AIBOM: The Complete Guide

A global enterprise rolls out a generative AI assistant to support developers and customer service teams. Afterward, the model is updated, the new dataset is incorporated, the open-source edition is updated, and the prompts are improved. However, when an auditor, regulator, or security team asks for information about what is actually being run in production, the information is split between different tools, teams, and spreadsheets. 

The result? Increased risk and decreased visibility. 

In fact, according to Cycode’s 2026 State of Product Security report, only 19% of organizations have full visibility into where and how AI is used across development.This is exactly the type of problem that the AI Bill of Materials (AIBOM) was developed to address. 

Key Takeaways:

• An AI Bill of Materials (AIBOM) is a continuously updated inventory of AI assets (including models, datasets, prompts, dependencies, and controls) across the full AI lifecycle.

• AIBOMs expose hidden risks such as drift, policy violations, and unapproved changes that traditional security and governance processes miss.

• Manual AIBOMs do not scale. Automation is required to keep pace with modern AI development and CI/CD pipelines.

• Cycode operationalizes AIBOMs by correlating AI risk across code, pipelines, and runtime environments.

What Is an AI Bill of Materials (AIBOM)?

An AIBOM is an extensive list of every AI model, tool, and dataset used in a software application to facilitate governance and responsible AI adoption. They’re designed to enhance transparency, accountability, and governance for AI systems by generating a detailed inventory of their components and development. 

An AIBOM can be viewed as a map or schema that scrutinizes all the individual components that make up your AI system. These would include:   

• The architecture around your model’s training data 

• The types of inputs and outputs it allows 

• Its intended uses and the potential areas of risk or misuse 

• Any environmental or ethical concerns of the model  

• The model’s name, version, type, and who created it

Why AI Software Bills of Materials Are Critical for Security, Governance, and Compliance

As businesses grow with AI, they face increasing pressure from stakeholders to meet the ever-changing security, regulatory, and governance needs of their businesses. Demonstrating traceability and accountability throughout their entire AI supply chain is therefore essential. 

AI Software Bill of Materials can help by:

Strengthening Model and Dataset Integrity 

For an AI system to build trust, its components must do the same. If a company cannot see what version of the model is being used, where the training data came from, or how many times the model has been updated, then problems are going to fall through the cracks. Those gaps can then go on to become the source of security breaches, regulatory violations, or unintended model behavior. It doesn’t take much. Even a few minor, undocumented updates could destroy trust over time. 

Ultimately, AIBOMs help companies avoid these consequences by: 

• Tracking the versions, configurations, and the update history of their models, over time 

• Validating the origins of the datasets (licensing terms etc) and approve the usage rights for the datasets 

• Identifingy unauthorized modifications, substitutions, or unapproved retraining events 

Without this level of transparency, organizations run the risk of deploying compromised, out-of-date, or unverified models into production, often without realizing it until something downstream happens as a result of the deployment. 

Improving Governance and Policy Enforcement 

Governance gaps are already widespread with over half (52%) of organizations saying they lack a formal or centralized governance framework for managing AI adoption. An AIBOM helps close this gap by turning governance from static policy into enforceable, system-level controls embedded directly into AI workflows.

Using an AIBOM, companies can:  

• Define AI assets to establish clear ownership, accountability, and approvals  

• Enforce use restrictions, access controls, and deployment guardrails 

• Align AI development best practices with corporate security, privacy, and ethics policies 

 By doing so, governance moves from a reactive, manual review process to a continuous and scalable enforcement capability that can grow as fast as an organization’s AI adoption.  

Regulatory Compliance for Auditing  

More and more auditors and regulators are looking for evidence, rather than just assurance, when auditing AI systems. An AIBOM provides a traceable and organized record of how each AI system was developed, trained, validated, and deployed throughout its lifecycle.  

Some key benefits of AIBOM include: 

• Faster and more predictable audits 

• Clear documentation of all aspects of the development and deployment process 

• Less risk of non-compliance with varying regional, industry and regulatory requirements 

Fun fact: The IDC reports that organizations with automated AI asset tracking can reduce audit preparation time by up to 40% in addition to providing consistent, high-quality audit response information. 

Reducing AI Supply Chain Risks and Vulnerabilities 

The use of AI has created new and often invisible supply chain risks, including poisoning of data sets used to train AI models, trust issues related to AI model sources, and vulnerability to open source dependencies and third-party APIs. 

