The provenance and lineage layer for your AI SDLC.
When your engineers use Claude Code, Cursor, or Copilot, compliance can't see how code was produced. Secuarden records every session — prompt, decision, and commit — into a defensible audit trail mapped to SOC 2 CC8.1.
You already track what's in your software. A Software BOM lists your dependencies. An AI-BOM inventories the AI systems in your environment.
Neither tells you how your code was actually written.
A Context BOM is a per-session record of what got into your code — the prompt that shaped a change, the human who accepted or rejected it, and the files it touched.
An AI-BOM tells you what's in your environment. A Context BOM tells you what got into your code.
Every AI-assisted session produces one automatically. Together they form a tamper-evident chain of custody — the provenance of each change and its lineage from prompt to production — that maps directly to SOC 2 CC8.1 change-management controls.
Read the Context BOM spec →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.
A Big Four auditor reviewing a SOC 2-audited engineering team that ships AI-generated code now hands you a list that looks like this. It's not hypothetical — this is the question pattern that's converging across audit firms.
| # | Auditor question | Your current Tooling |
|---|---|---|
| 01 | List every AI coding tool used by engineering — vendor name, contract type, and attestation date. | Partial |
| 02 | Show me the data egress policy that governs what your developers paste into AI prompts. | Not today |
| 03 | Pull a sample of 10 production commits from the audit window and identify which were AI-assisted. | Yes |
| 04 | Show the review record for each AI-assisted commit — reviewer identity, approval timestamp, and risk classification. | Yes |
| 05 | Demonstrate that customer data classified as confidential or above did not enter a third-party model during the audit window. | Not today |
We built Secuarden to answer all five.
We don't replace scanners or hard-block deploys. Secuarden observes AI-agent sessions, scores review risk, routes sensitive changes to the right reviewer, and preserves the evidence of how code was authored, reviewed, and approved.
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.
Compliance-grade audit trail of every LLM interaction from prompt to production. The flight recorder for AI-assisted development. Immutable, queryable, audit-ready.
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.
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.
Paste any public GitHub repo. We analyse commit patterns, PR metadata, and AI attribution signals — no login required.
You'll see what your auditor will eventually ask about. Most teams are surprised.
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.