Definition ∞ AI threat modeling systematically identifies potential security vulnerabilities and attack vectors targeting artificial intelligence systems. This process assesses risks associated with AI components, data pipelines, and deployment environments. It considers various adversarial techniques, including data poisoning, model evasion, and extraction attacks, aiming to predict how an AI system might be compromised. The practice provides a structured approach to understanding and mitigating security risks specific to AI applications.
Context ∞ The state of AI threat modeling in digital assets is evolving rapidly as AI integration increases in trading algorithms, risk management, and smart contract auditing. A key discussion point concerns the unique challenges of securing AI models that interact with high-value, immutable blockchain transactions. Future developments will likely focus on standardized threat modeling frameworks for decentralized AI applications and the creation of AI-specific security audits to address emerging vulnerabilities.
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