This guide explains that identifying vulnerabilities is no longer the primary challenge in AI-driven systems—effective remediation is. It highlights that AI-related failures are complex, spanning prompts, data, tools, and permissions, and require coordination across teams. Traditional one-time fixes are insufficient because AI systems evolve continuously, causing vulnerabilities to reappear. The guide emphasizes the need for contextual risk understanding, root-cause fixes, and strategies that prevent repeat failures. It advocates closing the gap between detection and prevention through durable remediation practices. The key takeaway is that organizations must shift from reporting risks to actively reducing them, ensuring long-term resilience in AI-native environments.

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