These risks are fragmented among teams and tools without an AIBOM.

Components of an AIBOM

An effective AIBOM captures more than just models and data. It documents every component that influences how an AI system is built, deployed, and governed, including: 

Together, these AI software Bill of Materials (AI SBOM) elements create a complete picture that will support decision-making with risk awareness.

Why AIBOMs Need Automation to Scale  

Given the fast pace that modern AI environments are changing, there’s no way that manual documentation can possibly stay current. The models are being re-trained, the data sets are being re-loaded, the prompts are evolving, the dependencies are shifting…and this is all happening many times per day. 

This environment requires automation to ensure relevance and provide genuine security and governance value. Otherwise, the BOM would become obsolete shortly after it was developed. 

Let’s explore the benefits of automation in more detail. 

Continuous Discovery of AI Components 

Development teams are continually introducing new AI components, modifying existing ones, retiring old ones, and moving them among development, test and production environments. Continuous discovery through automation will ensure that the AIBOM reflects what is truly in use by the business, not just what teams think should be in use. 

The benefits of automation include:  

• Real-time identification of new models, data sets, prompts, and dependencies within an environment 

• Consistent coverage across all development teams, repositories, and tooling 

• Identification and removal of “shadow” AI components that exist outside of the governance structure 

Continuously discovering all of the AI components in an environment eliminates the growing number of blind spots which leave security and compliance teams unaware of the unmanaged or unauthorized AI components in the environment. 

Automated Detection of AI Drift and Policy Violations  

All AI systems are subject to some degree of drift over time. As data changes and the model continues to learn and improve the model behavior will change. Therefore, it is essential to automate the process of identifying and reporting this drift to reduce the overall AI exploitability before it becomes a larger issue. 

Some key capabilities associated with automated drift and policy violation detection include: 

• Automated detection of drift across models, data sets, and inference behaviors  

• Automated alerts when policy violations occur or unauthorized modifications take place 

• Contextual risk assessments that tie the drift to business and security implications  

Identifying and addressing drift as soon as possible prevents small issues from becoming large-scale failures or compliance breaches. 

Seamless Integration with CI/CD Pipelines 

Automated application of AIBOM into a CI/CD pipeline allows for the same monitoring and transparency during the development phase. In addition, all policy checks run with each Pull Request, thus ensuring that no non-compliant components are pulled into production. This allows developers to rapidly iterate to create secure solutions without slowing down the delivery process.  

Some key capabilities associated with integrating AIBOMs into CI/CD pipelines include: 

• Automated AIBOM updates during build and deployment 

• Policy enforcement during pull request and merge workflows 
• Immediate developer feedback on security, compliance, and governance impact 

Real-Time Mapping of Dependencies and Risks 

Thanks to automation capabilities, organizations can continually map the dependencies among AI models, datasets, prompts, and third-party components, and correlate them with enterprise risks. Real-time risk scoring provides teams with the ability to prioritize remediation, understand the scope of potential impact (blast radius) across systems, and focus on the components that present the highest degree of risk. 

Some key capabilities associated with real-time dependency and risk mapping include: 

• Continuous correlation of AI components and dependencies  

• Real-time assessment of enterprise risk exposure 

• Improved prioritization of remediation efforts

Without continuous mapping, organizations may overlook high-risk vulnerabilities or misallocate limited resources to low-risk issues, placing critical AI-based systems at increased risk of failure or attack. 

Reduction of Manual Documentation Effort 

Manual maintenance of an AIBOM is time-consuming, error-prone, and not scalable. Through automation, the documentation effort required to maintain the AIBOM is greatly reduced, allowing teams to focus their efforts on mitigating risks and establishing governance rather than documenting activities. 

Some key benefits associated with reducing manual documentation effort include: 

• Lower operational overhead 

• Fewer documentation gaps and inconsistencies 

• Improved accuracy and reliability of AIBOM data

Building Your AI Bill of Materials Program: 6 Steps

A well-built AIBOM program is foundational to ensuring that your AI systems are secure, compliant, and auditable. The AIBOM program should be built upon a structured process with clearly identified roles and responsibilities, along with integration points that support current workflows. 

