This guide provides best practices for securing AI-assisted development and applications. The visual grid covers topics like prompt injection defense, restricting LLM data access, validating AI-generated code, and maintaining human oversight. It emphasizes treating AI outputs as untrusted, mitigating hallucinations, and securing the AI supply chain. It also highlights avoiding sensitive data exposure to public AI tools and using hybrid AI approaches where appropriate. The guide references OWASP Top 10 for LLMs as a framework. The key takeaway is that AI introduces new security risks that require strict validation, access control, and governance across both development and deployment.

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