Shadow IT has given way to shadow AI. Enter AI-BOMs

As artificial intelligence becomes increasingly embedded throughout enterprise environments, a new security threat has emerged that traditional safeguards cannot address. Shadow AI—the unauthorized or undocumented use of AI tools and models within organizations—now poses a significant risk to enterprise security and compliance. Unlike shadow IT of the past, which involved unsanctioned software applications, shadow AI operates at a deeper, more complex level, requiring new visibility and inventory solutions known as AI-BOMs (AI Bills of Materials).

The transition from shadow IT to shadow AI represents a fundamental shift in how organizations must approach security and governance. While traditional software bills of materials (SBOMs) once provided comprehensive visibility into application components and dependencies, they no longer capture the full scope of AI systems operating within enterprise environments. This visibility gap creates substantial risks, particularly as AI agents and applications proliferate across departments without centralized oversight or documentation. The challenge lies in the fact that AI components—including models, training data, and inference systems—operate differently than traditional software, making conventional inventory methods insufficient.

• Compliance Risk: Undocumented AI systems may violate regulatory requirements for transparency, auditability, and data governance across industries
• Security Vulnerabilities: Unknown AI implementations cannot be properly secured, monitored, or updated, potentially exposing sensitive enterprise data
• Model Governance Gaps: Shadow AI prevents organizations from understanding which AI models are in use, who trained them, and what data they access
• Supply Chain Exposure: Untracked AI components increase the risk of compromised models or malicious AI agents entering the enterprise ecosystem
• Operational Inefficiency: Multiple teams deploying redundant or conflicting AI solutions without coordination wastes resources and creates inconsistent outcomes

As enterprises accelerate AI adoption to maintain competitive advantage, the inability to see and control AI implementations threatens both security posture and regulatory compliance. Organizations that lack visibility into their AI infrastructure cannot effectively manage risks, ensure responsible AI deployment, or respond to emerging threats. The emergence of AI-BOMs addresses this critical gap, enabling enterprises to establish comprehensive inventories of AI components and maintain the governance necessary in an increasingly AI-driven business environment.