Enterprise AI applications depend on more than an LLM. They need architectures that connect user questions with enterprise data safely, accurately, and in context. Join us as we explain the core concepts behind enterprise AI application architectures, including vectors, vector databases, embeddings, retrieval augmented generation (RAG), vector databases, and agentic workflows. Learn how text is chunked, tokenized, vectorized, and converted into numerical representations that help AI systems understand semantic meaning. Sourabh also explains why teams need to account for challenges such as stale data, access control gaps, authentication, and enterprise permissions. The video also covers agentic architectures, where AI agents retrieve information from live applications and data stores, break complex problems into subproblems, use LLMs to solve intermediate steps, and combine results into a final answer. As organizations build and scale enterprise AI, security teams need visibility and control across prompts, responses, agents, data flows, vector databases, AI applications, and employee GenAI usage. Understanding how generative AI works is an important first step toward securing it. CrowdStrike Falcon® AI Detection and Response helps organizations gain visibility into AI use and protect AI applications, agents, prompts, and enterprise data as adoption expands. 🛡️ CrowdStrike AI Security Hub Explore CrowdStrike resources for understanding & securing generative AI adoption across the enterprise: https://cs.link/urLra 📣 Connect With Us: ► X: https://twitter.com/CrowdStrike ► Instagram: https://www.instagram.com/crowdstrike ► LinkedIn: https://www.linkedin.com/company/crowdstrike 🔔 Subscribe to stay updated! #CrowdStrike #Cybersecurity #AISecurity101

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