Guardrails around a live AI system
Check Point AI Guardrails, built from Lakera's technology, is aimed at teams running AI applications rather than ordinary end users chatting with an assistant. It can inspect inputs and outputs for prompt attacks, data leakage, malicious links and policy violations.
Its newer agent protections also look at tool use - including whether an agent is calling an allowed tool and whether the action fits the user's intent.
Securedby.ai view
This is a useful example of defence in depth. Guardrails can reduce risk around a live AI application, but they do not replace good permissions, secure system design or careful handling of secrets.
If prompt attacks are new to you, read Prompt injection: when AI reads instructions you never gave it first.
Five checks worth making
- Map where the guardrail sits in the request path. Know exactly which inputs, outputs and tool events are screened.
- Choose policies for the application. A customer chatbot, coding agent and internal assistant should not all use identical rules.
- Test false positives and misses. Blocking everything noisy is not the same as protecting the system well.
- Protect the guardrail configuration itself. Policy changes can materially alter the application's security boundary.
- Keep traditional controls. Secrets management, identity, authorisation and least privilege still matter even when prompt defences are excellent.
Useful resources
Good places to learn more, check current product details and go deeper. We favour first-party documentation, then add genuinely useful independent references where they earn their place.
