How Securie keeps codebase review repeatable

Security review is only useful if the result is repeatable. A generic model response is not enough. The product has to route work to specialists, replay suspected exploits where possible, keep evidence, and keep every review inside clear plan limits.

That is why Securie treats AI as a managed subsystem inside the review workflow, not as the product itself.

Specialist routing first

Every run starts with the repo, diff, deploy context, policy, and available runtime evidence. Securie chooses the right specialists for each change: broken access control, secrets, dependency risk, Supabase RLS, auth replay, prompt-injection surfaces, cost exposure, and other checks as the app requires.

Customers do not choose between scanners. They choose how deep Securie should work: review, prove, patch, verify, gate, and attest.

Proof before noise

Securie does not want to hand teams a long list of "maybe" findings. When a finding can be reproduced, the review boots the relevant environment, runs the exploit path, records the result, applies or suggests the fix, and checks the patched path again.

That proof loop is what makes a finding useful to an engineer, a founder, a release gate, and an auditor. The evidence is part of the work product, not an afterthought.

Managed routing and clear limits

Self-serve plans include managed routing, safety controls, sandbox replay, evidence signing, and usage caps. The customer-facing commitment is simple: reviews are repeatable, proof runs are bounded by plan, and upgrades are explicit before deeper work begins.

Enterprise customers can add private deployment controls when procurement, residency, or security policy requires them. The evidence chain stays consistent across those deployment modes.

More

If you want to dig deeper, our AI Bill of Materials explains the public governance posture for model use, retention, evaluation, and human oversight.