THE diary post argues that the cybersecurity industry may be approaching AI security the wrong way by trying to cage powerful general‑purpose agents rather than designing smaller, focused tools. The author notes that modern AI agents can code, run shells, browse the web, call APIs and access enterprise data, yet many organisations apply containment measures (containers, guardrails, monitoring, human in the loop) in an attempt to keep them under control.
The central claim is that if the base models can scale their capabilities, restricting them after deployment is less effective than creating narrowly scoped, single‑purpose agents that only perform specific tasks.
A concrete example is used from Check Point’s October article, which describes a Dangerous Deviation Detection capability that sits between the model’s proposed tool call and the application executing it. The detector evaluates whether a tool call is dangerous and unwarranted, considering data leakage, privilege escalation and destruction of resources. It looks for justification in user and system messages, with tool responses not counted as authorization.
The post reiterates the importance of separating duties and enforcing the principle of least access: an investigation agent should gather evidence with read‑only access, and any containment actions (like disabling accounts) should be performed by a separate, authorised workflow. The author suggests adopting this boundary in code and credentials and advocates testing for edge cases—e.g., whether an agent will attempt to close tickets or expand actions beyond the immediate task.
The piece references Check Point’s technical documentation and Check Point’s own article as sources, and connects the discussion to SANS SEC573, which teaches building smaller, safer agents. The overall recommendation is to prioritise architectural boundaries and narrowly scoped agents over “perfect cages” for powerful AI platforms. Publication date: 16 October 2026.