Certified AI Governance Officer (CAIGO) Training
Brit Certifications and Assessments (BCAA) is a leading UK based certification body. This CB is formed to address the gap in the industry in IT and IT Security sector. The certification body leads in IT security and IT certifications, and in particular doing it with highly pragmatic way.
BCAA UK works in hub and spoke model across the world.
R A C E Framework
The Read - Act - Certify - Engage framework from Brit Certifications and Assessments is a comprehensive approach designed to guarantee optimal studying, preparation, examination, and post-exam activities. By adhering to this structured process, individuals can be assured of mastering the subject matter effectively.
Commencing with the "Read" phase, learners are encouraged to extensively peruse course materials and gain a thorough understanding of the content at hand. This initial step sets the foundation for success by equipping candidates with essential knowledge and insights related to their chosen field.
Moving on to the "Act" stage, students actively apply their newfound expertise through practical exercises and real-world scenarios. This hands-on experience allows them to develop crucial problem-solving skills while reinforcing theoretical concepts.
“Certify” stage is where you will take your examination and get certified to establish yourself in the industry. Now “Engage” is the stage in which BCAA partner, will engage you in Webinars, Mock audits, and Group Discussions. This will enable you to keep abreast of your knowledge and build your competence.
Certified AI Governance Officer
AI governance has moved from a specialist compliance topic to a core board responsibility. C-suite leaders now need a coherent governance model that connects purpose, strategy, information, technology, data, risk, ethics, accountability, performance, and assurance.
This 16-module programme equips senior leaders to establish and oversee an enterprise AI governance system that is aligned with corporate governance and measurable through board-level performance and risk indicators.
Board & C-Suite Orientation: Governance is framed through governing-body obligations, executive decision rights, risk appetite, value creation, oversight, accountability, and assurance.
Integrated Standards Architecture: ISO 37000 provides the organisational governance foundation; ISO/IEC 38500 governs IT; ISO/IEC 38505 governs data; ISO/IEC 38507 addresses AI governance implications.
Information Governance Model: Information is treated as a strategic asset connecting data, records, knowledge, privacy, security, analytics, AI, legal obligations, and business value.
Measurable Governance: KPIs, KRIs, dashboards, board packs, balanced scorecards, thresholds, escalation rules, and trend analysis convert governance into executive intelligence.
Target Audience
Board members, CEOs, CIOs, CTOs, CDOs, CROs, CISOs, Chief AI Officers, AI Governance Officers, Chief Data Officers, General Counsel, Compliance leaders, Internal Audit leaders, Enterprise Risk leaders, and senior transformation executives.
Executive Curriculum
Module 1: Enterprise AI Governance & the C-Suite Mandate
1. AI governance as a governing-body and enterprise value responsibility
2. Distinguishing governance, management, assurance, and operational control
3. Board accountability, executive sponsorship, and delegated authorities
4. AI governance within enterprise risk management and corporate strategy
5. Stakeholder trust, legitimacy, reputation, and sustainable value creation
6. Executive maturity assessment and governance ambition setting
Module 2: ISO 37000 — Governance of Organizations
1. Purpose, value generation, strategy, oversight, accountability, and viability
2. Governing-body composition, competence, independence, and ethical leadership
3. Stakeholder engagement and balancing competing stakeholder expectations
4. Data and decisions as a core principle of organisational governance
5. Risk governance, social responsibility, and long-term organisational viability
6. Applying ISO 37000 principles to enterprise AI governance
Module 3: Enterprise Information Governance Model
1. Information as a strategic enterprise asset and source of organisational value
2. Information governance principles: authority, accountability, integrity, protection, availability, retention, and disposition
3. Connecting data, records, knowledge, privacy, security, analytics, and AI governance
4. Information ownership, stewardship, classification, lineage, and provenance
5. Information lifecycle governance from creation to defensible disposition
6. C-suite information governance operating model and decision rights
Module 4: ISO/IEC 38500 — Governance of IT
1. Executive interpretation of the Evaluate–Direct–Monitor governance model
2. Responsibility, Strategy, Acquisition, Performance, Conformance, and Human Behaviour
3. Governance of AI investments within the enterprise technology portfolio
4. Value, benefits realisation, technology dependency, and strategic alignment
5. Board oversight of IT/AI performance, conformance, resilience, and capability
6. Integrating ISO/IEC 38500 with ISO 37000 and enterprise governance
Module 5: ISO/IEC 38505 — Governance of Data
1. Governing-body responsibility for effective, responsible, and ethical data use
2. Data value, data risk, constraints, and decision quality
3. Applying the data accountability map across collection, storage, reporting, decision, distribution, and disposal
4. Data quality, provenance, lineage, ownership, and trustworthy AI inputs
5. Board oversight of privacy, data security, regulatory constraints, and data ethics
6. Executive data governance scorecard and accountability framework
Module 6: ISO/IEC 38507 — Governance Implications of AI
1. Purpose and scope of ISO/IEC 38507 for members of governing bodies
2. Governance implications of AI opportunities, risks, and responsibilities
