Explore the Securing AI with CrowdStrike Lab, where AI security meets real-world challenges. Discover how CrowdStrike's AIDR and Falcon Cloud Security protect against AI-specific threats like prompt injection and data leakage, ensuring robust security for workforce, development, and runtime environments. Uncover the future of AI security today.

In this lab, you will walk through an on‑premises solution for serving your private cloud AI system using VMware Private AI. Building on the foundational components of VMware Cloud Foundation (VCF), VMware Private AI leverages Tanzu Kubernetes to deliver a streamlined, enterprise‑ready platform. This solution is designed to help you accelerate your AI journey by enabling scalable, secure, and efficient deployment of AI workloads within your private cloud environment.

This lab presents a simplified two-switch high-availability design for a Cisco AI deployment, highlighting both North–South and East–West traffic flows. It uses NDFC to demonstrate operational visibility and telemetry monitoring. The provided NDFC account is read-only for demonstration purposes. The topology separates administrative connectivity and GPU communication, offering a focused view of core AI networking concepts.

This lab is intended to give you an overview of HPE Private Cloud AI, HPE's turnkey solution for on-prem AI workloads. HPE has paired with NVIDIA's AI Enterprise software giving customers a scalable on-prem solution that can handle multiple different AI workloads from Inference, RAG, model fine-tuning, and model training.

Explore the transformative potential of AI in cybersecurity with an AI-powered assistant that automates threat analysis. By integrating real-time data from CrowdStrike, this lab showcases AI's ability to enhance security operations, offering insights and recommendations that accelerate investigations and improve decision-making in security operations centers.

This hands-on lab for LangChain 1.0 progresses from basic LLM calls and prompt templates through conversation memory, Retrieval-Augmented Generation (RAG), and autonomous agents with tools. By the end, you'll have the foundational patterns to build chatbots, document Q&A systems, and intelligent assistants that can reason over your own data.

Training data poisoning poses significant risks to Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) systems. This lab explores these dangers through a case study of an online forum, demonstrating how corrupted data can compromise AI effectiveness and security, and examines methods to mitigate such threats.

In this lab a customer will deploy the Patch & Vulnerability Prioritization Agent on the HPE Private Cloud AI instance in the ATC. This Assistant is built on top of the NVIDIA NeMo Agent Toolkit and leverages Open AI OSS NIMs under the hood. Leveraging representative data from an IT Service Management Platform, the Patch & Vulnerability Prioritization Agent