A tailored course, built for your situation
Deeper command of AI governance frameworks in financial services
Master the architecture, standards, and enforcement models shaping trusted AI in regulated environments
$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.
The situation this course is for
Who this is for
Mid-tier data & AI practitioner in a regulated financial institution, operating as an individual contributor with increasing responsibility for governance, compliance, and cross-functional alignment on AI risk.
Who this is not for
Executives seeking board-level summaries, vendors building AI tools, or those outside financial services where regulatory context differs significantly.
What you walk away with
- Confidence to independently design and justify AI governance controls aligned to APRA, ISO 42001, and internal risk frameworks
- Ability to anticipate regulator and auditor questions and embed answers into framework design
- Templates and logic flows for policy-to-implementation translation, audit trails, and control evidence packaging
- Clear articulation of where financial services AI governance differs from generic frameworks
- Mental models to navigate trade-offs between innovation velocity and control rigor
The 12 modules (with all 144 chapters)
Module 1. The anatomy of a financial services AI governance framework
Break down real-world frameworks from top-tier banks and insurers into components: scope, principles, control layers, escalation paths, and review cycles.
12 chapters in this module
- What makes financial services AI governance different
- Core components of enforceable frameworks
- Mapping principles to technical controls
- Lifecycle stages and governance touchpoints
- Control ownership models: centralised vs embedded
- Where ISO 42001 applies, and where it doesn’t
- APRA CPS 234 and AI: control implications
- Integrating with model risk management
- Audit readiness by design
- Versioning and change control for policies
- Stakeholder alignment without consensus traps
- Framework documentation that serves multiple audiences
Module 2. Standards in practice: ISO, NIST, and internal policy alignment
Translate high-level standards into actionable control statements that work within existing risk and compliance infrastructure.
12 chapters in this module
- ISO 42001 control catalogue breakdown
- NIST AI RMF: mapping to operational workflows
- Gap analysis between standards and current state
- Internal policy hierarchies: from board mandate to team practice
- Control rationalisation across overlapping standards
- Exemption processes with audit trail
- Control testing protocols
- Evidence requirements per control type
- Automating evidence collection paths
- Version control for standard interpretations
- Tailoring templates to local risk appetite
- Crosswalking between frameworks
Module 3. Designing governance controls for high-risk AI use cases
Apply control patterns to common financial use cases: credit decisioning, fraud detection, customer segmentation, and operational automation.
12 chapters in this module
- Use case risk stratification model
- Credit decisioning: fairness and explainability controls
- Fraud detection: feedback loops and drift monitoring
- Customer segmentation: consent and data lineage
- Operational automation: fallback mechanisms
- Human-in-the-loop design patterns
- Thresholds for escalation and override
- Model documentation standards
- Bias testing protocols
- External vendor models: control inheritance
- Incident response playbooks
- Control validation methods
Module 4. Policy to artefact: turning intent into audit-ready outputs
Bridge the gap between governance policy and working documentation that satisfies internal audit and regulators.
12 chapters in this module
- Translating policy clauses into control statements
- Control register design
- Evidence matrix by control
- Model risk assessment templates
- Data provenance mapping
- Versioned decision logs
- Stakeholder sign-off workflows
- Change impact assessments
- Policy exception tracking
- Audit trail construction
- Packaging artefacts for review cycles
- Automated checklist generation
Module 5. Anticipating regulator and auditor scrutiny
Learn the recurring questions, evidence expectations, and reasoning gaps that lead to findings, and how to preempt them.
12 chapters in this module
- Common APRA review focus areas
- Internal audit testing patterns
- External auditor evidence requests
- Regulator questioning sequences
- Defining 'reasonable assurance' in practice
- Handling model drift findings
- Responding to control gaps
- Evidence sufficiency thresholds
- Temporal alignment of documentation
- Third-party model validation expectations
- Escalation timelines and ownership
- Lessons from enforcement actions
Module 6. Control ownership and cross-functional influence
Navigate organisational dynamics to establish clear ownership and drive compliance without direct authority.
