Mathematical Governance Infrastructure for Autonomous Systems
Abstract
Mathematical Governance Infrastructure for Autonomous Systems ruahAI provides constraint-based governance frameworks for autonomous systems in legally fragmented environments. Specifications define system boundaries through mathematical operators, enabling fail-closed behavior by design. Core Features:- Sensor-agnostic perception abstraction- Deny-by-default fusion permission matrices- Autonomy envelopes with monotonic restriction under ambiguity- Perception-to-governance state translation- Adversarial awareness protocols- Formal audit trace generation Delivery Formats:Mathematical operators (LaTeX/PDF), Infrastructure-as-Code (Terraform/OPA), policy kernels (JSON/YAML), solver-ready formulations (MIP/CVXPY), verification artifacts (Z3/Dafny), natural language specifications. Applications:Defense (sensor fusion, coalition ops), Finance (AML compliance), Municipal Administration, Hardware Governance (AI chips). Licensing: Non-exclusive commercial. Unlimited internal use. No IP transfer. Derivative rights retained by author. Author: Winfried Brueckner (ruahAI Kleingewerbe)ORCID: 0009-0009-0008-5263Contact: brueckner@bw-ruah.de | https://bw-ruah.de
Fields of Study & Subjects
Access Control and Trust
Ethics and Social Impacts of AI
Safety Systems Engineering in Autonomy
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 15 January 2026
- Language
- EN