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During this webinar, you will gain insight into how to design and implement secure data paths and cross domain strategies that maintain zero trust boundaries, even when ingesting complex training data and AI artifacts from partner networks, open-source repositories, or OSINT feeds.
What we explore:
Converting, inspecting, and transferring AI training data and embeddings into high-side environments while enforcing zero trust principles
Secure ingestion of model files – e.g., Safetensors and GGUF (GPT) into classified environments
Protecting offline LLM deployments against prompt-based attacks
Improving AI model assurance in isolated enclaves by using techniques such as “model-to-watch-the-model” and high-side signature validation
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