MCP Servers Are Closing The AI Context Gap In GRC – But They're Also Creating New Challenges

2026 has brought a new trend to the governance, risk and compliance (GRC) software market. In April, Optro launched Model Context Protocol (MCP) server capabilities, and SureCloud followed in May with the launch of Gracie AI, built on MCP servers. The trend is still gaining momentum, with LogicGate also announcing MCP access in September. MCP servers are modernizing GRC platforms at the architectural level, offering context-rich AI outputs that support highly regulated risk management use cases. However, this capability may be too advanced, given the current state of user data hygiene and AI maturity.

Before MCP servers, one limitation of using AI within risk management was the lack of context, data and ontology-supported outputs, which heightened trust issues for auditors and regulators. Though application programming interfaces (APIs) helped bolt AI onto legacy GRC platforms, they did not solve the architectural issue of carrying organizational knowledge and context into AI models accurately. As a result, risk leaders became hesitant to integrate AI into sensitive, context-dependent use cases. Indeed, just 2% of respondents to our 2026 global risk survey are currently using AI for compliance and regulatory change management, down from 32% in the 2025 survey.

MCP servers address this gap by acting as a translation layer, letting AI systems tap directly into enterprise data and knowledge to retain context. Within risk management, this can be highly valuable, for example, in interpreting vague new regulations where applicability depends heavily on organizational context. Vendors are marketing this capability in different ways: Optro positions its MCP server as a universal AI interoperability layer, while SureCloud frames it as codifying the user experience by turning organizational knowledge into explainable and traceable AI outputs.

However, selling MCP servers can be challenging on multiple fronts. Firstly, vendors must educate customers on the value of codifying the rich risk data that sit in legacy systems and turning them into decision-grade outputs. As many firms today operate on simpler systems, such as spreadsheets, this will widen the maturity gap between vendor offerings and user maturity. Predictability – both in terms of pricing and value gained – is a significant buying factor for AI capabilities, as buyers seek clear, stable licensing costs, as well as confidence that context-rich insights will deliver measurable value. Proving that return can be tough when the audience is used to legacy systems, and the leap may be too wide at the moment, given users’ current data hygiene and AI maturity.

Security is the bigger hurdle. MCP servers can log into enterprise systems autonomously with their own credentials, making it tough for security teams to track what data are being accessed, and how the server’s access is being used. This is resurfacing traditional shadow IT issues. With trust already the second-largest barrier to AI adoption in risk management, access opacity could make MCP servers tough to sell to CISOs and CIOs.

Vendors can address these challenges through educational marketing campaigns on why MCP servers are critical in highly regulated use cases such as compliance and regulatory change management, in which lineage, traceability and context play a substantial role. Moreover, allowing users to restrict MCP server permissions to operator- or token-based access, instead of superuser access, can offer greater assurance over when and how the MCP server accesses enterprise data.

For more insights on risk management, read the reports on our solution page: Enterprise Risk & Resilience.