RNSek
RNSek is a term used in some discussions of secure artificial intelligence to describe a theoretical framework for building resilient neural network systems. The concept arises from concerns over adversarial manipulation, data integrity, and operational reliability in AI deployments. In its proposed form, RNSek encompasses a layered architecture combining secure data pipelines, verifiable model training, and runtime safeguards to monitor and respond to anomalies.
The core principles include integrity, confidentiality, availability, and traceability throughout the AI lifecycle. Proponents emphasize secure
Typical components include a secure data inlet with provenance tagging; a reproducible training workspace with cryptographic
Usage and status: RNSek is not a standardized technology and does not refer to a single widely