Snowflake’s AI Security Framework details threats and mitigations across AI systems, covering training data leakage, privacy breaches, bias, lack of explainability, and insider attacks like model backdooring. It addresses prompt injection, adversarial and sponge samples, model stealing, fuzzing, and inversion attacks. The framework also covers DDoS, model and data poisoning, multitenancy risks, and exposure of sensitive inputs. Mitigations include robust access controls, encryption, differential privacy, adversarial training, monitoring, and supply chain security. The guide emphasizes continuous evaluation, transparency, and proactive defense to protect AI integrity and trust.

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