As organizations increasingly run AI workloads in public clouds, security teams face new challenges around data protection, identity, workload isolation, misconfiguration, and visibility. This talk shares lessons learned and common concerns we have heard from customers running AI workloads in the cloud, based on our experience helping them address security challenges. We will explore why understanding the AI environment and having visibility into the workloads, infrastructure, data, and access paths supporting it are critical to managing risk effectively.
*Sponsored by Fortinet
Virtual
AI agents are becoming trusted operators in cloud environments, deploying infrastructure, writing code, managing identities, and executing privileged actions. That trust also creates a new attack surface. Recent research demonstrated how attackers manipulated AI coding agents through prompt injection and social engineering, enabling cloud attack chains without exploiting a software vulnerability. Once compromised, AI agent activity can appear indistinguishable from legitimate automation, making detection significantly more difficult. This session exlores how AI is reshaping cloud threat models and provides a practical framework for securing autonomous agents in cloud environments.
*Sponsored by Trend AI
Virtual
Virtual
AI hasn’t just changed how we build—it’s fundamentally reshaped the attack surface. Today’s AI applications aren’t standalone systems; they are dynamic, interconnected ecosystems spanning models, data pipelines, agents, and cloud infrastructure—creating entirely new paths for risk. In this session, we’ll explore the rise of the AI Application Protection Platform (AI-APP) and why traditional security approaches fall short. Learn how a graph-powered, unified platform brings together visibility, risk analysis, and runtime protection to secure AI end-to-end—from code to cloud to model behavior. Discover how security teams can move beyond fragmented tooling to understand real attack paths, reduce noise, and protect AI at the speed it’s built.
*Sponsored by WIZ
Virtual
Virtual
Traditional threat modeling assumes a system stable enough to diagram, review, and revisit quarterly. AI-assisted development breaks that assumption. Agentic coding tools generate pull requests continuously, not on a human review cadence. CI/CD pipelines increasingly let an agent propose — and sometimes approve — its own changes. Dependency bots auto-merge updates faster than anyone evaluates whether the maintainer, or transitive package graph beneath a version number, has changed. The result is a release velocity that no review process built for human-paced development was designed to handle. You can’t threat model every change at this pace, so the real question becomes which changes are worth the effort. This talk proposes a way to make that call: a threat modeling framework that scales rigor to risk and velocity rather than to the calendar – so the highest-risk changes still get human scrutiny while routine ones can move at machine speed.
*Sponsored by Adobe
Virtual
Autonomous AI agents unlock immense business value by executing complex workflows, but they introduce novel security risks like prompt injections, identity sprawl, and tool misuse. Join our session to explore how you can use built-in security and governance controls to confidently scale AI agents into production without compromising developer velocity. Learn how to establish a zero-trust framework for AI agents, secure agent interactions and tool access, enforce real-time guardrails, and proactively manage risk and discover shadow AI.
*Sponsored by Google
Virtual
Virtual
