Software Observability for application security

We stop bad code.

Where it's written, where it's built, and before it reaches production. Crash Override gives security teams independent, deterministic facts about what was built, what's inside it and what's running.

Book a demo How it works

payments-api

$ git push origin main

# build 4821 started

record signed by the build sensor

commit 8f3c2a1

policy no high or critical CVEs

found lodash CVE-2021-23337

✗ held build 4821 does not ship

fix upgrade lodash to 4.17.21

# reason and evidence on the record

AI is working for both sides.

Your teams ship ten times the code. Attackers use AI to find complex flaws in minutes. When anyone can find the flaw, the only defense is not shipping it.

Lines of code
Mythos-class exploits
Hockey-stick risk

Production hygiene

Prevent. Can this ship?

Every build is checked against your policy before it ships. Bad code is held, with the reason and a fix.
Exposure response

Respond. Are we exposed?

When a malicious package hits the news, see every service running it, or that ever ran it, in minutes.
Continuous evidence

Prove. Can you prove it?

Evidence for the board, auditors and regulators from what was actually built and run. Current every time you ask.

Facts, not guesses.

A new wave of security tools points a model at your code and logs and asks it what happened. A model can only infer, and two runs can give you two answers. We don't use AI to secure AI. We record what happens, as it happens, and sign it.

From repository to runtime

Developer workspace

Written

In the developer workspace, the workspace sensor records how code was written, before anyone switches context.
Build

Built

At build time, the build sensor adds a signed record to every artifact: what it is, where it came from and what's inside it.
Production

Running

In production, that record travels with the artifact and reports where it runs. Pick any service and see what's inside it, which commit it came from and when it shipped.

Data plane

One data plane connects all three, so every answer traces back to its source.

The alternative, spelled out

	AI-based security tools	Crash Override
How they know	A model reads scanner output and logs, then infers	Sensors record it where it happens
Ask the same question twice	Can get two different answers	Same answer, every time
"Can you prove it?"	"The model said so"	A signed record, traced to the commit
Who decides what ships	The model	Your policy, approved by your security team. No model makes the decision

Less work, not more

At Blackstone, 98% of production deploys ship automatically. The AppSec team reviews only the 1 to 2% that need a human, and those arrive with the evidence attached.