AI writes code faster than anyone can review it. Review it anyway.
Plausible-looking validation that does not validate, permissions checked in the wrong place, insecure defaults copied from documentation. Automated, contextual review is the only thing that keeps up.
AI-assisted coding is a real productivity gain, and it introduces subtle security flaws at scale: plausible-looking validation that does not validate, permissions checked in the wrong place, secrets handled carelessly, patterns copied from insecure examples. The volume makes manual review impossible. Automated, contextual review is the only thing that keeps up.
- Scan on every syncConnect the repository once. Re-check changed files as branches move so new code is reviewed at the pace it is written.
- Context, not pattern matchingAI-generated code often looks correct. The analysis follows the actual call paths and data flow to decide whether a check is real or decorative.
- Human verdict on every lineFindings do not become work until an analyst confirms them. Retracted lines stay recorded so the same false alarm is never raised twice.
- Dedup across scansThe same issue reported by two scans or in two places is one finding, so the backlog reflects reality.
- Guardrails for teams shipping fastDecision makers see what the AI-accelerated team is producing and whether it is getting safer over time.
What continuous review looks like
Typical findings in AI-assisted code
- CriticalAuthorization checked on the list endpoint but not on the detail endpoint the assistant generated next
- HighInput "sanitized" with a function that only trims whitespace, then passed to a query or shell
- HighSecrets read from a config example and shipped in the repository
- MediumInsecure defaults copied from documentation: permissive CORS, disabled TLS verification, weak randomness
- MediumDuplicated logic where one copy was fixed and the other was not
Everything needed to run code security as a process, not a project
Fully integrated with GitHub
Connect an account, browse organizations and repositories, import in bulk and start scanning in minutes. Sync keeps the working copy current with the branch you choose.
Scan levels with estimates
From a quick inventory to a full advanced review. Each level shows its estimated scope and cost before you commit, so depth is a business decision, not a surprise.
Expert analysis console
Analysts work in a Claude-powered console that reads files, searches code and finds definitions across the repository. Notes and evidence save directly to the finding.
Verdicts and re-checks
Every reported line gets a human verdict. Fixed locations are re-checked against current code, and retracted ones stay on record so noise never comes back.
Reports and audit trail
Generate PDF findings reports, email them to stakeholders, and rely on a complete history of who changed what on every finding.
Multi-tenant with fine-grained permissions
Separate companies, projects and users. Presets from read-only to company admin, plus per-action permissions, so partners and clients each see only their own work.
Quick inventory
Files, languages, structure. No AI. Seconds.
Targeted
Deep analysis of the focus areas you define.
Medium
Broad coverage of code files with contextual analysis.
Full
Every code file, cross-file dedup, prioritized findings.
Advanced
Maximum depth, sharded across parallel workers for large codebases.
More answers on the FAQ page.
How is it different from the static analysis tool we already run in CI?
Does our source code leave our environment?
How large a codebase can it handle?
Shipping fast with AI? Make sure it is also shipping safe.
Connect one repository and we will show you continuous review on the code your team is producing right now.
Or call 1-877-342-8592