What question did this study set out to answer?

This work aims to establish a new framework for governing AI agents whose inner workings are not transparent.

February 14, 2026Open Access

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

Key Points

This work aims to establish a new framework for governing AI agents whose inner workings are not transparent.
Proposes a six-layer Multi-Signal Behavioral Analysis framework.
Integrates techniques from multiple fields including forensic linguistics and process mining.
Positions MSBA alongside the Agentic Accountability Layer settlement architecture.
Suggests that traditional AI audit methods are rendered obsolete.
Advocates for a novel approach to ensure accountability in AI systems.

Abstract

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)

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Cite This Study

Yuchia Chang (Thu,) studied this question.

synapsesocial.com/papers/699011932ccff479cfe585dd https://doi.org/https://doi.org/10.5281/zenodo.18624513

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