Checklist
AI Compliance Readiness Checklist (Executive Summary)
A rapid self-assessment for boards, executives and compliance leaders deciding whether the foundations are in place to expand AI work.
Overview
This executive summary provides a rapid self-assessment for organizational readiness.
It is designed for board members, executives, and compliance leaders who need to evaluate whether foundational guardrails are in place before expanding initiatives.
Check each item honestly — gaps indicate where focused attention or investment is required.
Governance & Oversight
- A formal governance policy exists and is approved by senior leadership.
- An oversight committee or ethics board meets regularly and includes cross-functional members (IT, Legal, Risk, HR, Operations).
- All systems in use are registered and inventoried in a central repository.
- Accountability for decisions is clearly assigned — both system owners and business sponsors are known.
- Governance documents align with corporate GRC frameworks and are reviewed annually.
Risk Management
- AI-specific risks are integrated into the enterprise risk register.
- Each system has been classified by risk level (e.g., limited, high, prohibited).
- Model validation and bias testing occur before deployment and at defined intervals.
- Documented contingency plans exist for failures, outages, or regulatory findings.
- Independent audits or peer reviews are conducted on high-risk systems.
Compliance & Legal
- The organization tracks relevant AI regulations (EU AI Act, GDPR, CCPA, U.S. Executive Orders, etc.).
- Data collection and model-training practices follow established privacy and consent laws.
- Vendor and partner contracts include compliance clauses and audit rights.
- Version histories, datasets, and decision logic are fully documented.
- A compliance-by-design process is embedded into development and procurement.
Data & Model Integrity
- Data used for training is accurate, representative, and up to date.
- All data sources are cataloged with provenance and ownership.
- Security controls protect data in transit and at rest, following corporate standards.
- Models are explainable and traceable, with documentation for key variables.
- Obsolete or biased models are retired through a controlled process.
Training & Culture
- Employees receive basic literacy training as part of onboarding or compliance programs.
- Specialized training exists for developers, risk officers, and executives.
- A reporting channel is available for related issues or ethical concerns.
- Leadership regularly communicates goals, risks, and safeguards to all staff.
- The organization promotes a culture of transparency and continuous learning.
Stakeholder Trust & Transparency
- Customers, partners, and regulators can access clear, accurate information about the organization’s AI use.
- A process exists for responding to stakeholder inquiries and correcting misinformation.
- Regular transparency reports summarize performance, risk, and progress.
- Feedback from users or the public is reviewed and acted upon promptly.
- Communications emphasize responsible innovation — neither hype nor fear.
Scoring Guidance
| Readiness Level | Description |
|---|---|
| Fully Ready | 90–100% of items checked; governance, compliance, and culture are operational. |
| Developing | 60–89% checked; structure exists, but maturity and consistency need work. |
| Foundational | 30–59% checked; awareness present, but frameworks incomplete or informal. |
| At Risk | Below 30%; immediate attention required before further AI expansion. |
Next Step:
If any section scores below “Developing,” review the corresponding topic in the AI Compliance Readiness Guide or proceed to the Internal Implementation Guide for detailed action plans.
Riptide Solutions – Advisors in Responsible AI Governance and Compliance