Organisations are placing greater emphasis on documenting how AI applications are assessed, approved, monitored, and controlled, creating demand for technologies that can generate traceable evidence of governance activities. AI governance programmes increasingly require records of risk assessments, policy decisions, control implementation, approvals, and monitoring outcomes rather than relying solely on written policies. This is creating demand for platforms that automate evidence collection, maintain audit trails, and provide centralised documentation across AI applications and governance processes. Recent industry research also indicates that organisations are moving toward more operationalised AI governance, increasing the need to connect governance activities with demonstrable controls and evidence.

Securing sensitive enterprise information

The AI data protection & security controls segment is supported by the need to secure sensitive enterprise information accessed and processed through AI applications. Organisations are deploying capabilities to identify sensitive data, monitor AI-related data flows, control information access, prevent unauthorised disclosure, and enforce data handling policies. Integration of AI applications with corporate databases, documents, SaaS platforms, and other enterprise repositories is increasing the need for stronger controls that protect confidential information while enabling legitimate AI use. Integration with existing data security and enterprise security infrastructure is also supporting adoption of these capabilities.

SMEs benefit from cloud-based modular technologies

The SMEs segment is supported by the growing availability of cloud-based and modular AI governance technologies that require limited infrastructure and internal resources. Smaller organisations are adopting AI across functions such as customer service, marketing, software development, finance, and operations, creating requirements for practical controls around AI usage. Solutions that automate AI risk assessment, policy management, usage monitoring, compliance documentation, and security enforcement are helping organisations address these requirements with smaller technology and governance teams. Subscription-based platforms and integrated solutions are further lowering implementation complexity and enabling SMEs to expand governance capabilities as their use of AI increases.

Established cyber security ecosystem benefits North America

North America is expected to account for the largest share of the shadow AI risk & governance market, supported by its established cybersecurity ecosystem, concentration of AI technology providers, and continued development of practical frameworks for managing AI-related risks. In the US, NIST is expanding its AI risk-management work through updates to the AI Risk Management Framework, AI standards initiatives, and security-focused guidance covering generative AI and AI agent systems. NIST is also developing a Trustworthy AI in Critical Infrastructure Profile to provide sector-specific risk-management practices for organisations deploying AI-enabled capabilities. In Canada, government initiatives are advancing responsible AI through the national AI strategy, AI transparency measures, privacy modernisation, and guidance for the responsible use of generative and agentic AI. These developments, alongside the presence of established cyber security and AI governance providers, are supporting demand for AI discovery, data protection, policy management, risk assessment, and access controls across enterprises in the region.

Key players

Some of the main players in this market and mentioned by the researchers include businesses such as, Palo Alto Networks, Microsoft, Zscaler, Netskope, Cisco, IBM, Servicenow, Onetrust, Forcepoint, Proofpoint, Credo AI, Holistic AI, Mindgard, Cranium AI, Neuraltrust, Modelop, Monitaur, Saidot, Relvance AI, Truvo, Airia, Trustible, Hiddenlayer, Check Point, AI Shadow, Zenity, Noma Security, and Harmonic Security.