Video · IBM Technology ·
Sensitive data makes AI powerful, but also risky. Jeff Crume explains five essential capabilities for using sensitive data safely across AI systems, agents, tools, and RAG pipelines. Learn how visibility, classification, governance, access controls, and evidence help secure AI-driven workflows.
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The video explains how to give AI coding agents relevant repository context so they can produce code that matches existing architecture, dependencies, tests, conventions, and developer intent. It matters because a single prompt cannot capture a large codebase, while excessive context can dilute the information the agent needs.
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The video explains how PyTorch’s torch.compile optimizes models through Dynamo, AOT Autograd, and Inductor, including lazy compilation, graph capture, kernel fusion, and hardware-specific code generation. It also shows why speedups vary and how graph breaks, recompilation, and incorrect benchmarking can limit performance.
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The video examines how AI is exposing weaknesses in web security, conversational model evaluation, cultural understanding, and enterprise data infrastructure. It argues that organizations must modernize legacy systems and rethink benchmarks as AI agents discover flaws faster and create new operational demands.
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The video explains how quantization compresses LLM parameters into lower-precision formats to reduce memory use and improve inference speed. It shows the tradeoff between smaller models and potential accuracy loss, and compares post-training quantization with quantization-aware training.