Most discussions about AI security focus on prompt injection, model poisoning or jailbreaks.

Those are important—but they are only the beginning.

As organizations deploy AI agents, Model Context Protocol (MCP), autonomous workflows, API orchestration and agent-to-agent communication, an entirely new attack surface is emerging.

Unlike traditional applications, AI systems continuously consume external data, invoke tools, exchange context and make decisions across multiple systems. Every connector, identity, API, memory store and autonomous action becomes a potential entry point for attackers.

This session explores the hidden enterprise attack surface created by modern AI ecosystems.

We will discuss identity propagation, agent permissions, API trust relationships, MCP security considerations, supply-chain risks, Shadow AI, data exposure and the governance challenges introduced by autonomous systems.

Instead of focusing on theoretical attacks, this presentation provides practical guidance for architects, security leaders and engineering teams preparing their organizations for the next generation of AI-enabled enterprise systems.

Because protecting AI is no longer about protecting a model.

It is about protecting an entire ecosystem.

Audience

Security Architects
AI Engineers
Platform Engineers
Enterprise Architects
CISOs
DevSecOps
AI Governance Teams

Session Length

30–45 minutes

Level

Intermediate

Takeaways

Understanding the emerging AI attack surface
Risks introduced by AI Agents and MCP
Identity and API security in autonomous systems
Governance recommendations for enterprise AI adoption
Practical controls beyond prompt security

No live hacking. Enterprise-focused architecture and security strategy.