The following six steps serve as a template for developing an enterprise-level AIBOM program.  

1. Define Scope and Ownership for Your AIBOM Program   

The first task is to define which AI systems fall within the scope of your AIBOM program and to identify who is going to be responsible for each one. This includes internally developed models, third-party AI services, and AI components (such as models, model parameters, and suchlike) embedded within applications or in a pipeline. 

To determine your scope for your AIBOM program, determine high-priority AI use cases and focus on those that directly affect security, compliance, or business results. Then assign ownership to each AI component, making sure there is an owner to maintain its inventory and metadata.

Note: ambiguity at this point in your process will lead to duplication of effort, missed AI components, or unmonitored risks. 

2. Inventory All AI Models, Data, and Dependencies 

An inclusive AI/ML inventory provides the basis for any AIBOM. The inventory should include all models, datasets, prompts, pipelines, and third-party components such as AI libraries or services. To develop a proper inventory of your assets, automatically discover all AI assets across repositories, development environments, and production systems, ensure that your inventory includes both internally developed models and externally-sourced AI (including cloud-based services and embedded tools), and document all dependencies (whether code libraries, datasets, or prompts) to develop an inclusive AI/ML inventory. 

Remember that an incomplete inventory of AI assets creates blind spots, and blind spots create opportunities for future incidents, particularly as AI adoption expands across teams and departments. 

3. Document Risk Across The AI Supply Chain 

Each AI component presents different types of AI supply chain risks, including licensing issues, security vulnerabilities, data privacy concerns, and ethical or bias considerations. To document risks associated with your AI components, evaluate the sensitivity of the data used in the AI system, where the data originates, and the potential privacy implications to ensure that the data is being used safely, track the vulnerabilities associated with dependencies, third-party models, or embedded services, and associate each identified risk with the potential business impacts so that remediation priorities are based on real-world consequences. 

Failing to document these risks limits your ability to respond if issues arise. Additionally, failing to document risks will increase exposure during audits or incidents and may extend the remediation timeframe when vulnerabilities are found. 

4. Document Metadata And Validation Requirements 

Metadata provides important context for understanding the AI systems. Documentation of metadata must include how models were trained, validated, and approved, as well as any relevant audit history. To document all metadata requirements, document the validation criteria for each model and dataset to ensure that the models and datasets meet quality and ethics standards, document approval points to confirm that each AI component has been reviewed and approved, and document lineage and provenance to track version numbers of models, source data sets, and histories of prompts. 

Without metadata, businesses will find it difficult to demonstrate compliance, reproduce results, or provide an explanation for the decisions made by deployed AI systems. In addition, gaps in metadata will limit confidence in the output of the AI system and will likely impede incident response efforts. 

5. Integrate AIBOM Creation Into Existing Pipelines 

Your AIBOMs need to be created as part of your normal workflow, not as an afterthought. When integrated, your AIBOM will stay up-to-date, and changes to your models, datasets, or code will be reflected in your AIBOM. To embed AIBOM generation into your workflows, create automated AIBOM generation within your CI/CD pipelines, capturing changes to your AI assets in real-time, trigger updates every time you make a change to your models, datasets, or dependencies, and give your developers actionable feedback and insight, enabling them to address issues from within their own workflows. 

When you fail to integrate your AIBOM creation into your existing workflows, documentation will lag behind what is actually happening in your environment, resulting in compliance gaps, security blind spots, and operational friction. 

6. Establish Review, Approval, and Update Cycles 

AIBOMs are dynamic documents, subject to ongoing review and update cycles to keep them relevant, actionable, and compliant with your organization’s policies. To create a comprehensive review cycle for your AIBOM, develop update triggers tied to model changes, dataset changes, or policy changes, develop approval workflows verifying updates before applying them to your production systems, and tie your review cycle to your release cycle, ensuring your AIBOM reflects the current state of all your AI assets. 

Without established review and update cycles, your AIBOMs will eventually become static, eroding trust in the AIBOM and increasing the risk of an adverse event occurring due to an outdated AIBOM.

Best Practices for Managing AI Bills of Materials 

Managing bills of materials for AI requires discipline, automation, and continuous improvement. The following are the best practices that organizations should be using to ensure that their AIBOMs remain valid and effective. 

