Board Oversight of AI in the Caribbean: The Questions Directors Must Ask (and the Evidence They Should Demand) | Dawgen TRUST™

 

Dawgen TRUST™ Series

AI is no longer an “innovation topic.” It is increasingly a core operating capability—embedded in customer service, credit and fraud decisioning, HR workflows, compliance monitoring, forecasting, cybersecurity tooling, and vendor platforms. As AI becomes operational infrastructure, it becomes a board governance responsibility.

Boards and audit committees do not need to understand model architecture to govern AI effectively. What they need is clarity on four things:

1. Where AI is used and what it impacts (people, money, compliance, reputation)

2. Who owns it (accountability and decision rights)

3. How it is controlled (risk controls, monitoring, escalation, human override)

4. What evidence exists (audit-ready documentation and assurance)

In the Caribbean, oversight requirements are amplified by:

• compressed reputational risk in small markets,

• multi-territory complexity,

• lean operating teams,

• heavy reliance on third-party AI and vendor platforms,

• rising expectations from partners, lenders, regulators, and customers.

This article provides a practical board-ready guide to AI oversight using the Dawgen TRUST™ Framework, including:

• a director-level question set,

• evidence packs boards should request,

• governance cadence and reporting templates,

• the “red flags” that indicate unmanaged exposure,

• a 90‑day roadmap to become board-ready.

1) AI Oversight Is Now Part of Fiduciary Duty

Directors are responsible for oversight of material risks and strategic resilience. AI creates material risk because it can:

• automate decisions,

• influence financial outcomes,

• create customer harm,

• expose the organisation to regulatory scrutiny,

• introduce new cyber threat pathways,

• create operational fragility through vendor dependence.

If AI is influencing outcomes in high-impact areas (Tier 1 use cases), then oversight belongs in:

• board risk committees,

• audit committees, and/or

• technology/digital committees (where they exist).

The goal is not to slow AI. The goal is governance that enables confident scale.

2) The Board’s Role vs Management’s Role

Boards are accountable for oversight, not execution.

Board responsibilities:

• ensure a governance framework exists,

• confirm high-impact AI use cases are visible and controlled,

• require evidence of controls and monitoring,

• ensure accountability is assigned,

• ensure incident response readiness,

• validate that vendor risks are managed,

• ensure strategy aligns to risk appetite.

Management responsibilities:

• implement controls, policies, and operating procedures,

• maintain evidence packs, monitoring dashboards, and change logs,

• run testing and periodic assurance,

• execute training and awareness programs,

• manage vendor contracts and due diligence.

Boards should demand clarity and evidence; management must deliver it.

3) Introduce Tiering to Boards: “Not All AI Is Equal”

One reason board conversations become unproductive is that AI is discussed as one thing.

Dawgen TRUST™ recommends tiering AI use cases:

• Tier 1 (High-impact): affects people, money, compliance, or major trust outcomes

• Tier 2 (Material): meaningful operational impact but lower direct harm exposure

• Tier 3 (Low-impact): productivity and internal assistance with limited risk

Board oversight should focus primarily on Tier 1.
That keeps oversight practical and proportional.

4) The Dawgen TRUST™ Board Oversight Model

Boards can govern AI using the same structure they use for other risk domains—by requiring evidence across five pillars:

T — Transparency & Explainability

• Do we know where AI is used?

• Can we explain high-impact decisions?

R — Risk & Controls

• What could go wrong?

• What prevents/detects harm?

• Are controls tested?

U — Use-Case Governance

• Who approves and owns AI use cases?

• What are prohibited uses?

S — Security & Data Protection

• Are AI systems secure and privacy-aligned?

• What’s our incident response posture?

T — Testing & Assurance

• Do we have validation, drift monitoring, and assurance evidence?

• Is AI audit-ready?

This becomes the board lens for oversight.

5) The Director Question Set: What to Ask (and Why)

Here is a board-ready question set designed to drive useful answers—not technical debates.

A) Visibility and materiality

1. Where is AI used today (including vendor AI and embedded AI features)?

2. Which use cases are Tier 1 (high impact) and why?

3. What decisions does AI influence in customer outcomes, financial outcomes, or compliance outcomes?

Why it matters: You cannot govern what you cannot see.

B) Accountability and governance

4. Who is accountable for each Tier 1 AI use case (business owner)?

5. Who is accountable for technical integrity and access controls (IT/data owner)?

6. Who is accountable for risk, compliance, and defensibility (risk owner)?

7. What is the approval process for deploying or materially changing AI?

Why it matters: AI risk is unmanaged when ownership is unclear.

C) Controls and safe operation

8. What controls exist to prevent harmful outcomes (human review, overrides, escalation)?

9. How do we ensure data quality and prevent AI from using inappropriate data?

10. How do we handle exceptions and edge cases?

11. What is our policy for staff using public AI tools with company/customer data?

Why it matters: Controls translate governance into reality.

D) Transparency, fairness, and customer impact

12. Can we explain AI-driven decisions to customers and employees in plain language?

13. Where could bias/unfair outcomes occur, and what testing do we perform?

14. What recourse exists for customers/employees to challenge an AI-influenced decision?

Why it matters: In the Caribbean, perceived unfairness becomes reputational risk quickly.

