LF Live Webinar: Agentic AI in the Wild: Rethinking Trust When Your AI Has the Keys

Why It Matters

Agentic AI will process sensitive data autonomously, so failing to secure data-in-use jeopardizes compliance, intellectual property, and business continuity.

Key Takeaways

• Traditional perimeter security insufficient for autonomous AI workloads.
• Data-in-use protection via confidential computing is critical for enterprises.
• Agentic AI introduces new trust dependencies like inference providers.
• Continuous attestation and verifiable workloads needed for ongoing trust.
• Hardware‑rooted security and low‑level primitives form trust foundation.

Summary

The webinar examined the emerging security challenges of agentic AI—autonomous systems that act without human oversight—and why traditional perimeter‑based defenses no longer suffice. Panelists from Nvidia, Intel and Agile Systems argued that confidential computing, which protects data while it is being processed, must become a core component of enterprise security architectures. Key insights included the inadequacy of encrypt‑at‑rest alone, the emergence of new attack surfaces such as prompt injection, and the need to treat data as executable code that requires continuous verification. Jesse highlighted that once data is decrypted for use, it is exposed in memory, while Daniel emphasized attestation, signed containers, and verifiable workloads as mechanisms to maintain trust over time. Phix underscored the risk of inference providers seeing raw data and suggested on‑prem, encrypted remote execution, or hardware‑enforced confidential AI as mitigation paths. The discussion also stressed that trust must be negotiated across the entire stack—from silicon‑level hardware roots to cloud hypervisors and model providers. Building a chain of provenance through hardware‑based attestation and policy‑driven decisions enables organizations to meet data‑location and regulatory requirements while still leveraging powerful autonomous agents. For enterprises, the implication is clear: security strategies must evolve to incorporate confidential computing primitives, continuous evidence generation, and ecosystem‑wide standards. Without this shift, the promise of agentic AI—greater productivity and real‑time decision making—remains constrained by compliance risk and potential data breaches.

Original Description

Sponsor: Confidential Computing Consortium

The era of agentic AI isn't coming; it's already running across the enterprise and touching sensitive infrastructure with a level of autonomy that most security architectures were never designed to handle. This webinar explores how to use Confidential Computing to protect your enterprise in the new and rapidly evolving world of agentic AI. Learn more about the expanding threat surfaces that emerge when AI agents operate autonomously across enterprise environments, from prompt injection and model tampering to data exfiltration through compromised runtimes, and why vulnerabilities uncovered through new agentic tools make clear that traditional perimeter-based defenses are no longer sufficient.

Hear directly from experts at Edgeless Systems, Intel and NVIDIA on the front lines of agentic AI deployment as we examine where existing security stacks fall short and how Confidential Computing delivers the hardware-rooted, full-stack trust model that agentic AI demands, securing models, data, and execution environments from silicon to cloud. Hosted by the Confidential Computing Consortium ahead of Confidential Computing Summit 2026, this talk will provide the essentials for enterprise security architects, AI infrastructure leads, and anyone responsible for deploying AI in regulated or high-stakes environments who refuses to let speed of adoption outpace security posture.