Prioritised findings
Only human-approved findings appear as report findings.
Source code review
LAN10 AppSec Review analyses your application's source code at an exact commit using two separate AI security reviewers. Every candidate finding is assessed by a human reviewer before it reaches the final report.
Two separate AI reviews. Human-reviewed findings.
The review process
One run moves through four defined stages: the code is pinned to an exact commit, two AI security reviewers analyse it separately, overlapping findings are consolidated deterministically, and a human reviewer assesses every candidate before the report is approved.
Source code review walkthrough
57sReport preview
A synthetic example of how a confirmed finding appears in the final report.
Security review report
Evidence
The export handler verifies authentication but does not check that the requested account belongs to the authenticated tenant.
Remediation
Enforce tenant-scoped authorisation before generating or returning the export.
The deliverable
The final report turns candidate findings in your application code into a reviewed, prioritised set of actions tied to the exact commit assessed. Confirmed findings retain their source locations, supporting evidence and remediation guidance, with coverage and limitations recorded so your team can judge what the assessment establishes.
Only human-approved findings appear as report findings.
Each finding identifies the relevant code location and supporting evidence.
The human reviewer records the final severity and the reason for the decision.
Each confirmed finding includes practical guidance for engineering teams.
The report records which reviewer reported the issue and retains the underlying reviewer material.
The report states the commit reviewed, the coverage achieved and any limitations.
When to use it
Use LAN10 AppSec Review when a security-sensitive release, customer review or internal assurance decision needs an outside view of the application code.
Review security-sensitive code before it reaches customers or enters a critical deployment stage.
Give engineering and security leaders an outside assessment of a defined codebase.
Provide a traceable report tied to the exact version of code that was reviewed.
Accountability and traceability
The report identifies the precise commit reviewed.
Neither AI reviewer receives the other reviewer's analysis.
Overlapping findings are consolidated without discarding the original reviewer records.
Partial or limited coverage is reported as such.
Ephemeral source handling. Source is cloned into a run container created for the review and removed when the run ends.
Read the confidentiality approachTell us what you need reviewed and why. We will recommend an appropriate scope and provide a quote for the engagement.
Request a scoped reviewHuman-reviewed findings on every engagement