Invariant-Centered, Agent-Assisted Defense: A Security Architecture for Unbounded Attack Techniques and Unenumerable Attack Objectives

Abstract

Generative AI has industrialized the attack on human judgment: voice cloning, real-time video synthesis, and hyper-personalized phishing convert social engineering from a craft into a commodity, while a residual category of attacks — those that redefine the objective itself, as ransomware once did — cannot be enumerated in advance. We propose a defensive architecture for these two conditions built on a single organizing claim: under unbounded attack techniques and unenumerable attack objectives, the highest-value security investments are those that hold regardless of technique. The architecture layers a mesh of strictly advisory AI sentinel agents, which detect cross-surface incoherence rather than synthetic content, above a deliberately simple, non-interpretive enforcement core holding a small set of hard invariants that no intelligence — human or artificial — can rewrite quickly. We give the reactive-defender objection a full treatment: the claim that any AI-era defense necessarily responds late. Our answer is that the objection is correct for every layer that must recognize attacks, and that the architecture is designed around that concession — detection layers are built for their own capture, while the invariant core does not race because it constrains consequences rather than recognizing techniques. The framework relocates residual reactivity to a single measurable point, the interval between the world changing and the invariant set catching up, and proposes that interval as the headline resilience metric. We state residual risks explicitly and outline an evaluation agenda.