12 chapters in this module
- Defining control owners vs process owners
- Escalation paths for unresolved risks
- Influencing data science teams
- Working with legal and compliance
- Engaging risk committees
- Facilitating cross-functional reviews
- Conflict resolution in control design
- Building credibility through consistency
- Communicating risk trade-offs
- Managing competing priorities
- Establishing governance rhythms
- Measuring control effectiveness
Module 7. Framework evolution and change management
Manage updates, versioning, and adoption of governance changes across teams and systems.
12 chapters in this module
- Change drivers: regulation, tech, incidents
- Impact assessment for framework updates
- Staged rollout strategies
- Training and adoption toolkits
- Feedback loops from implementers
- Version control for policies and controls
- Deprecation protocols
- Tracking implementation completeness
- Metrics for adoption success
- Updating audit and review schedules
- Managing legacy system exceptions
- Framework maturity models
Module 8. AI assurance and independent review
Prepare for and participate in assurance activities, from internal audits to external certifications.
12 chapters in this module
- Assurance scope and objectives
- Review vs audit: key differences
- Preparing for ISO certification
- Engaging external assessors
- Evidence pack assembly
- Responding to findings
- Corrective action planning
- Independent validation design
- Red teaming AI systems
- Scenario testing for edge cases
- Audit communication protocols
- Lessons from failed assurance cycles
Module 9. Vendor and third-party AI risk governance
Extend governance controls to external models, APIs, and platforms with limited visibility.
12 chapters in this module
- Third-party risk classification
- Due diligence checklists
- Contractual control levers
- Model card analysis
- API monitoring for drift
- Incident response coordination
- Right-to-audit provisions
- Performance benchmarking
- Fallback and exit strategies
- Vendor control validation
- Transparency request protocols
- Multi-vendor ecosystem management
Module 10. Incident response and governance escalation
Design and execute response protocols for AI incidents, from bias findings to operational failures.
12 chapters in this module
- Incident classification framework
- Escalation thresholds
- Response team composition
- Communication protocols
- Root cause analysis methods
- Remediation tracking
- Regulatory disclosure triggers
- Post-mortem documentation
- Pattern detection across incidents
- Updating controls based on incidents
- Reputation risk management
- Legal hold procedures
Module 11. Metrics that demonstrate governance effectiveness
Define and track KPIs that show control performance, adoption, and risk reduction, not just activity.
12 chapters in this module
- Activity vs effectiveness metrics
- Control testing pass rates
- Incident recurrence trends
- Time-to-remediate findings
- Policy exception volumes
- Audit finding severity trends
- Stakeholder confidence surveys
- Adoption rate by team
- Risk exposure reduction
- Benchmarking against peers
- Reporting cadence and format
- Visualising governance maturity
Module 12. From practitioner to governance owner
Transition from implementing others’ frameworks to owning and evolving your own, with confidence, clarity, and authority.
12 chapters in this module
- Defining your governance philosophy
- Building a personal body of work
- Creating repeatable design patterns
- Mentoring others in control design
- Presenting trade-offs to leadership
- Influencing strategy discussions
- Developing your point of view
- Contributing to industry practice
- Establishing recognition internally
- Balancing innovation and control
- Managing scope creep
- Sustaining technical depth
How this maps to your situation
- Designing a new AI governance framework from scratch
- Improving an existing framework under audit pressure
- Taking ownership of governance after a team restructure
- Preparing for external certification or regulator review
Before vs. after
Before
Relying on ad-hoc coordination, inherited templates, and reactive responses to audit findings or policy updates.
After
Owning a coherent, defensible, and evolving AI governance framework with clear control logic, evidence paths, and stakeholder alignment.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- 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 3-4 hours per module, designed to be completed over 6-8 weeks with applied work between modules.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level strategy decks, this course delivers technical precision on control design, audit readiness, and financial services regulatory context, written for practitioners who own outcomes.
Frequently asked
Is this course specific to Australian financial regulation?
It uses APRA and Australian context as a primary reference but includes crosswalks to global standards like ISO and NIST, making it applicable to multinational institutions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an audit?
Yes, by teaching you how to build audit-ready artefacts from the start, with templates and logic that anticipate common findings.
$199 one-time. Approximately 3-4 hours per module, designed to be completed over 6-8 weeks with applied work between modules..
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