How Can Agencies Adapt Software Inventory Practices for AI Systems?
Federal agencies already have experience with software inventory initiatives through software supply chain security efforts and SBOM requirements. But adapting those practices for AI systems introduces additional complexity.
Unlike traditional software inventories, AI-BOMs must account for models, APIs, agents and data sets operating across hybrid and multicloud environments.
“Federal agencies have had far less enterprise visibility into cloud environments and third-party platforms than their on-premises counterparts,” Herckis says.
He notes that many agencies still rely on legacy cloud security tools that may fail to review assets such as containers, virtual machines and serverless functions.
“With multicloud and hybrid systems, there is a need to leverage platforms that can identify not only AI models but APIs, agents and other AI services,” he says.
That reflects the increasingly interconnected nature of AI ecosystems. A single AI-enabled workflow may rely on multiple cloud providers, third-party APIs and embedded services operating across different environments.
For agencies, the transition from SBOM to AI-BOM is not about replacing existing inventory practices but extending them. Agencies need ways to continuously identify AI components, understand their dependencies and evaluate the risks associated with those systems.
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How Do AI-BOMs Strengthen Zero Trust and Supply Chain Oversight?
As agencies continue implementing zero-trust architectures, visibility into AI systems is becoming more important.
Herckis says AI-BOMs can provide the continuous visibility needed to support those efforts.
“AI-BOMs give security teams a clear, continuous list of every AI component in the environment, how it’s configured and what it can access,” he says. “That visibility is the baseline that a zero-trust approach requires to identify and mitigate risks early.”
AI-BOMs can also help agencies understand how AI systems interact with sensitive data and external services.
“AI-BOMs also capture the relationships between AI components, so teams can see how AI systems actually operate in production and build a foundation for traceability, risk assessment and governance as AI systems evolve,” Herckis says.
That visibility becomes especially important when agencies rely on third-party AI providers. Herckis points to the Office of Management and Budget’s M-26-05 memorandum as an example of the federal government’s increasing focus on software and AI supply chain transparency.
“When working with third-party AI providers, federal agencies can look to the recent OMB memoranda and consider working into contract language a requirement that the software producer provide an SBOM or AI-BOM of the runtime production environment upon request,” he says.
Such requirements could help agencies assess exposure more quickly when vulnerabilities or supply chain risks emerge.
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How Can a Resource-Constrained Agency Launch an AI Inventory?
For many agencies, one of the biggest obstacles to AI governance is limited staffing and resources. But Herckis says agencies should avoid treating AI inventory as a manual process.
“The steps around building an inventory should be practical,” he says. “Building Excel spreadsheets of use cases is already becoming virtually impossible due to the ubiquity of AI as it becomes increasingly embedded into all aspects of digital infrastructure.”
Instead, agencies should prioritize automation and continuous visibility.
“Agencies, therefore, should focus on being able to automate the continuous inventorying of both AI models and technologies within their environments,” Herckis says.
He also recommends that agencies establish identity and access controls for AI systems themselves.
“It is also critical to consider ensuring agentic identities for each individual AI entity, limiting entitlement and data access,” he says.
As agencies mature their AI governance practices, those controls can be integrated into broader security posture management and application protection platforms.
Ultimately, the goal of an AI-BOM is not simply to catalog AI technologies. It is to provide the visibility agencies need to support AI risk management, strengthen zero-trust security strategies and govern AI systems responsibly as adoption expands across the federal enterprise.
