Scbkr – an owner-signed responsibility-chain workbench for local LLMs
Scbkr is a workbench that implements an owner-signed responsibility-chain model for local LLMs, allowing users to track and manage accountability across AI-generated outputs and workflows running on local hardware.
Background
- Scbkr is a new open-source tool that lets developers run local LLMs (models like Llama, Mistral, etc. on their own hardware) while keeping a record of who is responsible for each output.
- It creates an "owner-signed responsibility chain" — meaning every query, model response, and data transformation is cryptographically signed by the person or system that handled it.
- This matters because current local LLM setups have no audit trail; if a model generates harmful or incorrect content, there's no way to trace blame or fix accountability.
- The project targets regulated industries (healthcare, finance, legal) and teams deploying LLM agents or pipelines where compliance and traceability are required.
- It fills a gap between fully closed hosted models (where the provider controls logs) and unaccountable local usage, offering a third path: private + auditable.