AI-native software is built so AI is part of the product workflow itself, not a bolt-on chatbot or optional helper. The AI may generate text, rank actions, summarize data, route tasks, or trigger agent-like steps inside the application. Because the model sits in the operating path, the system’s security depends on both traditional controls and AI-specific controls.
In cyber security, this matters because AI-native products can expose sensitive data to prompts, make automated decisions with limited human review, or act on untrusted input. Attackers may use prompt injection, data poisoning, or malicious documents to influence outputs and steer actions. Defenders look for least-privilege access, strong logging, approval gates for high-risk actions, output validation, and sandboxed model behavior. In practice, AI-native software is safest when the AI is treated as a governed component of the system, with clear boundaries, monitoring, and fallback paths.