The same adversarial principle predicts robustness across 6 security domains
Adversarial Control Analysis (ACA) — the principle that system robustness depends on which features an attacker can manipulate — predicted security outcomes correctly across 6 different domains: network intrusion detection, fraud detection, vulnerability prioritization, agent security, supply chain analysis, and post-quantum cryptography migration. Why this matters Security teams typically treat each domain as its own silo with its own threat models, its own tools, and its own assessment frameworks. But the underlying adversarial dynamic is the same everywhere: an attacker controls some inputs, the defender controls others, and robustness depends on the ratio between them. ACA formalizes this into a repeatable methodology. When I applied the same feature controllability analysis across all six domains, the systems with the highest ratio of attacker-controlled features were consistently the least robust — regardless of model architecture, data modality, or deployment context. ...