Security researcher Zack Korman has published analysis arguing that security limitations, not model capabilities or tooling, represent the primary bottleneck constraining enterprise AI adoption. The research contends that organizations possess AI technology capable of transforming operations but cannot deploy it at scale due to unresolved security concerns that outpace mitigation strategies.

Korman analysis identifies a fundamental mismatch between AI capability advancement and security solution development. While models grow more powerful and tools more sophisticated, the security mechanisms required to safely deploy these capabilities in enterprise environments lag significantly behind. Organizations find themselves with AI that could deliver substantial value but cannot be trusted with the access required to realize that value.

The bottleneck manifests across multiple dimensions including data security concerns about exposing sensitive information to AI systems, operational security worries about AI actions affecting production systems, and compliance uncertainty about AI decision-making in regulated contexts. Each dimension requires security solutions that current practices cannot adequately provide.

Enterprise security teams report spending more time constraining AI capabilities than enabling them. Guardrails designed to prevent misuse often eliminate the autonomous operation that makes AI agents valuable. The result is AI deployments that are either too restricted to deliver promised benefits or too permissive to satisfy security requirements.

Korman recommends that the AI industry prioritize security research investment proportional to capability research, develop practical security frameworks that enable rather than prevent AI deployment, and recognize that security advancement is prerequisite to AI value realization. Organizations should advocate for security tooling as aggressively as they pursue AI capabilities.