Webinar Description
Key Takeaways
- Addresses the governance challenges that emerge as enterprise AI adoption scales across multiple teams and models
- Explores the concept of an AI control plane for managing access, usage and risk centrally
- Examines how AI Gateway technology supports distributed AI governance
- Presented in collaboration with CheckMates AI Security Masters
- Relevant for security teams, IT leaders and governance professionals managing enterprise AI deployments
Establishing Centralised Control Over Enterprise AI
As organisations accelerate their adoption of artificial intelligence, security and governance teams face an increasingly fragmented landscape. Different departments may be accessing different AI models, each with varying access rights, budget allocations and control mechanisms. This session, developed in collaboration with the CheckMates AI Security Masters, examines how enterprises can implement a practical control plane to bring consistency and oversight to AI usage across the organisation.
The challenge is not simply technical but organisational. When AI adoption occurs in silos, security teams lose visibility into which models are being used, who has access to them, and what data flows through these systems. The result is a governance gap that widens as adoption scales, creating potential compliance risks and inconsistent security postures across business units.
The Case for an AI Control Plane
The session introduces the concept of an AI control plane as the missing architectural layer in enterprise AI deployments. Much like network control planes provide centralised management for distributed infrastructure, an AI control plane offers security teams a unified mechanism for governing access, monitoring usage patterns and managing risk across diverse AI implementations.
This approach recognises that enterprise AI adoption is inherently distributed. Teams across an organisation will inevitably select different tools and models suited to their specific requirements. Rather than attempting to standardise on a single solution, a control plane architecture allows for this diversity whilst maintaining consistent governance policies and security controls.
AI Gateway as a Governance Mechanism
The session explores how AI Gateway technology functions as a practical implementation of centralised governance for distributed AI adoption. By positioning a gateway layer between users and AI models, organisations can enforce access policies, log interactions for audit purposes and apply consistent security controls regardless of which underlying model or service is being accessed.
This architectural approach addresses several governance requirements simultaneously. Access management ensures that only authorised users can interact with specific models. Usage monitoring provides visibility into how AI tools are being employed across the organisation. Risk management capabilities allow security teams to identify and respond to potential issues before they escalate.
Who Should Attend
This session is designed for security professionals, IT leaders and governance specialists responsible for managing AI adoption within their organisations. It will be particularly relevant for those who have moved beyond initial AI pilots and are now grappling with the complexities of scaling AI usage across multiple teams whilst maintaining appropriate controls. Practitioners seeking practical frameworks for AI governance rather than theoretical discussions will find the session’s focus on real-world implementation valuable.