Cloud-hosted AI models offer undeniable convenience. But every prompt or API call to a hosted AI model includes the possibility of your company’s "secret sauce" leaking into a provider’s operational or training data sets.

In this session, we will identify the "hidden lifecycles of data" in AI systems, and criteria you can use to determine if those risks or unknowns are acceptable or not. We will also outline a "local-first" AI stack that brings your own models to your data and workflows, for those instances where the risks, costs, or unknowns of using hosted models are not acceptable.

Your takeaways will include:
* The Risk Surface: Understanding the vulnerabilities that can arise by bringing your data and workflows to hosted AI models.
* The Decision Framework: Criteria for balancing privacy, cost, and performance to determine when you should stay local and when you may need hosted models.
* The Local Stack: A curated guide to discovering, deploying, and operating your own AI models and tools.

The Bottom Line: Treat your data and workflows like the valuable, protected assets that they are. Deploying a local AI stack gives you modern AI capabilities on your own terms and in your own infrastructure.