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Securing the AI Future: Leveraging AI/ML Bill of Materials to Mitigate Cyber Threats


Implementing an AI/ML BOM as part of a comprehensive cybersecurity strategy ensures a proactive stance against the sophisticated threats targeting AI-driven technologies. It not only aids in fortifying defenses but also ensures the ethical and transparent development of AI systems, reinforcing trust among stakeholders and paving the way for the secure advancement of AI technologies. For Chief Information Security Officers (CISOs) navigating the complexities of securing AI/ML systems, embracing the AI/ML BOM is a strategic step toward safeguarding the future of innovation while mitigating emerging cyber threats in an ever-evolving digital landscape.

In the evolving landscape of cybersecurity, Artificial Intelligence (AI) and Machine Learning (ML) technologies accelerate the pace of innovation while also uncovering a multifaceted arena of vulnerabilities. This blog aims to illuminate how AppSec professionals can not only comprehend these specific attack vectors but also enhance their defenses against the intricate threats poised to undermine our AI-centric future through leveraging an AI/ML Bill of Materials (BOM). By integrating an AI/ML BOM into cybersecurity strategies, organizations gain the visibility needed to see, know, and manage every facet of their AI environment and ML models, fortifying their infrastructure against the evolving landscape of cyber threats.

The Genesis of Traditional Cyber Kill Chains

The Cyber Kill Chain®, trademarked by Lockheed Martin, has been a cornerstone in understanding and thwarting cyberattacks. Detailing the sequential phases an attacker traverses, from reconnaissance to achieving their ultimate goals, this model has been invaluable for navigating the landscape of conventional cyber threats. The MITRE ATT&CK® framework complements this by offering a granular view of attacker techniques across the kill chain stages, enhancing our mitigation strategies.

The Evolution: AI Application Kill Chain

The advent of AI and ML systems introduces complexities that demand a refined analytical lens. The MITRE ATLAS™ framework, designed specifically for AI and ML systems, marks a pivotal shift towards addressing the sophisticated challenges posed by these technologies in cybersecurity. It provides a structured approach to understanding attacks against AI/ML systems, highlighting the nuanced tactics and techniques adversaries might employ.

Bridging the Gap with an AI/ML Bill of Materials (BOM)

An AI/ML Bill of Materials (BOM) is a comprehensive inventory that details all components and dependencies within an AI/ML system, including datasets, algorithms, models, and associated metadata. It offers transparency into the AI/ML development process, enabling better insight into the system's components and their origins. This level of transparency is crucial for assessing and managing security risks in AI/ML systems, akin to software components in traditional applications.


Unlike Software BOM (S-BOM) and Product BOM (P-BOM), which focus on software components and physical product parts respectively, the AI/ML BOM specifically targets the elements of AI and machine learning systems. It addresses risks unique to AI, such as data poisoning and model bias, and requires continuous updates due to the evolving nature of AI models.

The Risks of Not Having an AI/ML BOM

Lacking an AI/ML BOM can leave hidden vulnerabilities in datasets and models undetected, pose compliance challenges with regulatory standards, lead to operational inefficiencies, and potentially damage public trust if AI systems behave unpredictably or unethically due to unknown elements.

The Benefits of Implementing an AI/ML BOM

Creating an AI/ML BOM can significantly enhance security by enabling the proactive identification and mitigation of AI/ML-specific risks. It supports regulatory compliance, operational efficiency, and fosters trust and transparency by demonstrating a commitment to ethical and secure AI practices.

Understanding and Mitigating the AI/ML Cyber Kill Chain with AI/ML BOM

Integrating an AI/ML BOM into the cybersecurity strategy provides AppSec professionals with a detailed understanding of the AI/ML components at each stage of the kill chain. This knowledge is instrumental in identifying potential vulnerabilities early in the development process and crafting targeted defenses against sophisticated attacks.

Reconnaissance: The AI/ML BOM aids in anticipating areas attackers might target for information gathering, allowing security teams to flag and review known vulnerabilities.

Weaponization: Insights into algorithms and models help identify weaknesses that could be exploited to create adversarial inputs, strengthening defenses against such techniques.

Delivery: Documenting pipelines and applications touched by the AI/ML system enables the implementation of targeted safeguards at critical data ingestion points.

Exploitation: Detailed views of model architectures from the BOM help implement robust anomaly detection and input validation mechanisms.

Installation: The BOM's record of dependencies highlights areas where malicious code could be injected, aiding in the identification of unauthorized modifications.

Command and Control: Information on the model's communication mechanisms helps ensure secure command reception and integrity verification.

Actions on Objectives: Transparency into the system's components allows for the implementation of safeguards detecting and preventing unauthorized actions.


In addressing the intricate vulnerabilities and sophisticated attack vectors inherent in AI and ML technologies, the need for a comprehensive platform that enables organizations to See, Know, and Manage their entire AI environment and all ML models across an enterprise's infrastructure is paramount. Protect AI emerges as the most comprehensive solution in the industry for AI security, risk management, and governance. Its managed services, Radar and Guardian, empower organizations to navigate the complex cybersecurity landscape of AI and ML with unparalleled confidence and efficiency. Radar offers an advanced solution for securing AI and ML systems, facilitating the detection and mitigation of risks efficiently, thereby safeguarding against vulnerabilities that could compromise the integrity and security of AI-driven technologies. On the other hand, Guardian revolutionizes ML supply chain security by enabling enterprise-level enforcement and management of model security, effectively blocking unsafe models from entering your environment. As the leading tool for model scanning, Guardian not only enforces model security and detects malicious code but also integrates seamlessly with existing MLOps workflows. This comprehensive capability to See, Know, and Manage the entirety of an AI/ML landscape ensures that organizations can uphold the security and compliance of their AI and ML systems.

In this era of AI and ML, securing these technologies extends beyond protecting data and systems; it encompasses ensuring the ethical development and deployment of AI, maintaining public trust, and fostering responsible innovation. Protect AI stands at the forefront of this endeavor, offering the tools and expertise necessary to navigate the evolving threats targeting AI and ML technologies. To discover more about how Protect AI can enhance your organization's approach to AI security and give you an AI/ML BOM, book a demo of the industry's premier AI security platform.