Your team already writes code with AI — but nobody checks whether it is safe
For organizations whose developers now rely on AI to write code, and who need to know whether what shipped contains vulnerabilities or leaked secrets — before an auditor or an attacker finds them first.
Not broken code — code that looks fine while being unsafe
Secrets sitting in the code, unnoticed
A connection string, password, or API key added during testing and never removed — usually already in git history, which is far harder to erase than people expect.
Dependencies with published vulnerabilities
AI tends to pick library versions it saw often in training, which may be older releases with published CVEs — and nothing checks this at merge time.
Authorization that looks present but is incomplete
Permission checks on the main endpoint but not the secondary ones, or a check that the caller is logged in without checking the record actually belongs to them.
Code nobody on the team truly understands
When code is produced faster than anyone reads it, parts emerge that nobody dares change — and that is exactly what breaks slowly during an incident.
Audit, then leave guardrails the team can run themselves
The goal is not for us to review forever — it is for your own pipeline to catch these before a merge.
Audit the single riskiest repository first
We pick the repository closest to customer data or money and review both the current code and its git history.
Rank by real risk, not by warning count
Automated tools emit hundreds of warnings until teams stop reading them. We surface only what genuinely matters and explain how each could actually be exploited.
Put guardrails into the pipeline
Secret scanning, vulnerable-dependency scanning, and a threshold that fails the merge when something high-severity appears.
Write the team's rules for using AI to write code
Spelling out what must never be pasted into an AI tool — real customer data, keys — and which changes always require human review.
A report that names files and lines, not vague advice
Findings down to file and line
Each item names the file, the line, a severity, the scenario in which it could be exploited, and a recommended fix with a rough effort estimate.
Automated checks that keep running
Configured so every merge is checked for leaked secrets and vulnerable dependencies without relying on manual review.
An AI coding policy people will actually follow
A policy draft covering restricted uses and human-review points, with length and detail determined by the team's scope and process.
If your development team matches these
- Developers already use AI coding tools routinely
- You run systems touching customer data, employee data, or financial transactions
- No secret or vulnerable-dependency scanning in the pipeline yet
- An enterprise customer or external auditor is about to review you
We will say plainly if you do not need this yet
- You already have an internal security team covering this
- Nothing is in production yet and no real data is involved
- You only want a certificate, not actual remediation
What CTOs and team leads always ask first
Do we have to give you our whole codebase?
No. Assessment can start from a selected repository, while NDA terms, review location, and access must be confirmed before work begins. If code cannot leave the organization, the team will assess whether an on-environment review is feasible.
How is this different from just running a scanner?
A scanner tells you what might be a problem, not which ones are actually exploitable in your context. We filter to what genuinely matters, explain the attack path, and leave guardrails your team can maintain.
Will this report be used against our developers?
The report is written at system level and does not attribute code to individuals. The goal is reducing risk and improving process, not performance review. It goes only to the recipients you nominate.
Do we have to keep paying after the audit?
No. The report and the guardrails are yours to run. Ongoing monitoring and periodic re-checks are a separate monthly service you can consider later, if you want it.
Let us audit one repository and see what turns up
The team assesses repository scope and confirms the review method, timing, fees, and report format before work begins.