3. Human accountability for AI-enabled and AI-influenced decisions
4. Policy alignment, organisational implications, and delegated authority
5. Risk appetite, oversight, monitoring, and assurance for AI use
6. Executive implementation blueprint for ISO/IEC 38507
Module 7: AI Governance Operating Model & Decision Rights
1. Centralised, federated, and decentralised governance operating models
2. Board, executive committee, AI governance council, and management accountabilities
3. RACI/RAPID decision rights for AI investment, risk acceptance, deployment, and retirement
4. AI portfolio inventory, classification, ownership, and governance tiers
5. Policy hierarchy, standards, procedures, exceptions, and waiver governance
6. Three Lines Model for AI governance, risk, compliance, and assurance
Module 8: Strategic AI Portfolio Governance & Value Realisation
1. Aligning AI investments with enterprise purpose, strategy, and value agenda
2. Portfolio prioritisation using strategic fit, value, risk, readiness, and dependency
3. AI business cases, total cost of ownership, opportunity cost, and benefits tracking
4. Investment gates, stage reviews, funding decisions, and termination criteria
5. Governance of third-party, foundation-model, and strategic technology dependencies
6. Board oversight of realised value versus expected value
Module 9: Enterprise AI Risk Appetite & Risk Governance
1. AI risk taxonomy across strategic, operational, legal, ethical, security, data, and reputational domains
2. Board-approved risk appetite statements and executive tolerance thresholds
3. Risk aggregation, concentration risk, interconnected risk, and systemic exposure
4. Risk acceptance authority, exceptions, escalation, and residual risk ownership
5. Scenario analysis, stress testing, emerging risk, and horizon scanning
6. Integration with enterprise risk management and board risk committees
Module 10: AI Governance KPIs — Performance & Value
1. Design principles for executive KPIs and outcome-oriented governance measures
2. AI portfolio performance, value realisation, adoption, and strategic alignment metrics
3. Governance process KPIs: review cycle time, exception closure, training, and control effectiveness
4. Data, model, compliance, assurance, and stakeholder confidence indicators
5. Targets, baselines, trend analysis, benchmarking, and metric ownership
6. Linking governance KPIs to executive incentives and enterprise objectives
Module 11: AI Governance KRIs — Early Warning & Risk Escalation
1. Design principles for board-relevant KRIs and leading indicators
2. Risk indicators for model drift, bias, security, privacy, data quality, and third parties
3. Thresholds, triggers, breach levels, escalation pathways, and risk acceptance
4. Risk velocity, risk concentration, control failure trends, and emerging exposure
5. Connecting KRIs to risk appetite and executive decision thresholds
6. Board risk reporting and treatment accountability
Module 12: Executive Dashboards & Board Reporting
1. Designing concise board dashboards for decision-making rather than operational reporting
2. Top-level views of AI portfolio, value, risk, compliance, incidents, and assurance
3. Red–Amber–Green thresholds, trends, heat maps, exceptions, and narrative insight
4. Dashboard governance: data quality, ownership, frequency, and challenge process
5. Board pack structure, executive commentary, decisions required, and action tracking
6. Tailoring reporting for Board, Risk Committee, Audit Committee, and Executive Committee
Module 13: AI Governance Balanced Scorecard
1. Strategy translation through Financial/Value, Stakeholder, Internal Governance, and Learning/Capability perspectives
2. Defining objectives, measures, targets, initiatives, and owners for each perspective
3. Integrating KPIs, KRIs, compliance indicators, and assurance findings
4. Linking governance maturity to strategic outcomes and sustainable value
5. Cascade from enterprise scorecard to business unit and AI portfolio scorecards
6. Quarterly executive review and strategic corrective action
Module 14: Assurance, Audit & Governance Effectiveness
1. Board assurance map across management, risk/compliance, internal audit, and external assurance
2. Governance effectiveness reviews against ISO 37000 and ISO/IEC 38500/38505/38507
3. Evidence-based assurance of policy implementation, risk treatment, and decision controls
4. Internal audit coverage of AI governance, information governance, and management systems
5. Issue severity, root cause, remediation accountability, and closure validation
6. Executive assurance statements and governance attestation
Module 15: Culture, Ethics, Stakeholder & Regulatory Stewardship
1. Tone from the top and culture as a governance control
2. Ethical principles, responsible AI, human oversight, and contestability
3. Executive management of stakeholder expectations and societal impact
4. Regulator engagement, evidence readiness, and transparent governance narratives
5. Governance of workforce impact, competence, incentives, and responsible adoption
6. Reputation, trust, crisis communication, and board accountability
Module 16: Chief AI Governance Officer Executive Playbook
1. Defining the CAIGO mandate, authority, reporting line, charter, and independence
2. Building a 90-day executive agenda and 12–24 month governance roadmap
3. Establishing board-approved governance architecture, policies, and decision rights
4. Launching KPI/KRI dashboards and the AI Governance Balanced Scorecard
5. Presenting the enterprise AI governance posture, priorities, and assurance to the Board
6. Capstone: Board simulation, governance crisis scenario, and strategic action plan
Exams
The Training is followed by Subjective exam for three hours.
Contact
BRIT CERTIFICATIONS AND ASSESSMENTS (UK),
128 City Road, London, EC1V 2NX,
United Kingdom enquiry@bcaa.uk
+44 203 476 4509
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