AI Threat Modeling is the systematic practice of analyzing AI systems to identify potential attack vectors, vulnerabilities, and threats before they can be exploited. It extends traditional threat modeling methodologies like STRIDE and PASTA to address AI-specific risks such as adversarial attacks, data poisoning, model theft, and prompt injection.
The AI threat modeling process typically involves: defining the AI system scope (models, data pipelines, APIs, integrations), identifying assets (training data, model weights, user data, intellectual property), mapping data flows (how information moves through the AI system), identifying threats using AI-specific taxonomies (OWASP Top 10 for LLMs, MITRE ATLAS), assessing likelihood and impact of each threat, and prioritizing mitigation strategies.
AI-specific threat categories include: adversarial input attacks (crafted inputs that cause misclassification), model extraction (stealing model functionality through API queries), training data poisoning (corrupting data to manipulate model behavior), membership inference (determining if specific data was used in training), prompt injection and jailbreaking, supply chain compromise of model dependencies, and privacy attacks that extract sensitive training data.
Organizations should conduct AI threat modeling before deploying any AI system, during major updates, and periodically as the threat landscape evolves. The results should feed into AI risk management processes and inform security control implementation.