E) Security and resilience

15. What are our top AI security risks (prompt injection, deepfake fraud, vendor exposure)?

16. Do we have AI-specific incident response procedures and tabletop exercises?

17. How do we monitor drift and detect failures early?

Why it matters: AI increases attack surface and operational fragility.

F) Vendor governance

18. Which Tier 1 AI systems are vendor-supplied?

19. Do contracts include audit rights, incident reporting timelines, and change notifications?

20. Do we have an exit/portability plan if a vendor fails?

Why it matters: Vendor AI risk becomes enterprise risk.

G) Evidence and assurance

21. What evidence pack can management provide today for each Tier 1 AI use case?

22. When was the last validation, control test, or assurance review performed?

23. What were the results and corrective actions?

Why it matters: Oversight requires proof, not confidence statements.

6) The Evidence Packs Boards Should Demand

For every Tier 1 AI system, boards should request a concise AI Assurance Pack containing:

Minimum pack contents

• AI register entry and tier rating

• use-case rationale and approvals

• ownership model (RACI)

• data flow diagram and privacy alignment summary

• risk and controls matrix (what could go wrong + controls)

• testing and validation results

• monitoring dashboard (drift, error rates, complaints)

• change log (versions, vendor updates)

• incident response plan and escalation path

• vendor due diligence summary + contract control clauses

• recourse process for customer/employee disputes (if applicable)

Boards should not accept “it’s handled.” Boards should require evidence.

7) Red Flags: Signs AI Governance Is Weak

Boards should treat the following as governance red flags:

• No AI register exists (unknown AI exposure)

• AI is “owned by IT” with no business accountability

• High-impact systems have no evidence pack or testing records

• Vendor AI contracts lack audit rights and change notifications

• No monitoring exists post go-live

• No incident response tabletop exercises involving AI scenarios

• Staff use public AI tools with sensitive data without controls

• Repeated complaints or disputes without structured analysis

• Models or AI features change without governance approval

These red flags indicate unmanaged risk—and should trigger immediate remediation.

8) Board Reporting Cadence: What Good Oversight Looks Like

Boards need a lightweight but consistent reporting cycle.

Recommended cadence

• Monthly (management-level): monitoring dashboards for Tier 1 systems

• Quarterly (board-level): AI oversight report summary + exceptions + incidents + remediation progress

• Annually (board-level): enterprise AI governance review + risk appetite alignment + assurance plan

What the board report should include

• Tier 1 AI inventory and changes

• key risk indicators (KRIs) and drift indicators

• incidents, near misses, and root causes

• vendor changes and updates

• assurance/testing results and remediation status

• upcoming AI deployments and approvals required

This makes oversight predictable and structured—not reactive.

9) A 90‑Day Roadmap to Become “Board‑Ready” on AI

First 30 days: Establish visibility and ownership

• build the AI register (including vendor AI)

• tier use cases and identify Tier 1 systems

• assign owners and approval gates

• identify documentation gaps for evidence packs

Days 31–60: Build evidence and controls

• create AI Assurance Packs for Tier 1 systems

• validate key controls (human review, logging, overrides)

• perform baseline testing and fairness review where relevant

• harden vendor controls (audit rights, change governance)

Days 61–90: Operationalise monitoring and assurance

• implement drift monitoring and monthly dashboards

• conduct AI incident tabletop exercise

• formalise quarterly assurance cadence

• publish board-level AI governance reporting template

This approach delivers governance maturity quickly while keeping innovation moving.

Moving Forward: The Dawgen Global Advantage

Dawgen Global helps boards and executive teams govern AI with confidence—without turning governance into bureaucracy. Using the Dawgen TRUST™ Framework, we help you build AI oversight that is:

• board-friendly and evidence-based,

• audit-ready,

• regionally relevant,

• aligned to enterprise risk management,

• practical for lean teams,

• and ready to scale.

Next Step: Request a Proposal

If your organisation’s board needs a clear AI oversight posture—or you want to prepare Tier 1 AI systems for audit, regulator scrutiny, partner due diligence, or reputational protection—Dawgen Global can help.

📩 Request a proposal: [email protected]
💬 WhatsApp Global: 15557959071

Share your sector, territories, and top AI use cases. We will propose a board-ready AI governance and assurance roadmap aligned to your risk exposure and strategic objectives.

About Dawgen Global

Dawgen Global is one of the top accounting and advisory firms in Jamaica and the Caribbean, offering integrated multidisciplinary services in audit, tax, advisory, risk assurance, cybersecurity, and digital transformation. Through our borderless, high-quality delivery methodology, we help organisations deploy AI responsibly—embedding governance, controls, and audit-ready assurance that builds trust and protects long-term value.

“Embrace BIG FIRM capabilities without the big firm price at Dawgen Global, your committed partner in carving a pathway to continual progress in the vibrant Caribbean region. Our integrated, multidisciplinary approach is finely tuned to address the unique intricacies and lucrative prospects that the region has to offer. Offering a rich array of services, including audit, accounting, tax, IT, HR, risk management, and more, we facilitate smarter and more effective decisions that set the stage for unprecedented triumphs. Let’s collaborate and craft a future where every decision is a steppingstone to greater success. Reach out to explore a partnership that promises not just growth but a future beaming with opportunities and achievements.