Functor Model Transparency and Auditability
https://github.com/AutonomicAI/functor-resnet/tree/main/slm_template shows a refactoring of the model into separate files. For example, a file f.py is used for the model function, delta.py for model changes that are applied and w.py for the weights function. Every changed to the model is logged to an enterprise streaming event log and versions in a source code repository for these files can be referenced. The model.py code now references these other files as well as containing model functionality. It resembles an orchestrator or a controller in a sense. model.py => { load current functions, route inference, invoke components, invoke state/context representation functions, apply governed updates, emit provenance } With a specialized VCS tied into normal engineering workflow, a model change can become an ordinary governed change record. Then an auditor can traverse the chain in either direction answering: who changed it? what changed? why it changed? which requirement authorized it? what evidence supported it? which tests passed? which model version resulted? That is much richer than conventional model observability. So, there are really two layers: prediction audit - explain why a certain output prediction was given evolution audit - explain the current manifestation of the model Simple integration of the event log with the version control system and the project management tooling such as Microsoft Planner Functor Models may be unusually strong on the second because the change itself is a first-class artifact. The Microsoft Planner/Jira/PR integration matters because we are not asking enterprises to invent a new governance process. We are mapping model evolution into processes they already use for software change control. The specialized version control is a separate topic in as sense as we could see some significant energy savings via reuse in learning/training computation. A VCS with sophisticated semantic searching, fragmentation matching (we computed learning with a expression containing reusable piece for example) could save computation. This is preliminary research and requires further investigation of course. "Treat a learned model change with the same rigor as a production code change". For regulated environments, that is a very compelling proposition.
What are the recommendation and procedures for flagging data as CUI and managing it?
In preparing for our CMMC Certification, as a company that is heavily uses SharePoint and Teams in the Office365 cloud environment -- what are the recommendations and procedures for flagging data as CUI and managing it?