AI Governance Checklist (Updated 2025)

Summary

Lumenalta's 2025 AI Governance Checklist cuts through the complexity of modern AI oversight by merging cybersecurity and governance considerations into a single, actionable framework. Unlike generic compliance checklists, this resource specifically addresses the challenges of 2025's AI landscape—including Large Language Model (LLM) deployments, evolving regulatory requirements, and the practical realities of building stakeholder trust while managing costs. The checklist provides a systematic approach to identifying governance gaps, prioritizing security measures, and establishing oversight mechanisms that actually work in practice.

What Makes This Different

This isn't another theoretical governance framework. Lumenalta's checklist stands out by treating cybersecurity and AI governance as interconnected disciplines rather than separate concerns. The 2025 update reflects the maturation of AI regulations globally and incorporates lessons learned from early AI deployments that failed due to overlooked security vulnerabilities or inadequate governance structures.

The resource acknowledges that organizations need practical guidance for LLM implementations specifically—addressing unique challenges like prompt injection attacks, data leakage through model outputs, and the governance complexities of third-party AI services. Rather than starting from scratch, the checklist helps organizations build on existing risk management processes while extending them to cover AI-specific concerns.

Who This Resource Is For

Primary audience: Risk managers, compliance officers, and IT security leaders at mid-to-large organizations who are responsible for establishing or auditing AI governance programs. This is particularly valuable for teams that have some AI governance foundation but need to strengthen the integration between security and oversight functions.

Also useful for: C-suite executives seeking a comprehensive view of AI governance requirements, legal teams preparing for regulatory compliance, and consultants advising clients on AI risk management. Organizations in regulated industries (financial services, healthcare, government contractors) will find the systematic approach especially relevant for demonstrating due diligence.

Not ideal for: Very small organizations with limited AI deployments, academic researchers focused on AI ethics theory, or teams looking for technical implementation guides rather than governance frameworks.

How to Apply This Checklist

The resource is designed for iterative use rather than one-time completion. Start by using the checklist to conduct a baseline assessment of your current AI governance maturity, identifying immediate security gaps and governance blind spots. The format allows teams to assign ownership for each checklist item and track progress over time.

For organizations new to AI governance, focus first on the foundational security controls and basic oversight mechanisms before advancing to more sophisticated governance structures. Established AI users should pay particular attention to the LLM-specific considerations and updated regulatory alignment sections that reflect 2025's evolving compliance landscape.

The checklist works best when customized to your organization's risk profile and regulatory context. Use it as a starting template, then adapt the specific requirements based on your industry, geographic jurisdiction, and AI use cases.

Key Implementation Areas

Security-First Approach: The checklist emphasizes establishing robust security controls before scaling AI deployments. This includes data protection measures, access controls, and monitoring capabilities specifically designed for AI systems.

LLM Governance: Dedicated sections address the unique challenges of governing large language models, from input validation and output monitoring to managing third-party LLM services and API integrations.

Stakeholder Trust: Beyond compliance, the framework includes measures for building and maintaining trust with customers, regulators, and internal stakeholders through transparent governance practices and clear accountability structures.

Cost Optimization: Practical guidance on balancing governance requirements with budget realities, helping organizations prioritize high-impact controls and avoid over-engineering governance systems.

FAQs

How often should we revisit this checklist? Lumenalta recommends quarterly reviews for active AI deployments, with more frequent assessments during periods of significant regulatory change or when introducing new AI capabilities.

Can this replace our existing risk management framework? No, this checklist is designed to complement and extend existing enterprise risk management processes, not replace them. It fills AI-specific gaps that traditional risk frameworks often miss.

Does this address specific regulations like the EU AI Act? While the checklist takes a global approach rather than focusing on specific regulations, the 2025 update incorporates requirements from major AI regulations including the EU AI Act, ensuring broad regulatory alignment.