Effective AI governance cannot rely on principles alone. As artificial intelligence systems increasingly influence high-stakes decisions across security, finance, public administration, and justice, governance depends on the ability to reconstruct, verify, and contest AI-assisted outcomes through evidence. This article argues that forensic thinking—rooted in traceability, evidentiary integrity, and explainability—is a foundational requirement for governing AI responsibly. Drawing on established forensic methodologies and current AI governance frameworks, the analysis demonstrates how evidence-based practices transform abstract accountability into operationally defensible governance. It explores why traceability is essential for responsibility allocation, why explainability must support decision reconstruction rather than surface-level transparency, and how AI governance with AI introduces new legitimacy risks when evidentiary safeguards are absent. By positioning forensic discipline as a governance capability rather than a technical afterthought, the article highlights how institutions can mitigate automation bias, reduce systemic risk, and preserve decision legitimacy in AI-amplified environments. The result is a governance model grounded not in compliance rhetoric, but in defensible, auditable, and ethically grounded decision-making.