Post-training alignment is the stage after a model’s base pretraining, where developers tune it with human feedback, preference data, safety rules, and policy constraints. The goal is not to make the model “neutral,” but to make its behavior more useful, safer, and easier to govern. Alignment can change how a system answers questions, refuses risky requests, follows style rules, or handles sensitive topics.
In cyber security, this layer matters because it directly shapes what an AI assistant will say about phishing, malware, identity verification, or internal procedures. Strong alignment can reduce harmful advice, limit data leakage, and enforce enterprise policies. Weak or inconsistent alignment can be abused through prompt injection, jailbreaks, or manipulative prompts that try to bypass safeguards. Defenders test aligned models for refusal quality, policy compliance, and resilience to adversarial prompts, while attackers look for gaps between the model’s training and its runtime controls.