Modern AI systems introduce new security risks such as manipulation, model leakage, and exploitation, turning prompt engineering into a form of social engineering. As AI becomes a new attack surface, external integrations further increase exposure by expanding potential entry points. Effective security requires capabilities like asset discovery and mapping, prompt injection testing, and guardrail bypass detection. Additional protections include identifying integration exploits, preventing data extraction and poisoning, ensuring proper output handling, and conducting compliance checks. By simulating chain attack scenarios and evaluating third-party risks in production-safe environments, organizations can better mitigate threats and safeguard AI systems.

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