A tailored course, built for your situation
Mastering GLBA for AI and Data Leaders in Financial Services
Turn compliance constraints into velocity advantages for AI and data teams.
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Compliance slowing down AI deployment?
The situation this course is for
AI initiatives stall when governance feels like a gatekeeper rather than an enabler. Teams waste cycles rewriting documentation, revisiting control mappings, or reworking models to meet retrofitted compliance checks.
Who this is for
Senior technical leader in financial services driving AI and data engineering, accountable for compliance alignment without sacrificing delivery speed.
Who this is not for
Entry-level analysts, auditors without technical build experience, or consultants outside financial services.
What you walk away with
- Produce GLBA-compliant AI governance documentation in half the time
- Ship working compliance artefacts alongside model deployments
- Re-use control mappings across AI projects without rework
- Reduce review cycles between legal, risk, and engineering teams
- Build internal credibility as the go-to practitioner for compliant AI velocity
The 12 modules (with all 144 chapters)
Module 1. GLBA Core Requirements in AI Context
Break down GLBA’s Safeguards Rule and privacy provisions as they apply to ML data flows, model inference, and customer data handling in production systems.
12 chapters in this module
- GLBA scope for AI systems
- Identifying covered data
- Data lifecycle mapping
- Risk assessment design
- Encryption standards
- Access control logic
- Third-party vendor risk
- Incident response planning
- Customer notice requirements
- Opt-out mechanism design
- Data minimization rules
- Retention policy alignment
Module 2. Control Mapping for Data Engineering Pipelines
Apply GLBA controls directly to data engineering workflows in cloud and on-premise environments, ensuring compliance is built in, not bolted on.
12 chapters in this module
- Pipeline segmentation
- Schema-level tagging
- Compliance metadata layers
- Data lineage tracking
- Masking rules at ingestion
- Access logging
- Role-based filtering
- Audit trail design
- PII detection thresholds
- Anomaly alerting
- Pipeline documentation
- Version-controlled controls
Module 3. AI Model Development with GLBA Guardrails
Embed GLBA requirements into model development lifecycle, from training data selection to inference monitoring, without slowing innovation.
12 chapters in this module
- Training data provenance
- Bias assessment timing
- Model justification records
- Inference logging
- Customer data usage logs
- Explainability documentation
- Model risk tiering
- Approval workflows
- Model inventory standards
- Retraining compliance checks
- Decommissioning process
- Model audit package
Module 4. Automated Documentation Generation
Generate repeatable, auditable compliance artefacts directly from code and pipeline metadata, cutting manual documentation time by 70%.
12 chapters in this module
- Metadata harvesting tools
- Auto-generated SoA templates
- Control mapping output
- Policy compliance reports
- Audit trail packaging
- Versioned documentation
- Approval routing logic
- Cross-module linking
- Stakeholder-specific views
- DevOps integration
- Review cycle reduction
- Compliance debt tracking
Module 5. Cross-Team Alignment Without Delays
Align legal, risk, and engineering on a single compliance language and artefact set, eliminating rework loops and approval bottlenecks.
12 chapters in this module
- Shared control glossary
- Compliance narrative standard
- Stakeholder briefing packs
- Feedback integration process
- Change impact analysis
- Escalation paths
- Peer review cadence
- Compliance sprint planning
- Risk committee reporting
- Regulatory Q&A prep
- Cross-functional ownership
- Velocity tracking
Module 6. Third-Party Vendor Risk in AI Ecosystems
Extend GLBA compliance to cloud providers, data partners, and AI platforms with precise contractual and technical controls.
12 chapters in this module
- Vendor risk tiering
- Contractual language
- Data processing agreements
- Audit rights definition
- Compliance certification
- Subprocessor tracking
- API security standards
- Data residency checks
- Penetration testing
- Incident response SLAs
- Vendor exit planning
- Ongoing monitoring
Module 7. Incident Response for AI Systems
Design incident response protocols specific to AI model breaches, data leaks, or unauthorized access to customer insights.
12 chapters in this module
- Breach scenario planning
- Detection thresholds
- Notification timelines
- Customer communication
- Regulator notification
- Forensic data capture
- Model rollback
- Containment protocols
- Legal counsel loop
- Post-mortem process
- Compliance impact report
- Prevent recurrence
Module 8. Compliance Automation with Databricks and Snowflake
Leverage Databricks and Snowflake to automate data classification, access logging, and audit trail creation for GLBA compliance.
12 chapters in this module
- Unity Catalog setup
- Column-level security
- Audit log export
- Data profiling jobs
- Compliance dashboard
- Policy-as-code
- Data quality checks
- Access review automation
- Role assignment audit
- Alerting framework
- Pipeline documentation
- Cross-cloud consistency
Module 9. Executive Communication on Compliance Velocity
Communicate compliance progress not as risk avoidance, but as a source of development speed and trust-building.
12 chapters in this module
- Velocity metrics
- Risk reduction narrative
- Compliance ROI
- Leadership briefs
- Board-level summary
- Peer recognition
- Cross-department visibility
- Storytelling framework
- Quantified outcomes
- Trend reporting
- Success case studies
- Future roadmap
Module 10. Compliance Playbook for New AI Projects
Deploy a standardized, reusable compliance onboarding process for every new AI initiative, ensuring speed from day one.
12 chapters in this module
- Project intake form
- Risk tiering
- Control checklist
- Documentation templates
- Approval workflow
- Stakeholder mapping
- Timeline planning
- Resource allocation
- Milestone tracking
- Audit prep cycle
- Lessons learned
- Playbook iteration
Module 11. Maintaining Compliance Over Time
Ensure long-term compliance sustainability through automated monitoring, periodic reviews, and adaptive control updates.
12 chapters in this module
- Annual review process
- Control effectiveness
- Update triggers
- Version control
- Change management
- Personnel turnover
- Regulatory updates
- Audit readiness
- Stakeholder feedback
- Compliance KPIs
- Maturity assessment
- Continuous improvement
Module 12. Scaling Across Financial Services Use Cases
Apply the GLBA compliance velocity framework to credit risk, fraud detection, customer personalization, and other core banking AI workloads.
12 chapters in this module
- Credit model compliance
- Fraud detection rules
- Personalization limits
- Call center AI
- Wealth management
- Commercial banking
- Mortgage processing
- Anti-money laundering
- Behavioral analytics
- Customer segmentation
- Cross-selling rules
- Ethical boundaries
How this maps to your situation
- New AI project kickoff
- Regulatory audit preparation
- Third-party vendor onboarding
- Post-breach response
Before vs. after
Before
Manual, rework-heavy compliance processes slow down AI deployment and create friction across teams.
After
Automated, repeatable compliance delivery that accelerates AI projects and strengthens cross-functional trust.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters total)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 4 hours per module, designed for integration into active project work.
If nothing changes
Continuing with ad-hoc compliance approaches risks delayed AI deployments, regulatory scrutiny, and missed opportunities to build trusted systems.
How this compares to the alternatives
Unlike generic compliance training, this course is tailored to AI and data engineering leaders in financial services who need to move fast without breaking rules. It delivers actionable frameworks, not just theory.
Frequently asked
Who is this course for?
Senior AI and data engineering leaders in financial services responsible for deploying compliant AI systems at speed.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover other regulations like SOX or CCPA?
The focus is GLBA, but the framework can be adapted to other regulations.
$199 one-time. Approximately 4 hours per module, designed for integration into active project work..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours