Skip to main content
Image coming soon

DAT6132 Mastering ISO 42001 for Senior FP&A Analysts in Global Services

$199.00
Adding to cart… The item has been added

A tailored course, built for your situation

Mastering ISO 42001 for Senior FP&A Analysts in Global Services

From compliance intent to working AI governance artefacts in record time

$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.
Spending too many cycles translating AI governance mandates into financial impact assessments and control packages

The situation this course is for

Compliance frameworks like ISO 42001 are creating new reporting burdens, but FP&A teams lack a structured way to convert high-level AI risk mandates into audit-ready financial controls and Statements of Applicability without iterative delays or rework.

Who this is for

Senior FP&A Analyst at a global services firm navigating rising AI governance demands with limited internal templates or cross-functional playbooks

Who this is not for

Entry-level analysts, standalone auditors, or practitioners outside finance functions who don't own cross-functional control translation

What you walk away with

  • Produce a complete ISO 42001 Statement of Applicability aligned to FP&A risk metrics in under 3 weeks
  • Cut review cycles by 50% using pre-validated control mapping templates
  • Turn AI governance requirements into structured financial risk narratives stakeholders accept on first submission
  • Build internal muscle for handling DORA, NIS2, or MiCA ripple effects through existing ISO 42001 workflows
  • Position FP&A as the launchpad for firm-wide AI governance execution

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 42001's Core Principles
Foundational structure of ISO 42001 and its relevance to financial controls in AI governance.
12 chapters in this module
  1. Introduction to AI governance and ISO 42001 alignment
  2. Key differences between ISO 27001 and ISO 42001 frameworks
  3. Role of FP&A in AI risk classification and documentation
  4. Mapping AI use cases to financial exposure categories
  5. Identifying high-risk AI systems under Article 5 criteria
  6. Establishing governance roles within finance teams
  7. Integrating ethical considerations into risk assessments
  8. Using ISO 42001 to support regulatory readiness
  9. Linking AI governance to existing SOX and audit frameworks
  10. Documenting AI system lifecycles for compliance
  11. Creating a governance-first mindset in FP&A
  12. Aligning ISO 42001 with internal audit expectations
Module 2. Defining Organizational Context for AI Governance
Scoping AI governance within the financial and operational boundaries of a global services firm.
12 chapters in this module
  1. Assessing organizational scope for AI governance
  2. Identifying internal and external stakeholders
  3. Determining regulatory drivers for AI compliance
  4. Evaluating current AI use across business units
  5. Documenting dependencies between AI systems and financial reporting
  6. Setting governance boundaries for AI systems
  7. Establishing communication channels for AI risks
  8. Integrating AI governance with ERM frameworks
  9. Defining accountability for AI risk ownership
  10. Creating a register of AI systems with financial impact
  11. Prioritizing AI systems by financial materiality
  12. Mapping AI governance to corporate strategy
Module 3. Establishing Leadership and Commitment
Securing executive buy-in and embedding AI governance into financial leadership practices.
12 chapters in this module
  1. Demonstrating leadership commitment to AI ethics
  2. Developing AI governance policies endorsed by CFO
  3. Assigning governance responsibilities within finance
  4. Communicating AI risk priorities across departments
  5. Integrating AI governance into performance metrics
  6. Ensuring adequate resource allocation for compliance
  7. Establishing oversight mechanisms for AI risks
  8. Linking AI governance to incentive structures
  9. Creating a culture of responsible AI use
  10. Reporting AI posture to senior leadership
  11. Maintaining leadership involvement in audits
  12. Updating governance policies annually
Module 4. Developing AI Risk Assessment Methodology
Creating a repeatable process to identify, classify, and prioritize AI risks with financial implications.
12 chapters in this module
  1. Designing a risk matrix specific to AI systems
  2. Classifying AI risks by financial impact severity
  3. Assessing likelihood of AI system failures
  4. Evaluating bias and fairness in financial models
  5. Measuring data quality impact on AI outputs
  6. Assessing model drift in forecasting algorithms
  7. Identifying third-party AI vendor risks
  8. Evaluating explainability gaps in credit decisions
  9. Quantifying reputational risks from AI misuse
  10. Benchmarking against industry AI risk profiles
  11. Linking risk ratings to internal audit thresholds
  12. Updating risk assessments quarterly
Module 5. Implementing AI Risk Treatment Plans
Developing targeted actions to mitigate high-priority AI risks identified in FP&A workflows.
12 chapters in this module
  1. Creating action plans for top AI risks
  2. Assigning owners for risk mitigation tasks
  3. Setting timelines for AI control implementation
  4. Tracking progress on risk treatment activities
  5. Evaluating effectiveness of AI risk controls
  6. Integrating controls into financial reporting
  7. Using AI monitoring tools for compliance
  8. Testing AI control effectiveness annually
  9. Adjusting risk treatment based on performance
  10. Documenting exceptions to risk treatment plans
  11. Escalating unresolved AI risks to leadership
  12. Maintaining records of risk treatment decisions
Module 6. Managing AI System Lifecycle Compliance
Ensuring AI systems are governed from design through decommissioning with financial controls.
12 chapters in this module
  1. Defining stages of AI system lifecycle
  2. Establishing governance checkpoints at each stage
  3. Ensuring ethical design principles in AI models
  4. Validating training data quality for financial models
  5. Conducting pre-deployment risk assessments
  6. Obtaining sign-off before AI system launch
  7. Monitoring AI performance in production
  8. Auditing AI model behavior quarterly
  9. Managing version updates and retraining
  10. Establishing decommissioning procedures
  11. Maintaining documentation throughout lifecycle
  12. Linking lifecycle stages to financial audits
Module 7. Building Data Governance for AI Systems
Strengthening data controls to ensure AI outputs are reliable and comply with financial standards.
12 chapters in this module
  1. Identifying critical data sources for AI models
  2. Ensuring data accuracy for financial forecasting
  3. Validating data lineage in AI pipelines
  4. Protecting sensitive financial data in AI systems
  5. Enforcing access controls on training data
  6. Assessing data bias in credit scoring models
  7. Documenting data quality metrics
  8. Monitoring data drift in real-time
  9. Establishing data reconciliation processes
  10. Auditing data governance annually
  11. Integrating data quality into model validation
  12. Reporting data issues to compliance teams
Module 8. Designing Human Oversight Mechanisms
Implementing human-in-the-loop controls to maintain accountability in automated financial processes.
12 chapters in this module
  1. Defining roles for human oversight
  2. Establishing thresholds for manual review
  3. Designing dashboards for AI performance
  4. Creating escalation paths for anomalies
  5. Training staff on AI oversight duties
  6. Conducting regular sampling of AI outputs
  7. Validating AI decisions in high-risk areas
  8. Documenting human intervention records
  9. Assessing effectiveness of oversight
  10. Updating oversight rules based on feedback
  11. Integrating oversight into audit trails
  12. Reporting oversight findings to leadership
Module 9. Ensuring Transparency and Explainability
Producing clear documentation and justification for AI-driven financial decisions.
12 chapters in this module
  1. Creating technical documentation for AI models
  2. Writing user-facing explanations of AI outputs
  3. Developing model cards for internal stakeholders
  4. Ensuring compliance with AI transparency laws
  5. Providing audit trails for AI decisions
  6. Documenting assumptions in forecasting models
  7. Explaining credit scoring factors to customers
  8. Reporting model performance metrics
  9. Updating documentation after model changes
  10. Linking explainability to internal reviews
  11. Training teams on AI communication
  12. Maintaining version-controlled documentation
Module 10. Conducting Internal Audits and Reviews
Preparing for compliance audits with structured evidence collection and gap remediation.
12 chapters in this module
  1. Planning annual AI governance audits
  2. Collecting evidence for ISO 42001 compliance
  3. Conducting gap assessments against requirements
  4. Interviewing team members on AI practices
  5. Reviewing documentation completeness
  6. Testing control effectiveness in production
  7. Reporting audit findings to management
  8. Tracking corrective action plans
  9. Validating remediation efforts
  10. Preparing for external certification
  11. Maintaining audit records
  12. Improving processes based on audit feedback
Module 11. Maintaining Continuous Improvement
Embedding feedback loops and updates to keep AI governance current and effective.
12 chapters in this module
  1. Establishing regular review cycles
  2. Collecting stakeholder feedback on AI systems
  3. Monitoring regulatory changes in AI laws
  4. Updating policies based on new requirements
  5. Reassessing risk profiles annually
  6. Revising control mappings as needed
  7. Implementing lessons from audit findings
  8. Tracking AI incidents and near-misses
  9. Benchmarking against peer organizations
  10. Updating training materials regularly
  11. Measuring maturity over time
  12. Reporting improvement metrics to leadership
Module 12. Achieving ISO 42001 Certification Readiness
Finalizing artefacts and preparing for external assessment and certification.
12 chapters in this module
  1. Confirming compliance with all clauses
  2. Compiling the Statement of Applicability
  3. Finalizing control implementation evidence
  4. Conducting pre-certification gap analysis
  5. Engaging external assessors
  6. Scheduling certification audit
  7. Preparing leadership for interviews
  8. Submitting documentation package
  9. Addressing assessor findings
  10. Obtaining certification decision
  11. Maintaining certification annually
  12. Celebrating organizational achievement

How this maps to your situation

  • From project initiation to completed ISO 42001 SoA
  • Integrating AI governance into FP&A risk workflows
  • Reducing audit rework through early control design
  • Positioning FP&A as lead on cross-functional AI compliance

Before vs. after

Before
Spending weeks reconciling AI governance mandates with financial reporting needs, often resulting in delayed or fragmented outputs.
After
Producing complete, audit-ready ISO 42001 Statements of Applicability in under three weeks, with direct alignment to FP&A risk frameworks.

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 90 minutes per module, designed for completion within a single weekend.

If nothing changes
Continuing without a structured approach risks prolonged review cycles, increased audit friction, and missed opportunities to position FP&A as the strategic owner of AI governance execution.

How this compares to the alternatives

Unlike generic compliance courses, this program is tailored to FP&A professionals needing to translate AI governance standards into financial controls , with templates and examples grounded in real-world services sector reporting.

Frequently asked

Who is this course designed for?
Senior FP&A Analysts in firms adopting ISO 42001 or facing AI governance mandates in financial reporting contexts.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover other standards besides ISO 42001?
Focus is on ISO 42001, but connections to SOX, DORA, and NIS2 are included where relevant to financial risk.
$199 one-time. Approximately 90 minutes per module, designed for completion within a single weekend..

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
!