Why firewall review suits AI
Firewall review is fundamentally an interpretation problem. An expert reads policies, traces objects, correlates zones, and reasons about how rules combine to create effective access. It is tedious, pattern-heavy work that depends on understanding context — and that is precisely where modern AI is strong.
Unlike simple scanners that match keywords, an AI approach can reason about intent: recognizing that a rule is overly permissive given its context, or that two rules combine to expose something neither does alone.
What AI does well here
- Reading and interpreting large, complex configurations quickly
- Correlating rules, objects and zones into effective access
- Recognizing risky patterns and explaining them in plain language
- Generating specific remediation rather than generic advice
- Applying a consistent standard across every review
Where structure matters
AI is most reliable for firewall review when it is grounded in firewall semantics rather than asked to free-associate. A general chatbot pasted a config may give plausible but inconsistent answers; a purpose-built system that models the configuration and constrains the AI to firewall-specific reasoning produces accurate, repeatable results.
This is the difference between 'an LLM looked at my config' and a tool engineered for the task. FirewallScan takes the latter approach: it reconstructs the effective FortiGate policy and applies AI analysis within that structured understanding.
The role of human experts
AI does not eliminate the need for skilled engineers — it changes what they spend time on. By automating the mechanical reading and first-pass analysis, AI frees experts to focus on architecture, judgment calls and the genuinely hard decisions.
The most effective model is AI-assisted: the AI does the comprehensive, consistent review; the human applies context and makes the final calls.