The traditional security scanner ends its job exactly where the painful part begins: it hands you a report. Someone still has to read it, judge which findings are real, work out the fix, and write the code. Agentic review closes that loop — it doesn't just find the problem, it proposes the change.
Watch the full AI Code Review walkthrough
More on the Ayurak AI channel.
Review with the architecture in the room
Aribot doesn't look at a file in isolation. It reviews code against the system it lives in — the components, trust boundaries, and data flows it has already mapped. That context is what lets it answer the question a plain scanner can't: does this finding sit on a real path to something that matters? A weakness on an exposed route to sensitive data is treated very differently from the same pattern on an unreachable internal helper.
Findings ranked by attack path, not by label
Because every finding is tied back to threats and attack paths, the output arrives already prioritised by real risk. Developers see the handful that matter at the top instead of a flat wall of “high.” That single change is what rebuilds the trust that false-positive fatigue destroyed.
It sees the AI-specific flaws too
Modern systems embed models, and that creates a class of weakness most scanners never learned to look for. Aribot scans for AI and ML security patterns mapped to the references the field actually uses — the OWASP Top 10 for LLM Applications and MITRE ATLAS — alongside conventional code issues, so prompt-injection exposure and insecure model use show up in the same review as a SQL-injection risk, ranked the same way.
Conventional + AI-specific flaws, mapped to OWASP LLM and ATLAS.
By position on real attack paths, not by isolated severity label.
Propose the change as a reviewable pull request, not another report.
The agentic step: it ships the fix
This is the part that changes the workflow. For findings it can address, Aribot proposes the remediation as a pull request — a concrete, reviewable code change a human approves or rejects. The default is not auto-merge; it's a draft that respects the existing review gate. The agent does the tedious 80% — understanding the flaw, writing the patch, opening the PR — and leaves the judgement call where it belongs, with an engineer.
The measure of a code-security tool shouldn't be how many findings it produces. It should be how many real issues actually get fixed. Shipping a pull request instead of a PDF moves the needle on the only metric that matters — mean time to remediation, not mean time to report.
Compliance evidence as a by-product
Because findings map to recognised frameworks and the fixes are tracked as code changes, the audit trail writes itself. Each remediation is a reviewable artefact tied to a control reference, so demonstrating that a class of issue is handled becomes a query rather than a quarterly scramble.
For the why — how false positives broke developer trust in the first place — read the companion piece.
SAST fatigue and the trust collapse