GGCOS: Goal-Guided Constrained Optimal Search A New Paradigm for Strategic Planning in AI and Cyber Defense

This paper introduces a novel reasoning framework—Goal-Guided Constrained OptimalSearch (GGCOS)—designed to restructure adversarial decision-making environments byinverting traditional search paradigms. Rather than initiating from a starting state andoptimizing toward a goal, GGCOS begins at critical goal states, particularly thoseassociated with system compromise, and recursively traces backward to sever all possibleaccess paths. The framework was first validated in deterministic game settings, where itconsistently enforced draws against both algorithmic and human opponents, even whenfacing structurally advantaged adversaries. By embedding GGCOS within the MiniMaxarchitecture, the model neutralized first-move bias and demonstrated deterministicequilibrium across all configurations. Subsequently, GGCOS was extended to simulatedcybersecurity environments, where it operated as a logic-based preemptive filter, denyingattackers access to high-value target states through trajectory elimination. Unlikedetection or prediction systems, GGCOS functions by reshaping the decision space itself,offering an explainable and verifiable approach to anticipatory defense. The studyconcludes by positioning GGCOS as a strategic layer that complements existing securityframeworks and contributes to a broader shift in artificial intelligence from goal pursuitto goal prevention. The methodology described herein is protected under Patent No. 51928524