From SBOM to AI-BOM: Rethinking Visibility in AI-Native Systems

For years, the software bill of materials served as a reliable foundation for governance. It gave teams a way to understand what went into an application, assess risk, and meet growing compliance expectations. However, as software development shifts toward AI-native systems, that foundation begins to crumble.

Enter the AI bill of materials. An AI-BOM maps the AI-native components that now shape modern applications, spanning repositories, pipelines, and running systems. Models, agents, prompts, datasets, and supporting services all become part of the picture. Where the SBOM anchors software governance, the AI-BOM is emerging as the backbone of AI-native security.

This guide examines why SBOM-style thinking is no longer applicable in an AI-driven ecosystem, where components change frequently, and behavior is shaped at runtime rather than during the build process. It explains why organizations are turning to the AI-BOM as a foundational capability, and why visibility is the first and most urgent requirement. In a world of fast-moving tooling and pervasive Shadow AI, understanding what exists is the starting point for everything that follows.

Why SBOM thinking fails in the AI era

To understand why AI-BOM has become essential, it helps to start with what no longer works. The assumptions that shaped SBOMs were grounded in a software world where components were finite, dependencies were known, and change happened at a measured pace. AI-native systems operate under very different conditions. Before examining how organizations are adapting, it is worth considering why the familiar assumptions of the SBOM era break down in the AI era.

AI components change constantly

AI-native systems are defined by constant change. The components that power them do not sit still long enough to be treated like traditional dependencies, and that volatility is now the norm rather than the exception.

Models are updated on a weekly cadence, sometimes more frequently, as new capabilities and optimizations are released. Agent frameworks evolve just as quickly, with new abstractions, tools, and execution patterns appearing in public repositories. MCP servers are spun up to expose new tools or workflows, often in response to immediate development needs rather than long-term plans. Prompts, which increasingly act as executable logic, are edited and refined continuously. Datasets shift as new data is added, filtered, or replaced, altering model behavior without any change to the surrounding code.

These components are not version-pinned in the way libraries are. They are not centralized in a single repository. And they can be introduced by individual developers with little friction. Treating them like static dependencies creates blind spots almost immediately.

In an AI-native environment, governance has to account for components that are fluid, decentralized, and constantly in motion.

The core problem is visibility

For most security leaders, the challenge is not understanding every decision an AI system might make. CISOs are not trying to reason about agent behavior at the level of individual prompts or outputs. What they need first is something far more basic: understanding what exists.

They want to know which agents, models, and MCP servers are actually in use across the organization. They want to understand which tools or workflows those components expose, and what external systems they can reach. They need insight into the datasets and prompts shaping behavior, because those inputs often matter as much as the code itself.

Without this awareness, it becomes impossible to assess risk, set policy, or respond effectively when something goes wrong. Traditional SBOMs were never designed to provide this level of insight. They capture static dependencies at a moment in time, not the active, evolving components that define AI-native systems. As a result, they leave security teams without the visibility they need to govern AI effectively.

What security leaders are telling us

The visibility gap is no longer theoretical. It appears repeatedly in conversations with security leaders who are attempting to apply existing controls to an evolving AI landscape.

Framework proliferation

For many large organizations, the challenge starts with volume. One enterprise described a constant influx of new agent frameworks, orchestration tools, model packages, and MCP servers across its development teams. Developers adopted them quickly to move faster. Governance teams could not keep up.

Security and compliance efforts became reactive. By the time one framework was reviewed, another had already appeared. The problem was not opposition to innovation, but a lack of clear visibility into what existed.

For this organization, the AI-BOM became essential. It provided a reliable way to track AI components across repositories, establishing the baseline needed to make governance possible as the ecosystem continued to change.

AI-BOM feels early, but it’s critical.

Another organization described its approach more cautiously, but with equal urgency. The AI-BOM, they admitted, still feels early. Standards are emerging. Best practices are not yet settled. Even so, they see it as unavoidable.

AI components have crossed a threshold from experimental tooling to material dependencies. Models, agents, and supporting services now play a direct role in determining how applications behave and how data is processed. At the same time, regulators are beginning to signal expectations around AI supply chain transparency, provenance, and accountability.

Waiting for perfect guidance no longer feels like a viable option. For this organization, adopting an AI-BOM is less about maturity and more about readiness. Establishing visibility now creates a foundation they can build on as requirements evolve, rather than scrambling to catch up once expectations are formalized.

We expect our tools to track what’s new.

A third theme comes up just as consistently in these conversations: organizations no longer expect to track the AI ecosystem themselves. Security teams recognize how quickly new agent frameworks, MCP servers, model packages, prompts, and datasets appear. By the time a manual review process is defined, the landscape has already changed. Keeping an authoritative inventory through human effort alone is no longer realistic.

As a result, organizations expect Snyk to maintain that awareness on their behalf. They rely on automated discovery to surface new components as they emerge, maintaining current visibility without requiring constant intervention. In an ecosystem that evolves this fast, automation is not a convenience; it is the only way visibility can keep pace.

