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[Highlight: Agentic AI perspective](https://owaspai.org/goto/agenticaithreats/)
[Highlight: Periodic table of threats and controls](https://owaspai.org/goto/periodictable/)
[Highlight: Agentic AI perspective](/goto/agenticaithreats/)
[Highlight: Periodic table of threats and controls](/goto/periodictable/)
[1.2 Data limitation](https://owaspai.org/goto/datalimit/)
- [3. Development-time threats and controls](/docs/3_development_time_threats/)
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- [4. Runtime conventional security threats and controls](/docs/4_runtime_application_security_threats/)
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*[AI security testing](/docs/5_testing/)
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*[AI privacy](/goto/aiprivacy/)
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*[References](/docs/ai_security_references/)
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*[Index](/docs/ai_security_index/)
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## About the AI Exchange
@@ -578,7 +575,7 @@ Discovering potential risks that could impact the organization requires the tech
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Question: Do you use RAG (Retrieval Augmented Generation) ?
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Yes: Then your retrieval repository plays a role in determining the model behaviour. This means:
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- You need to protect against [leaking](/goto/leakaugmentation/) or [manipulation](/goto/manipulateaugmentation/) of your augmentation data (e.g., vector database), which includes preventing that it contains externally obtained poisoned data.
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- You need to protect against [leaking](/goto/augmentationdataleak/) or [manipulation](/goto/augmentationdatamanipulation/) of your augmentation data (e.g., vector database), which includes preventing that it contains externally obtained poisoned data.
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Question: Who runs the model?
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- The supplier: select a trustworthy supplier through [supply chain management](/goto/supplychainmanage/), to make sure the deployed model cannot be manipulated ([runtime model poisoning](/goto/runtimemodelpoison/)) - just the way you would expect any supplier to protect their running application from manipulation.
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