AI Governance Vendor Report 2026
This report categorizes comprehensive AI governance providers, using a framework that provides context to the evolving AI governance vendor ecosystem.
As organizations deepen their adoption of artificial intelligence, the need for clear and operational governance structures has never been more important.
A continual theme from our ongoing AI governance landscape research is that AI governance is not a single function, discipline or technology. Our diagram, “Mapping and understanding the AI governance ecosystem,” highlights the many different actors and functions involved in designing, developing and delivering AI governance. This includes external service providers, or AI governance vendors, as we will refer to this grouping of companies.
While AI governance companies started appearing in the marketplace as early as 2010, there has been a significant increase in the need for these types of services in recent years. This has led to a significant increase in companies launching AI governance businesses. While many of these efforts are not new, there are many companies that have expanded their business offerings to meet this new demand.
When we originally conducted research for the ecosystem map, we saw that AI governance vendors were grouped into two categories: 1) assurance and tools, and 2) service, compliance and advisory.
Since last year when we started this research, we saw a maturation of what AI governance service providers offer. To bring structure to this rapidly evolving ecosystem, the IAPP has grouped AI governance capabilities into four overarching categories. These categories reflect both the functional needs of organizations and the patterns emerging across market offerings:
- Policy and Compliance
Tools and services focused on internal principles policy development, creation of internal and external governance boards, regulatory alignment and compliance support, documentation, risk identification and management, and internal governance and procurement processes. - Technical Assessments and Evaluations
Tools and capabilities dedicated to analyzing data quality, robustness, model performance, safety, fairness and other factors that impact the development and ongoing monitoring of AI systems. - Assurance and…
Leading with a security-first mentality
As technology rapidly develops, the number of security and privacy concerns will only continue …
