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Why AI governance is the foundation of secure MCP adoption
Move beyond fragmented governance and build the accountability, ownership, and shared context needed for successful MCP adoption.
Description
MCP (model context protocol) is rapidly emerging as the standard for connecting AI systems with enterprise data and business processes. However, many organisations focus on technology before addressing a more fundamental challenge: governance readiness.
Successful MCP adoption depends on more than secure architecture and technical controls. It requires clear ownership, cross-functional accountability, trusted data sources, auditability, and governance structures capable of supporting AI-enabled decision-making across the enterprise.
This session explores how organisations can move from fragmented governance ecosystems (where risk, compliance, audit, legal, quality, and security operate in silos) toward a shared organisational context that enables responsible and scalable AI adoption.
Drawing on practical governance experience and research into governance under uncertainty, the session highlights common governance pitfalls that can undermine AI initiatives, including accountability gaps, siloed decision-making, and symbolic governance practices. Attendees will leave with a practical framework for assessing governance readiness, aligning key stakeholders, and establishing the governance foundations that transform MCP from a technical implementation into a strategic business capability.
Successful MCP adoption depends on more than secure architecture and technical controls. It requires clear ownership, cross-functional accountability, trusted data sources, auditability, and governance structures capable of supporting AI-enabled decision-making across the enterprise.
This session explores how organisations can move from fragmented governance ecosystems (where risk, compliance, audit, legal, quality, and security operate in silos) toward a shared organisational context that enables responsible and scalable AI adoption.
Drawing on practical governance experience and research into governance under uncertainty, the session highlights common governance pitfalls that can undermine AI initiatives, including accountability gaps, siloed decision-making, and symbolic governance practices. Attendees will leave with a practical framework for assessing governance readiness, aligning key stakeholders, and establishing the governance foundations that transform MCP from a technical implementation into a strategic business capability.
About the speakers
Salla Lutz is a governance professional working at the intersection of governance, risk, compliance, quality, and organisational transformation in a multinational high-tech group environment. Her work focuses on integrating governance structures, management systems, and decision-making processes across complex multi-entity organisations.
She holds a PhD in Business Transformation under Uncertainty and is currently pursuing a Master’s degree in Risk & Compliance Management. Her research focuses on governance competence, responsible leadership, and the organisational factors that enable — or obstruct — effective governance in increasingly complex and digital environments.
Combining academic research with hands-on experience in governance integration, internal auditing, risk management, management systems, and organisational change, Salla brings a practical perspective to the challenges organisations face when implementing AI-enabled governance structures.
Her particular interest lies in helping organisations move from fragmented governance systems toward shared organisational context, where technology supports transparency, accountability, and better decision-making without replacing human judgment and leadership responsibility.
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