AI Threat & Risk Management: Adversarial Testing, Red Teaming & Supply Chain Security
As artificial intelligence (AI) transitions from experimentation to enterprise-scale deployment, organizations are increasingly integrating AI into customer engagement, analytics, decision support, automation, and operational workflows. While these capabilities offer significant business value, they also introduce new categories of risk that extend beyond traditional cybersecurity concerns. Unlike conventional software systems, artificial intelligence models operate probabilistically, rely on complex data pipelines, and interact with prompts, retrieval mechanisms, autonomous agents, external tools, and third-party services. These characteristics create unique attack paths, including prompt injection, data poisoning, model inversion, model extraction, excessive agent autonomy, and supply chain compromise. As a result, AI security can no longer be viewed solely as a technical function; it must be managed as an enterprise governance, risk, and assurance discipline aligned with organizational risk tolerance, regulatory expectations, and executive oversight.
In EC-Council’s whitepaper, “AI Threat & Risk Management: Adversarial Testing, Red Teaming & Supply Chain Security,” we examine how organizations can establish a structured approach to identifying, assessing, and managing AI-specific threats throughout the AI lifecycle. The paper presents a governance-centric framework that integrates enterprise risk management principles with leading industry guidance, including the NIST AI Risk Management Framework (AI RMF), the NIST Generative AI Profile, MITRE ATLAS, and the OWASP LLM Top 10. Together, these frameworks provide organizations with practical mechanisms to classify AI risks, understand adversarial attack techniques, measure exposure, and implement governance controls that support secure and trustworthy AI adoption.
The whitepaper further explores AI risk taxonomy, AI-specific threat modeling, adversarial testing methodologies, and AI red teaming as a continuous assurance control. Particular emphasis is placed on the use of structured threat intelligence and attack mapping to evaluate model behavior, validate security controls, and assess resilience against real-world adversarial techniques. The paper also examines the growing importance of AI supply chain security, highlighting risks associated with third-party models, datasets, APIs, plugins, and orchestration layers. In this context, emerging practices such as AI Software Bills of Materials (AI SBOMs/AIBOMs) are discussed as mechanisms for improving transparency, traceability, vendor assurance, and governance oversight across increasingly complex AI ecosystems.
Because AI incidents can have operational, regulatory, financial, and reputational consequences, the paper also addresses AI-specific incident response, crisis management, and business continuity considerations. It outlines approaches for integrating AI incidents into existing governance, risk, and compliance processes while supporting continuous monitoring, post-market oversight, and ongoing assurance activities.
Ultimately, “AI Threat & Risk Management: Adversarial Testing, Red Teaming & Supply Chain Security” provides security leaders, risk professionals, governance practitioners, and executive stakeholders with a practical roadmap for building resilient AI security programs. By treating AI risk management as a continuous, measurable, and governance-driven discipline, organizations can strengthen trust, improve accountability, enhance regulatory readiness, and support the secure adoption of AI at enterprise scale.
