Research¶
Centaur Security Labs produces practitioner research on AI-augmented security operations: how to design systems that meet evidentiary and compliance standards, how to measure AI quality in operational environments, and what the correct division of labor looks like between human analysts and automated tooling.
The papers here are technical reports derived from operational experience building ARCHER and Sagittarius. They have not completed formal peer review. The argument and structure of each report are complete; remaining open items (empirical measurements gated on corpus data, footnotes requiring institutional journal access for final DOI/pagination) are tracked inline.
Published¶
The Centaur Framework →¶
The Centaur model has been described as a philosophy and advocated as a strategy. This paper makes it a specification — defining the three-layer responsibility architecture, documenting the failure modes that result when boundaries are violated, and deriving concrete design requirements for any tool that claims to implement the model correctly.
The Stochastic Trap →¶
Current AI security tools fail not because they are insufficiently capable, but because they apply probabilistic reasoning to roles that demand deterministic behavior. This paper names that failure mode, identifies the three architectural patterns that produce it, and argues that the remedy is not better models but a principled separation between the work that probabilistic systems do well and the work they are constitutionally incapable of doing reliably.
More papers are in preparation. See the Pending Release page for titles and abstracts. Subscribe via RSS to be notified when new papers publish.