Open Weight Models and Open Source Harnesses | Episode 56
About this episode
The episode discusses the implications of owning your AI stack, focusing on open-weight models and open-source harnesses for security teams.
In this episode of BHIS Presents: AI Security Ops, the team looks at what it actually means to own your AI stack. Open-weight models and open-source harnesses are no longer just lab toys. They are becoming practical options for security teams that care about where their prompts, code, client data, findings, and tooling actually live. The core question: when your work depends on AI, how much control are you willing to give away? We dig into: - What data sovereignty means for security teams - Why token sovereignty matters in agentic workflows - How provider terms can become a business risk - Open-weight models vs. truly open-source AI - Why harnesses like Hermes and OpenCode matter - Where cloud providers may apply fewer restrictions - The tradeoff between local control and hosted capability - Supply chain risk in models, harnesses, and plugins - Running local models with Ollama, VLLM, and similar tools - Why “local” does not automatically mean “safe” - How to start experimenting without buying expensive hardware - The next risk frontier: local prompt injection Owning your AI stack does not magically eliminate risk. It moves the risk. Hosted models create exposure around data…
More episodes of AI Security Ops
- Interview with Josh Mason | Episode 62
- Are Foreign Open Weight Models a Security Risk? | Episode 61
- Hey Skippy! | Episode 60
- Mythos and Fable Pulled | Episode 59
- Agentic Security: The Maturity Model — From Wild West to Locked Down | Episode 58
- Introducing Fusion AI Pentest | Episode 57
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