How Federal Agencies Can Inventory and Govern AI Systems With AI-BOMs

Why It Matters

Without an AI‑BOM, agencies cannot assess data exposure or third‑party risks, undermining zero‑trust security. The framework enables proactive governance of a rapidly expanding AI supply chain across the federal enterprise.

Key Takeaways

• AI‑BOM inventories models, data sets, APIs, and services across agencies
• Shadow AI proliferates in dev environments, bypassing governance controls
• Continuous AI inventory supports zero‑trust and supply‑chain risk mitigation
• Automation, not spreadsheets, is essential for scalable AI governance

Pulse Analysis

The federal government’s push to modernize operations has accelerated the deployment of generative AI, AI‑enhanced SaaS platforms, and code‑generation assistants such as GitHub Copilot and Claude Code. While these tools promise productivity gains, they also expand the attack surface by introducing components that sit outside traditional IT inventories. Agencies now confront a modern version of the classic shadow‑IT problem—“shadow AI”—where developers spin up models or APIs without centralized oversight. This lack of visibility hampers risk assessments, data‑privacy compliance, and the ability to enforce consistent security policies across multi‑cloud environments.

An AI bill of materials (AI‑BOM) offers a systematic remedy by cataloguing every AI artifact—trained models, training data sets, inference APIs, supporting frameworks, and the underlying infrastructure. Unlike a conventional software SBOM, which tracks static code libraries, an AI‑BOM captures dynamic dependencies and data flows that can change with each model update. For agencies pursuing zero‑trust architectures, the AI‑BOM becomes the baseline inventory required to verify who or what can access sensitive datasets and external services. The recent OMB M‑26‑05 memorandum reinforces this approach, urging contractors to supply AI‑BOMs alongside traditional SBOMs for greater supply‑chain transparency.

Implementing an AI‑BOM at scale demands automation rather than manual spreadsheets. Continuous discovery tools that integrate with cloud‑security platforms can surface AI containers, serverless functions, and third‑party APIs in real time, feeding a centralized repository that security teams can query. Smaller agencies should prioritize identity‑centric controls for each AI agent, limiting entitlements and data exposure. As AI models become more pervasive, a disciplined AI‑BOM process will not only satisfy regulatory expectations but also enable rapid remediation when vulnerabilities emerge, ensuring the federal enterprise can reap AI benefits without compromising its security posture.