What is AI security?

This post details common vulnerabilities in AI systems such as data poisoning, lack of transparency, and prompt injection attacks (direct, indirect, and chained). It also outlines mitigation strategies during AI deployment, including securing the AI lifecycle (data integrity, monitoring for drift) and input validation/prompt handling. The adoption of a zero-trust model with continuous verification, granular access controls, and endpoint/network security is emphasized. Best practices and frameworks like NIST AI Risk Management Framework, OWASP Top 10 for LLM Applications, MITRE ATLAS, and ISO/IEC standards for AI security are highlighted.