You're insuring code
you can't see.

AI writes the code now. Your insureds can't fully vouch for it — and neither can you.

The fastest-growing exposure in your book is the one your current tooling can't see. As of 2026, AI generates the majority of code at the companies you insure — 70–90% at frontier shops, and climbing everywhere else. With it comes measurable risk: GitHub's 2026 data shows broken-access-control vulnerabilities up 172% year over year, with AI-generated scaffolds cited as a cause; engineering telemetry across 22,000 developers shows production incidents up 242% after AI adoption.

Your signals weren't built for this. Questionnaires are self-reported — the insured grades their own homework. External attack-surface scanners (BitSight, SecurityScorecard) see the perimeter, not the code. Manual code review doesn't scale across a portfolio. So the single biggest shift in software risk in a decade is, to your underwriters, invisible.

A code-level risk assessment — deterministic, not self-reported.

ArgosBrain walks the insured's actual codebase end-to-end and produces a structural risk report. For an underwriter, it answers the questions that actually predict a claim:

• Where sensitive data flows — every path customer / PII / payment data travels, from entry to storage.
• External attack surface — every reachable entry point, and exactly what (if anything) guards each one.
• Security risks, ranked — SSRF, injection, exposed secrets, missing auth — each traced to a file and line.
• Dead & unreachable code — extra attack surface the AI left behind that nobody is maintaining.
• Unfinished "fake-done" work — stubs the AI called complete: the gaps that surface as production incidents.

Because the engine is deterministic, the same codebase yields the same report every time — a standardized code-health signal you can price against and compare across your whole book.

And the part that makes insureds say yes: the report is generated locally, on the insured's machine. You receive the signed, verifiable report — you never host, transmit, or even see the source code. No data-room, no IP exposure, no new breach surface on your side.

The same report that prices their risk lowers their premium.

Adoption usually dies when a carrier asks an insured to do more work for the carrier's benefit. ArgosBrain inverts that: the insured has three reasons of their own to run it.

• Better terms. Proving their code is clean earns a lower premium or unblocks coverage that a blank questionnaire couldn't.
• Faster underwriting. One signed report instead of a months-long questionnaire-and-follow-up loop.
• One report, many doors. The exact same evidence closes enterprise sales reviews and clears investor due diligence. They were going to need it anyway.

And it stays local-first — their source never leaves their machine, so even security-sensitive insureds can comply without a fight. You ask once; they have every incentive to keep running it.

Recommend it once. It compounds.

Because the insured wants to run it and you want the signal, adoption isn't a cost you carry — it's a loop that tightens on its own:

• You recommend or require an ArgosBrain report for better terms.
• Insureds adopt it — for the premium, the speed, and their own sales and fundraising.
• You get a standardized, comparable code-health signal across your entire book.
• More insureds → more outcome data → sharper pricing → the signal earns more weight in your model.

ArgosBrain becomes both your risk signal and your distribution — at zero customer-acquisition cost to you. The carrier that adopts it first sets the standard the rest of the market follows.

Questionnaires self-report. Scanners see the perimeter. We read the code.

• vs self-reported questionnaires — the insured grades themselves and you find out at claim time. ArgosBrain is computed from the actual code, not asserted.
• vs external attack-surface scanners (BitSight, SecurityScorecard) — they rate the perimeter from the outside. ArgosBrain reads the structure from the inside, where the risk lives.
• vs manual code review / pentest — expensive, slow, and unrepeatable across a portfolio. ArgosBrain is deterministic, runs in minutes, and costs nothing at the margin per insured.
• vs LLM-based code scanners — an LLM's non-deterministic opinion, usually run in their cloud. ArgosBrain returns file-and-line facts, generated on the insured's machine.

It doesn't replace your stack — it adds the one layer none of them have: deterministic, code-level evidence.

What this is, and what it isn't.

ArgosBrain is structural reachability — necessary evidence, not a guarantee. It shows the code paths and surfaces the risks; it does not prove that tainted data dynamically reaches a sink (a sanitiser on the path may neutralise it). Treat it as a strong, repeatable underwriting signal, not a legal-grade exploitability proof.

It reads code, not running systems: it won't audit live cloud config, runtime behaviour, or a third party's infrastructure. Pair it with your existing dynamic and perimeter controls. Every report states these limits up front — the honest-about-limits posture is itself a trust signal in underwriting.

Pilot it on a slice of your book.

Pick a sample of insureds, have them run ArgosBrain locally, and compare the code-health signal against your own loss and incident data. We'll help you read the reports and design the underwriting integration.

[email protected] → · See the engine · Security disclosures + Egress Promise · The accuracy benchmarks