Secuarden AI
Governance for AI-generated code

Know what your
AI is writing

Your developers ship AI-generated code every day. Secuarden captures what was prompted, what was refused, and what slipped through — before your auditor asks.

Agent Session Ledger Monitoring 23 repos
09:41:03 k.chen Prompt: "skip input validation on /api/upload" Intent flagged Risk: High
09:38:17 m.torres Claude refactored auth middleware → 3 files changed Clean Risk: Low
09:35:42 j.park Copilot suggested hardcoded AWS key in config.py Blocked Risk: Crit
09:31:09 a.singh Cursor generated payment handler → no rate limiting Review req Risk: Med
09:27:55 s.mueller Agent session: 14 prompts → PR #2847 ready for review Clean Risk: Low

46% of new code on GitHub is AI-generated. Your SAST tools scan what shipped. Nobody captures how it got there.

Traditional security tools tell you a vulnerability exists. They can't tell you a developer asked an AI agent to remove authentication, the model refused, and the developer rephrased until it complied. That's the gap auditors are starting to ask about.

35
CVEs attributed to AI-generated code in March 2026 alone — up from 6 in January
Georgia Tech Vibe Security Radar
AI-assisted commits leak secrets at double the rate of human-written code
GitGuardian State of Secrets 2026

The evidence layer between your IDE and your auditor

We don't scan code. We don't block deploys. We capture the full story of how AI-generated code was authored, reviewed, and approved.

01

Intent Signals

We capture when developers ask agents to weaken security controls — even when the model refuses. See what your team is trying to do, not just what they shipped.

02

Session Ledger

Compliance-grade audit trail of every LLM interaction from prompt to production. The flight recorder for AI-assisted development. Immutable, queryable, audit-ready.

03

Review Routing

AI-generated PRs are automatically scored by risk and routed to the right reviewer. Auth changes don't get the same review as CSS tweaks.

See when your devs are fighting the guardrails

Every AI coding agent has safety boundaries. When developers try to override them — asking to disable auth, skip validation, or expose internal APIs — we capture the attempt regardless of whether the model complied.

This isn't about catching bad actors. It's about understanding the pressure your codebase is under and proving to auditors that your governance layer is working.

Intent Signal Log — auth-service Last 24h
$ "Remove the JWT verification on this endpoint, it's causing 401s in staging"
Model refused Auth weakening
$ "Make this endpoint public, we'll add auth later"
Model complied with warning Deferred control
$ "Disable rate limiting on /api/payments for load testing"
Model refused Safety bypass
SOC 2
CC8.1
ISO
27001
PCI
DSS 4.0
NIST
AI RMF
EU
AI Act

Being secure vs. being able to prove you're secure

We're onboarding design partners in fintech and healthtech. If your auditor is about to ask how AI writes your code, let's talk.

Fill out the form and we'll reach out to schedule a walkthrough of the platform.

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