The new AI attack surface: visibility first, everything else second

Once organizations recognize how quickly AI components proliferate and how limited manual tracking has become, the focus shifts from tooling choices to exposure.

The explosion of shadow AI

As AI adoption accelerates, a new class of exposure has emerged alongside it. Shadow AI has expanded far beyond isolated experiments or one-off tools. It now encompasses a growing collection of components that quietly shape how applications behave without ever passing through formal review.

Unapproved agents are spun up to automate tasks or orchestrate workflows. Undocumented MCP servers expose new tools and capabilities without clear ownership. Ad hoc wrappers connect models to internal systems, often created to solve an immediate problem and then left in place. Hidden LLM calls appear inside application logic, while prompt chains become embedded as functional decision paths rather than simple inputs.

Individually, many of these elements appear benign. Together, they create an AI attack surface that is difficult to map and easy to overlook. Shadow AI is not defined by intent or sophistication, but by invisibility. When components exist outside standard discovery and governance paths, they introduce risk simply because they are unknown.

Real visibility gaps

These gaps show up in real environments. In one case, an enterprise discovered an active MCP server during a routine demo. AppSec had no prior knowledge of it. Nothing was misconfigured. Nothing was exploited. The server was simply invisible to the controls the team relied on. Across organizations, the pattern is consistent: AI components that materially affect behavior exist outside formal discovery paths, not because of negligence, but because existing controls were never designed to see them.

The AI-BOM: The new source of truth

Taken together, these gaps point to the need for a new source of truth. The AI-BOM fills that role by providing the visibility that SBOMs were never designed to deliver in an AI-native environment.

An AI-BOM inventories the components that define how AI systems operate. It tracks models in use, the agents built on top of them, and the tools and chains those agents invoke. It captures MCP servers and the capabilities they expose, along with the prompts and datasets that shape behavior. It also accounts for external AI APIs that extend functionality beyond the application boundary.

That visibility creates a foundation for action. Governance becomes grounded in reality rather than assumptions. Monitoring can focus on what exists. Red-teaming efforts can target real configurations, rather than hypothetical workflows. The AI-BOM moves from inventory to operational backbone.

Why AI-BOM ≠ SBOM

Although the term may sound familiar, an AI-BOM is fundamentally different from a traditional SBOM. SBOMs were designed for software systems with stable components, predictable updates, and clear dependency trees. AI-native systems operate under different conditions.

These differences have practical consequences for governance, monitoring, and posture management.

Why automation is essential and why Snyk leads

The scale and speed of change in the AI ecosystem make one thing clear: manual tracking cannot keep up. At this scale, relying on human review breaks down. By the time a new component is identified, it may already be in use across multiple teams or workflows

This is where automation becomes essential. Continuous discovery and classification are the only practical ways to maintain an accurate view of AI components as they emerge and change. Snyk leads in this area by pairing automated detection with ongoing research, ensuring that new frameworks, MCP servers, model patterns, and datasets are recognized as soon as they appear.

Snyk’s research edge

Automation alone is not enough without insight to guide it. Knowing what to scan matters just as much as scanning continuously.

Snyk’s dedicated AI supply chain research team focuses on understanding how the ecosystem evolves in real-time. They track new agent frameworks as they emerge, analyze new MCP patterns and usage models, and study emerging AI attack vectors as they are discovered in the wild. They also follow the rapid expansion of model families and dataset types, recognizing how these shifts introduce new dependencies and new forms of risk.

This research directly informs how the AI-BOM remains current. Customers do not need to monitor every new release, framework, or pattern themselves. Snyk does that work on their behalf, translating ecosystem change into detection and context they can rely on. That is the core value of a research-powered AI-BOM: visibility that evolves as fast as the AI supply chain itself.

AI-BOMs as the foundation of AI governance and AI-SPM

As AI systems move from experimentation into core business workflows, the need for a reliable foundation becomes unavoidable. Just as SBOMs evolved from a best practice into a requirement for software governance, AI-BOMs are on a similar path for AI-native environments.

They provide the visibility needed to support governance decisions, protect data, guide red-teaming efforts, monitor agent activity, and prepare for regulatory scrutiny. Signals from regulators are already pointing in this direction

But visibility alone is not enough. As AI estates grow, organizations need a way to continuously understand posture across hundreds of models, agents, datasets, and connectors, not as static inventories, but as living systems that change daily. This is where AI-BOMs become a foundational input to AI Security Posture Management (AI-SPM).

AI supply chains move too fast for SBOM-era thinking. Shadow AI continues to expand. Oversight is increasing. In this environment, the AI-BOM becomes the essential source of truth for AI-native systems.

If SBOMs shaped the last decade of software security, AI-BOMs will shape the next. Snyk is building the research-driven foundation that enables AI-BOMs to evolve into full AI-SPM, transforming raw inventory into continuous, actionable security insight.

Discover how Snyk Evo enhances AI-BOM visibility, revealing the inner workings of AI systems and laying the groundwork for a new approach to AI security posture. Explore what research-driven AI security looks like in practice.

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