What an AI security audit checks
- Exposure: open ports, services reachable from the internet, and risky bindings like a database listening on a public address.
- Access: user accounts, SSH keys and configuration, sudo rights, and password policy.
- Software: installed packages and known vulnerabilities (CVEs) against them.
- Hardening: firewall state, service configuration, and common misconfigurations.
- Integrity: unexpected changes to sensitive files.
Why use AI rather than a plain scanner
A checklist scanner flags every deviation from a template, which produces long lists that are mostly noise on a real server. The hard part is judgment: which findings actually matter here, and what is the safest fix.
SecAI runs a three-role AI pipeline for exactly that. A Specialist reads the evidence the agent collected and drafts findings, a Validator re-checks each one against the same evidence and dismisses what it does not support, and a QA role merges duplicates and writes the final report. The output is a scored posture and a short, ranked list of findings, each showing what it was based on. Where the fix is one SecAI can make (blocking an address, installing a package update or applying a hardening fix), the finding offers it as an action that waits for your approval; otherwise it tells you how to do it yourself.
AI audit versus a manual audit
A manual audit by a security engineer is thorough and context-aware, and it is also slow, expensive, and a snapshot in time. Most small teams get one rarely, if ever.
An AI audit is fast and repeatable enough to run on every server on a schedule: SecAI runs one every week on each monitored server, and again whenever you ask, instead of once a year. It does not replace a human for a deep, bespoke review, and a finding can be wrong, which is why every finding shows the evidence it was based on. A recommendation is a lead to check, not a verdict.