Prompt retuning is the process of revising instructions, examples, constraints, and tool guidance so a different AI model produces the same task reliably. Even when two models support the same interface, they may interpret wording, context, and priorities differently, so prompts that worked well before can become vague, overbroad, or brittle after a model swap.
In cyber security, prompt retuning matters because AI tools are now used for code review, incident triage, log analysis, and agent-driven workflows. If instructions are not retuned, an assistant may miss suspicious code, call the wrong tool, or expose sensitive context in its output. Defenders treat retuning like a controlled dependency change: they version prompts, replay representative tasks, and compare results across models. Attackers can also exploit poorly tuned prompts by steering an assistant into unsafe actions, making prompt quality part of the security boundary rather than just a usability detail.