How to implement zero trust for AI | TechTarget
putilov_denis - stock.adobe.com
putilov_denis - stock.adobe.com
AI environments involve complex data pipelines, model-training infrastructure, APIs and third-party components, all of which introduce new security risks.
Modern security techniques-- with and without AI -- recognize that traditional trusted-network approaches are inadequate. AI systems ingest new data, interact with users and integrate with other platforms, creating multiple entry points for attackers. A zero-trust model with continuous verification, strict access controls and ongoing monitoring offers a practical framework for protecting AI systems without slowing innovation.
Read on to learn how to apply zero-trust principles to AI by securing data, models, workflows and people.
AI security risks
AI systems create security challenges that most traditional defenses do not address. Specific threats include the following:
- Data poisoning manipulates the training data to alter the model's behavior.
- Model theft involves attackers extracting proprietary models through APIs or compromised infrastructure.
- Prompt injectionand malicious inputs can include...
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