Continually Update Your AIBOM Across All Pipelines  

As we’ve said, AI environments are dynamic. Models, Data Sets, Dependencies and other items can change rapidly at times with no formal notification.

Best practices for maintaining a continually updated AIBOM include: 

• Event-driven updates: Automatically trigger AIBOM refreshes when a model, data set or dependency is changed. 

• Pipeline-based enforcement: Include AIBOM updates in the CI/CD pipeline so that every time a build is created and deployed it validates the AIBOM. 

• Automated validation: Utilize automated checks to verify new components are documented, approved and compliant. 

Validate Dataset Lineage And Model Provenance On A Routine Basis  

Your ability to trust in your AI is based upon understanding how your data and models were developed. Understanding the lineage of your data sets and the provenance of your models are critical to transparency, reproducibility, and regulatory compliance.  

Validating regularly will help to identify: 

• Unauthorized changes: identify when any data sets, training routines, or model versions have been altered unexpectedly prior to impacting production.  

• Compliance with licensing obligations: verify all data sets, models, and dependencies meet the requirements of intellectual property and open source agreements. 

• Integrity risks: Avoid using corrupted or biased data in your AI outputs and prevent hidden vulnerabilities in your AI outputs. 

 Monitor Model Performance To Identify Potential Problems Early  

 Once your AI is deployed, monitoring its performance post-deployment is a significant requirement to identify drift, misuse, or unintended behavior in your models. 

Through continuous monitoring of your models, organizations can continue to manage their AI outcomes and demonstrate trust across development teams and operations teams.  

Enforce Governance Through Policy-Based Controls 

 Governance of AI will only be successful if policies are implemented operationally. Effective SDLM governance includes implementing policy directly into the development and deployment workflow, providing real-time guardrails without impeding the velocity of development. 

 Benefits include: 

• Uniform application of security, ethical and compliance policies

• Uniform application of policies in real-time

• Auditable trail demonstrating adherence to internal and regulatory standards

Align AIBOMs with Broader SBOM and ASPM Workflows 

AIBOMs exist independently and should work in conjunction with your existing software bill of materials and Application Security Posture Management (ASPM) initiatives. This enables:  

• Unified risk visibility: understand risk related to AI, software and supply chain in one view. 

• Tool consolidation: eliminate silos by integrating AIBOMs with existing scanning, monitoring, reporting platforms.  

• Prioritization: prioritize remediation efforts based on components and vulnerabilities that present the greatest risk across the organization. 

TLDR: By linking your AIBOMs to your overall security posture, teams will receive actionable intelligence and a single source of truth for managing both AI and traditional software risk.

Cycode’s AI Software Bill of Materials Solution Provides Enterprise Protection

Creating, maintaining, and enforcing an AIBOM at scale is difficult. However, Cycode makes this easier by integrating AIBOM creation, maintenance, and enforcement directly into development and security workflows. Cycode’s solution is designed to make AIBOMs a useful tool for both proactive security and governance and as a way to empower developers. 

 Key features and capabilities include: 

• Automated AI asset discovery: The continuous detection and cataloging of AI models, datasets, prompts and dependencies across all environments will eliminate blind spots and shadow AI assets. 
• Dataset lineage and model provenance tracking: Create recordable, traceable records for each AI component, including source information, version history, training data, and approval checkpoints. 

• Risk prioritization and contextual insight: The integrated risk scoring and correlation across models, code, and infrastructure offers insight into the most important exposures. 

• CI/CD pipeline support: Directly embed AIBOM creation and policy enforcement into CI/CD pipelines so your AI artifacts are compliant and secure without slowing development. 

• Automated drift and policy violation alerts: In real time, identify changes, deviations, or unauthorized updates to the AI components to decrease the likelihood of AI being exploited and avoid system-wide failures. 

• Support for developer-centric workflows: Provide easy-to-use feedback and direction directly within IDEs, pull requests, and pre-commit hooks to ensure security and compliance are part of the way developers develop. 

• Works with existing security ecosystems: Integrates with your software bill of materials (SBOM), ASPM tools, and DevSecOps frameworks for a single point of risk management. 

 Through automation, intelligence, and user-friendly developer workflows, Cycode enables large entities to use AI safely, comply with regulations, and protect their software supply chain. 

Book a demo today and see how Cycode simplifies the AIBOM process for enterprises.