Three worked examples of banking legal AI governance capabilities at peer banks.
Scripted talking points for the GC and Risk Committee Chair engagement.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: AI inventory + legal-risk classification scaffold drafted.

Week 4: Vendor playbook + privilege architecture delivered.

Week 8: Regulator engagement + litigation readiness operational.

Week 10: Full governance capability in operation.

Before and after

Before

Your legal team handles AI issues ad hoc. AI vendor contracts vary in quality. Privilege architecture for AI-augmented legal work is unclear. Regulator AI questions are handled reactively. Litigation-preservation practice has not caught up to AI evidence.

After

A shippable banking legal AI governance capability is in operation. AI inventory with legal-risk classification, vendor-contracting playbook with standard redline positions, privilege architecture, regulator-engagement model, litigation-readiness framework, UDAAP framework, employment-law AI framework, AI privacy framework, board-reporting cadence, outside-counsel AI model are all designed.

What happens if you do not address this

Banking legal teams without the AI governance capability cede authority to Risk/Compliance and Engineering on issues that are properly Legal. Regulator AI inquiry is increasing in frequency and severity.

Who it is for

For Chief Counsel, Managing Counsel, Senior Counsel, and Deputy GC at banks and bank-adjacent regulated financial services firms.

Who this is NOT for. Outside counsel without in-house deployment authority. Counsel at non-financial-services firms (regulator overlap is materially different). Pure litigation roles without governance scope.

How it arrives

Text-based course via LMS, plus downloadable templates and redline positions and the hand-built implementation playbook.

Time investment. Roughly 18 hours of reading and 80 to 160 hours of legal-team effort across the 10-week build.

Why $199 is the right number

External banking legal AI counsel charges $1,000-$2,500/hour. AmLaw 100 firms (Davis Polk, Sullivan Cromwell, Cravath, Cleary, Wachtell, Skadden, Latham, Sidley, Kirkland, Paul Weiss, etc) build comparable governance frameworks for $500K-$2M. Specialist firms (Hogan Lovells privacy + AI, Wilmer, Gibson Dunn AI) charge $300K-$1M. $199 buys the focused playbook plus the implementation document for your specific legal team.

FAQ

Will this replace AmLaw counsel for AI legal work?

No. It builds the in-house capability that converts AmLaw spend from build to specialised matters.

What if my bank is community-bank-sized (not regional or national)?

Modules 1-5 apply largely unchanged; modules 6 and 10 scale to community-bank cadence.

Does this cover insurance-affiliated bank AI considerations?

Module 7 covers state DOI overlap for affiliated insurance products.

What about non-bank affiliates (broker-dealer, RIA, mortgage)?

Modules 1, 5, and 7 cover non-bank affiliate considerations.

What is in the implementation playbook for me specifically?

AI inventory framework tailored to your bank's AI deployment pattern; vendor-contracting playbook matched to your vendor mix; a 10-week build plan.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.