AI alignment is the effort to make a model’s behavior match human intent, security rules, and operational limits. A well-aligned system should refuse unsafe requests, follow policy, and stay consistent when the situation changes. In practice, alignment combines training, evaluation, and guardrails so the model does not optimize the wrong objective or invent shortcuts that violate instructions.
In cyber security, alignment matters because AI tools are often used to draft code, review alerts, summarize logs, or assist incident response. If the model is poorly aligned, it may leak sensitive data, ignore access controls, or be manipulated through prompt injection into taking harmful actions. Defenders use alignment-related controls such as policy tuning, red-teaming, output filtering, and human review to keep AI assistance within safe boundaries. Good alignment does not replace security testing, but it reduces the chance that a helpful tool becomes an unsafe one.