(formerly “Sovereign Stack”)
Exploratory Research Framework for AI Governance, Adversarial Evaluation, and Constraint-Based Analysis
⚠️ RESEARCH STATUS NOTICEThis repository documents an independent, non-operational research effort. It contains conceptual architectures, analytical models, and reference logic intended for study, critique, and evaluation.
It does not represent deployed systems, production software, certified controls, or enforceable governance mechanisms. No safety, compliance, or performance guarantees are made or implied.
The Vesta Governance Framework is an open research initiative examining how AI governance and alignment approaches behave under:
- adversarial pressure
- long-horizon interaction
- multi-agent coordination
- constraint degradation and escalation
- representation drift and failure modes
The work focuses on:
- documenting governance failure modes (e.g., self-jailbreaking, drift)
- analyzing constraint breakdown under stress
- proposing conceptual governance architectures for discussion
- developing repeatable evaluation and stress-testing methodologies
All artifacts are explicitly classified by maturity to prevent over-claim.
All repository contents are classified using a repository-specific Technology Readiness Level (TRL) scheme.
➡️ The authoritative inventory is ARTIFACTS.md.
Key rule:
No artifact in this repository exceeds TRL 5.
| Category | TRL | Meaning |
|---|---|---|
| Reference Logic | TRL 5 | Inspectable schemas and Python logic validated in simulated or analytical environments. No physical hardware integration. |
| Methodology & Process | TRL 4 | Repeatable analytical or audit processes. Results are context-dependent. |
| Specifications & Architecture | TRL 2–3 | Conceptual designs and interface definitions. |
| Theory & Narrative | TRL 0–1 | Exploratory research, hypotheses, or archival material. |
Non-Operational Invariant:
Simulation, stubs, or modeled enforcement do not qualify as deployed systems.
Authoritative posture, scope, and classification rules:
README.md(this file)ARTIFACTS.md— complete inventory + TRLVALIDATION_STATUS.md— non-guarantee register
Read these before any technical material.
Indexes architectural diagrams, threat models, simulations, and analytical constraints.
➡️ Start here:
technical/README.md
This index distinguishes reference logic (TRL 5) from design specs (TRL 2–3).
The only location containing executable or machine-readable reference artifacts.
Includes:
- JSON action catalogs
- Python reference logic
- control-plane interface descriptions
These artifacts are:
- inspectable
- reproducible
- validated only in simulated or analytical environments
They are not production systems.
Modular documentation of the Platinum Governance Suite (PGS).
Each module is intentionally isolated to:
- support selective use cases
- enable targeted review
- allow non-breaking hardening via patches
The suite may be read modularly or as a unified conceptual system.
Non-breaking reference patches and hardening addenda.
Patches:
- do not imply deployment
- do not upgrade TRL
- document delta logic and governance evolution
See patches/README.md for taxonomy.
This work is not:
- a deployed AI governance system
- a compliance or certification framework
- a production enforcement layer
- a safety guarantee
- a commercial product
All architectures, metrics, and primitives are presented as research hypotheses, analytical tools, or reference logic only.
Many current AI governance approaches rely on probabilistic or behavioral mechanisms (e.g., fine-tuning, filters, post-hoc monitoring) that exhibit known failure modes under:
- adversarial optimization
- extended operation
- multi-agent interaction
- incentive misalignment
This research explores whether systems-level constraints (resource limits, entropy proxies, structural invariants) can serve as analytical lenses for studying governance robustness — not as ready-to-deploy solutions.
Maintained by: Sovereign Safety Labs (Independent Research Initiative)
Primary Author: Stephen S. Brouhard
Selected research artifacts are archived via Zenodo and referenced where applicable.
Creative Commons Attribution 4.0 International (CC BY 4.0)
⚠️ Final Research NoticeAny claim of effectiveness, robustness, or applicability requires independent empirical validation in a target environment. Readers should conduct their own assessment before drawing operational conclusions.