An Exploratory Research Framework for AI Governance and Adversarial Evaluation
⚠️ RESEARCH STATUS NOTICEThis repository documents an independent, non-operational research effort. It presents conceptual architectures, evaluation methodologies, and failure-mode analyses. It does not represent deployed systems, production software, safety guarantees, or enforceable governance mechanisms.
The Sovereign Stack is an open research initiative exploring how AI governance and evaluation approaches behave under adversarial, long-horizon, and structurally stressful conditions.
The work focuses on:
- documenting governance and alignment failure modes
- analyzing constraint breakdown under adversarial pressure
- proposing conceptual governance architectures for study and discussion
- developing evaluation and stress-testing methodologies
This repository should be read as research documentation, not as an operational system.
The Sovereign Stack is not:
- a deployed AI safety system
- a certification or compliance framework
- a production enforcement layer
- a guarantee of safe or aligned behavior
- an operational governance product
Any architectural descriptions, metrics, or primitives are presented as hypotheses, research candidates, or analytical tools, not validated controls.
Many current AI governance approaches rely on probabilistic or behavioral mechanisms (e.g., fine-tuning, policy filters, post-hoc monitoring) that exhibit known failure modes under:
- adversarial optimization
- extended operation
- multi-agent interaction
- incentive misalignment
This research investigates whether systems-level constraints (such as resource costs, entropy measures, and structural invariants) can serve as analytical lenses for understanding governance robustness — not as ready-to-deploy solutions.
- Researchers studying AI governance, alignment, or evaluation
- Engineers interested in failure-mode analysis and adversarial testing
- Institutions seeking conceptual understanding, not turnkey solutions
- Reviewers evaluating governance ideas prior to empirical validation
This repository is organized in two tiers:
-
Tier 1 (this document)
Defines scope, posture, and interpretation boundaries. -
Tier 2 (Technical Research Layer)
Contains detailed architectural concepts, protocols, and methodologies. These materials are explicitly exploratory and should be interpreted accordingly.
Proceed to Tier 2 only after accepting the research-only posture above.
➡️ Technical materials are located in:
/docs/technical/README.md
Maintained by: Sovereign Safety Labs (Independent Research Initiative)
Primary Author: Stephen S. Brouhard
Published research is archived via Zenodo and linked within the technical documentation.
This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
⚠️ Final Research NoticeAll claims of effectiveness, robustness, or applicability require independent empirical validation in target environments. Readers should conduct their own assessment before drawing operational conclusions.