Webinar Description
Key Takeaways
- Extending Zero Trust frameworks to secure non-human identities in agentic AI deployments
- Applying identity binding and workforce-grade access policies to AI agents, service tokens and machine-to-machine clients
- Implementing real-time governance to detect prompt injection and data exfiltration threats
- Using AI Gateways for model routing optimisation and operational resiliency
- Deploying secure MCP servers with least-privilege enforcement and audit capabilities
Securing Autonomous AI Within Enterprise Zero Trust Architectures
This webinar addresses the security challenges organisations face when deploying autonomous AI agents at enterprise scale. Designed for security architects, identity and access management professionals, and technology leaders, the session examines how Zero Trust principles can be extended to govern non-human identities—a critical gap in many current security frameworks. As executive mandates push organisations toward AI-first strategies, the tension between rapid innovation and maintaining enterprise control has become a defining challenge for security teams.
The Non-Human Identity Challenge in Agentic AI
The proliferation of autonomous AI agents introduces identity management complexities that traditional security models were not designed to address. Unlike human users who authenticate through established identity providers, AI agents, service tokens and machine-to-machine clients operate continuously and often with broad permissions that can be difficult to monitor. This creates what security professionals increasingly recognise as shadow AI—autonomous systems operating outside governance frameworks, accumulating access rights and generating costs without centralised visibility.
The attack surface expands considerably when AI agents interact with sensitive data, external APIs and internal systems. Prompt injection attacks, where malicious inputs manipulate agent behaviour, and data exfiltration through compromised agent workflows represent emerging threat vectors that conventional perimeter defences struggle to detect. These risks compound as organisations scale their AI deployments across business units.
Extending Zero Trust to AI Workloads
The webinar presents a framework for applying Zero Trust principles—traditionally focused on human workforce access—to the governance of AI agents and automated systems. Identity binding emerges as a foundational concept, treating AI agents with the same rigour applied to employee credentials. This means enforcing granular access policies, implementing just-in-time permissions and maintaining continuous verification rather than granting persistent elevated privileges.
Real-time governance capabilities allow organisations to monitor agent behaviour without introducing latency that would impair operational performance. Automated detection of anomalous patterns, including potential prompt injection attempts or unexpected data access, enables security teams to respond to threats as they emerge rather than discovering breaches through post-incident analysis.
AI Gateways and Operational Resiliency
AI Gateways serve as a control plane for managing model routing, load balancing and failover across AI infrastructure. Beyond security, these gateways address operational concerns including cost management, availability and performance optimisation. By centralising AI traffic through governed pathways, organisations gain visibility into model usage patterns and can enforce policies consistently across diverse AI deployments.
The session also covers secure experimentation environments using edge-hosted Model Context Protocol servers. These configurations enable development teams to test new AI capabilities within controlled boundaries, with built-in audit trails and least-privilege enforcement preventing experimental workloads from accessing production data or systems inappropriately.
Who Should Attend
This webinar is relevant for enterprise security architects evaluating how existing Zero Trust investments can extend to AI workloads, identity and access management teams responsible for non-human identity governance, and technology leaders balancing innovation velocity against risk management requirements. Organisations in regulated industries facing compliance obligations around AI transparency and data protection will find the governance frameworks particularly applicable.