Governing What You Cannot Inspect: Multi-Signal Behavioral Analysis for Post-Opacity Agent Accountability

Traditional AI audit methods depend on access to source code, model weights, or training data. Converging industry trends—including the disappearance of human-written code, the inherent opacity of neural network reasoning, and the proprietary closure of training datasets—render these approaches structurally obsolete. This position paper proposes a six-layer Multi-Signal Behavioral Analysis (MSBA) framework integrating techniques from forensic linguistics, process mining, malware fingerprinting, and information theory into a unified governance pipeline. We position MSBA as the analytical complement to the Agentic Accountability Layer (AAL) settlement architecture, and establish the category of post-opacity agent governance. Fifth paper in the OIA Lab series: P1: Cognitive Leakage Unified Framework (v2.1) — DOI: 10.5281/zenodo.18525055 P2: Responsibility Completion for Agentic AI (v1.3) — DOI: 10.5281/zenodo.18524766 P3: From Data Leakage to Intent Leakage (v1.3) — DOI: 10.5281/zenodo.18524974 P4: Intent Leakage by Design (v1.0) — DOI: 10.5281/zenodo.18526555 P5: Governing What You Cannot Inspect (this paper, v1.1)