The five blind spots in AI governance nobody instruments for

In a recent article on Dev.to, developer Kironov Laziz explores the critical gaps in current AI governance frameworks. While many organizations rely on basic dashboards and logs, the author argues that these tools fail to address the structural vulnerabilities that emerge between system layers. The five identified blind spots include: shadow AI, prompt-level data leaks, lack of agent delegation constraints, the absence of expiration for delegated permissions, and the lack of cryptographically verifiable audit trails. Laziz suggests that effective governance requires moving beyond passive observation toward active, structural enforcement—such as gateway-level masking, monotonic TTLs for agent tasks, and signed authorizations. The author also introduces an open-source project, Provenza, designed to address these provenance and security challenges. By treating AI governance as a network and security problem rather than a policy exercise, developers can better secure their autonomous systems against unauthorized actions and data exposure.