Classification
AI Security and Risk Management
Overview
Threat modeling is a systematic process used to identify, enumerate, and prioritize potential threats, attack vectors, and vulnerabilities within a system, including AI systems and applications. It helps organizations anticipate how adversaries might compromise confidentiality, integrity, or availability. Techniques such as STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) and MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) provide structured approaches for mapping threats specific to AI. While threat modeling enhances preparedness and risk mitigation, it is not foolproof; its effectiveness depends on the accuracy of threat identification, the completeness of system understanding, and the evolving nature of attack techniques. Limitations include potential blind spots, resource constraints, and the challenge of keeping models updated as systems and threats evolve.
Governance Context
In AI governance, threat modeling is recommended or required by several frameworks. For example, the NIST AI Risk Management Framework (AI RMF) emphasizes proactive identification and mitigation of risks, including adversarial threats, as a control for trustworthy AI. The EU AI Act requires high-risk AI system providers to conduct risk assessments, which often include systematic threat modeling. Concrete obligations include: (1) conducting periodic risk assessments to identify and address new or evolving threats (NIST AI RMF, Section 3.3), and (2) documenting threat mitigation strategies and residual risks (EU AI Act, Article 9). Organizations are also expected to maintain up-to-date records of identified threats and controls, and to integrate threat modeling results into their overall risk management lifecycle.
Ethical & Societal Implications
Effective threat modeling in AI systems helps protect users from harm, prevent misuse, and maintain public trust. However, inadequate or superficial threat modeling can lead to overlooked vulnerabilities, disproportionately affecting vulnerable populations or critical sectors. There is also a risk that overemphasis on technical threats could neglect broader societal risks, such as systemic bias or misuse by insiders. Balancing thoroughness with practicality is essential to avoid both under- and over-securitization. Additionally, transparency in threat modeling practices can foster accountability and stakeholder trust.
Key Takeaways
Threat modeling systematically identifies and prioritizes security risks in AI systems.; Frameworks like STRIDE and MITRE ATLAS offer structured methodologies for AI-specific threats.; Governance frameworks mandate or recommend threat modeling as part of risk management.; Concrete obligations include periodic risk assessments and documentation of mitigation strategies.; Limitations include incomplete threat identification and challenges with evolving attack vectors.; Ongoing updates and cross-disciplinary collaboration improve threat modeling effectiveness.; Failure to conduct thorough threat modeling can result in overlooked vulnerabilities and real-world harm.