Design Secure AI Architectures: Applying Zero Trust to LLMs, Agents, and Data Pipelines

Design Secure AI Architectures: Applying Zero Trust to LLMs, Agents, and Data Pipelines

AI is rapidly reshaping enterprise application architectures, introducing LLMs, AI agents, model context protocols (MCP), vector databases, external APIs, and autonomous workflows into environments that were never designed for dynamic, decision-making systems. Yet many organizations are deploying AI capabilities without rethinking their foundational security models. Traditional perimeter controls are insufficient for systems where identity, data access, and API interactions drive risk. In this webinar, IANS Faculty Dave Shackleford explores how the core pillars of zero trust (IAM, data security, network segmentation, device trust, and continuous monitoring) must evolve to secure modern AI architectures.

• Examine how zero trust principles apply to AI agents acting on behalf of users, LLM-to-API execution paths, prompt injection risks, sensitive data exposure in vector stores, and the governance of machine identities that now outnumber humans
• Learn how to map zero trust controls directly onto AI application components to design AI systems that are resilient, governable, and aligned with enterprise risk strategy