Uploaded March 2026 | Updated September 2026, 2 weeks ago
Every OpenClaw agent that reads sensitive data and sends it to a cloud-hosted LLM exposes that data in plain text at inference. Logged, retained, and visible to the hosting provider. Running models locally avoids this, but it's expensive, operationally heavy, and out of reach for most developers.
Protopia AI's Stained Glass Transform takes a different approach: it converts prompts into stochastic embeddings before they leave your machine, non-invertible and non-deterministic, so cloud-hosted endpoints process protected representations instead of readable text. The model still generates accurate responses. Your data never leaves your trust zone in plain text.
Join Kiyu Gabriel (IBM Field CTO) and Andrew Sansom (Protopia AI Lead Solution Engineer) for a hands-on walkthrough of exactly how this works in practice.
What you'll take away:
- How to run OpenClaw agents on sensitive data using managed cloud endpoints, without exposing that data at inference
- How to integrate Protopia AI's Stained Glass Transform with OpenClaw and vLLM, both directly and through Langflow
- How to eliminate the need for local model hosting or self-managed infrastructure while maintaining data protection
By the end, you'll have a working answer to the question every developer building OpenClaw agents will face: how do I use this with sensitive data I'm not able/comfortable to send to the cloud?
Every OpenClaw agent that reads sensitive data and sends it to a cloud-hosted LLM exposes that data in plain text at inference. Logged, retained, and visible to the hosting provider. Running models locally avoids this, but it's expensive, operationally heavy, and out of reach for most developers.
Protopia AI's Stained Glass Transform takes a different approach: it converts prompts into stochastic embeddings before they leave your machine, non-invertible and non-deterministic, so cloud-hosted endpoints process protected representations instead of readable text. The model still generates accurate responses. Your data never leaves your trust zone in plain text.
Join Kiyu Gabriel (IBM Field CTO) and Andrew Sansom (Protopia AI Lead Solution Engineer) for a hands-on walkthrough of exactly how this works in practice.
What you'll take away:
- How to run OpenClaw agents on sensitive data using managed cloud endpoints, without exposing that data at inference
- How to integrate Protopia AI's Stained Glass Transform with OpenClaw and vLLM, both directly and through Langflow
- How to eliminate the need for local model hosting or self-managed infrastructure while maintaining data protection
By the end, you'll have a working answer to the question every developer building OpenClaw agents will face: how do I use this with sensitive data I'm not able/comfortable to send to the cloud?
