Strategic AI Governance: Navigating Compliance and Risk in the AI Era
In the evolving landscape of artificial intelligence (AI), organizations face the challenge of effectively managing governance frameworks. Many AI programs often find themselves rebuilding governance structures multiple times. Teams redundantly document the same models and suppliers for various regulations, such as the EU AI Act, DORA, and other sector-specific guidelines. This results in duplicated evidence, prolonged preparation times, and significant approval bottlenecks, delaying deployments in sectors like financial services, healthcare, and the public sector.
Converging Governance Efforts
To improve efficiency, leading organizations are shifting towards a unified governance approach. Instead of treating each regulatory framework separately, they are adopting a single control catalog and evidence spine that can be utilized across multiple frameworks. This strategy reduces overlapping documentation efforts and significantly cuts audit preparation times from months to just weeks, enabling teams to deploy regulated workloads more swiftly.
Establishing a Robust Evidence Spine
Effective AI governance requires resilient programs that collect and manage vital information, including incidents, vulnerabilities, and model lifecycle artifacts. By employing the Open Security Controls Assessment Language (OSCAL), organizations can ensure that controls, assessments, and plans of action and milestones (POA&Ms) are machine-readable. Maintaining a single SBOM-backed supplier inventory aligned with NIST SP 800-161r1 further streamlines the documentation process.
AI-Specific Assurance Assets
In the realm of AI, traditional security audits need to evolve. An AI Bill of Materials (AIBOM) extends the conventional software bill of materials (SBOM) to include model-specific details such as training data sources and safety evaluations. The Vulnerability Exploitability eXchange (VEX) reports focus on real vulnerabilities affecting deployed models, allowing teams to quickly address questions about model safety and content.
U.S. AI Governance Framework
In response to recent federal guidance, organizations are appointing accountable AI officials and establishing cross-functional governance councils that encompass security, legal, compliance, product, HR, and operations. This governance body is responsible for defining AI policy, risk appetite, and reporting on significant use cases that impact rights and safety.
Inventorying AI Use Cases
Understanding the landscape of AI usage is crucial for effective governance. The U.S. federal government’s catalog of AI use cases has expanded significantly, categorizing numerous applications as rights- or safety-impacting. This model is being adopted by the private sector, where teams must register AI systems and apply stricter controls to high-risk applications.
Workflows Over Checklists
For governance to be effective, it must operate as a workflow rather than a simple checklist. The governance council is tasked with defining risk tiers and approval gates, ensuring that high-risk systems generate the necessary documentation (AIBOMs, SBOMs, VEX reports) as part of their release process. This documentation feeds into the evidence spine, enabling efficient querying by audit and legal teams.
Global Compliance and Design Principles
AI regulations emphasize key principles such as risk-based classification, documentation, and human oversight. The EU AI Act formalizes these principles, particularly for high-risk systems. Organizations must prepare for strict reporting requirements, including timely notifications of major ICT incidents.
China’s Global AI Governance Strategy
At the World Artificial Intelligence Conference (WAIC) 2025, China introduced a comprehensive Global AI Governance Action Plan, proposing a UN-related AI body that aims to empower numerous Global South member states. This plan underscores the importance of open-weight models and technology transfer, critiquing restrictive export-control regimes.
Building a Unified Deployment Model
Global vendors adopting a state-led, documentation-heavy approach create a standardized deployment model that effectively spans numerous markets. By aligning their evidence frameworks, AIBOMs, and incident playbooks with the most stringent standards, they simplify compliance across different regulatory environments.
Avoiding Common Pitfalls
Organizations that fail to implement a governance council often end up with outdated documentation. Similarly, those that classify use cases without enforcing controls may face significant audit findings. By adopting a comprehensive governance strategy, organizations can transform compliance from a burdensome task into a streamlined process that accelerates deployment across regulated markets.
