Secuarden AI

Control what AI can change. Prove what it did.

The assurance layer for AI-assisted software development.

Secuarden connects human intent, coding-agent activity, policy decisions and exact code changes into one verifiable chain. Govern agent actions before changes are accepted, then produce evidence your security and audit teams can trust.

Agent Session Ledger Provenance + evidence live
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 Gate preview Control: Fail
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
Available nowAgent Sessions Ledger

Supported agent activity bound to people, repositories and code changes.

Available nowCCR™ assurance scoring

A 0–100 measure of observed evidence completeness and control coverage.

Launching August 2026Policy Enforcement Engine

Deterministic permission boundaries, approvals and policy gates.

Verifiable governance for AI-written code

Capture supported coding-agent activity, verify append-only evidence offline and export an AI Bill of Materials. Policy-gate enforcement joins the platform in August 2026.

Explore Secuarden CLI
Local governance · no cloud required
$ secuarden verify
✓ Audit trail integrity verified

$ secuarden bom --since 30d
✓ AI-BOM exported

$ secuarden gate --since 24h --fail-on high
✓ Policy gate passed

Meet the Context BOM

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 shaped a change — the human instruction, agent activity, review decision and files touched.

An AI-BOM tells you what's in your environment. A Context BOM tells you what got into your code.

For supported coding-agent workflows, Secuarden produces one automatically. Together they form a tamper-evident chain of custody that can be mapped to change-management and AI-governance controls.

Read the Context BOM spec →

AI agents can change production code. Enterprises can’t control—or prove—how.

Coding agents operate across developer machines, repositories and delivery pipelines, but their activity is disconnected from enterprise controls and the code that ultimately ships. The resulting evidence remains fragmented across agent, repository, scanner and GRC systems.

01
Policy is not enforcement. Prompts and rules files guide agent behaviour, but do not provide consistent, independently verifiable enforcement.
Control gap
02
Provenance breaks. Commit history records the resulting code—not the human intent, agent identity and actions that produced it.
Evidence gap

What does missing AI evidence cost your team?

Estimate the audit reconstruction, undifferentiated review, investigation, and compliance effort Secuarden can help your organization recover.

Calculate your ROI
A transparent estimate
5 inputs
Turn your engineering profile into an editable, shareable business case.
Includes4 cost areas
ExcludesFines & breach claims

Five questions security and audit teams need to answer. Most toolchains leave gaps.

AI-assisted development expands the evidence required to demonstrate identity, authorization, review, data handling and change control.

# 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

Secuarden supplies the technical provenance and control evidence needed to answer these questions.

Control, verify and prove every AI-assisted change

Secuarden is the missing assurance layer across the agentic SDLC—connecting developer intent, agent action, policy outcomes and the exact code shipped.

01
Launching August 2026

Control

Authenticate the human, agent and session. Apply permission boundaries, deterministic policy gates and required approvals before a change is accepted.

02
Available now

Verify

Link agent activity and developer intent to the exact code change, detect control signals and calculate CCR™ evidence confidence.

03
Available now

Prove

Preserve tamper-evident records across repositories, surface control gaps and produce framework-mapped, audit-ready evidence exports.

Preserve the intent behind every supported change

Append-only session records connect human instructions, agent and tool activity, refusals and resulting changes to the relevant repository workflow.

Configurable redaction protects sensitive content while preserving the evidence required for review and audit.

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

Find out what your
AI agents committed
last week

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.

What the scan surfaces
  • Estimated volume of AI-assisted commits in the last 30 days
  • Sensitive paths touched by AI agents (auth, payments, config)
  • PRs with AI attribution and no human review signal
  • Your CCR™ score preview — a measure of observed evidence completeness and control coverage
Agent Activity Scanner — Public Repos
3 free scans · no account needed · results in ~20s
github.com/
Public repos only · e.g. vercel/next.js
Scan results
CCR™ Score
Context Confidence Rating
74
or connect directly
Read-only access · no code stored · results cached 24hrs

Control every change. Verify every result. Prove what shipped.

We're selecting regulated fintech and healthtech design partners for a focused 60–90 day pilot across 3–5 repositories.

See the live provenance and evidence platform now, then help shape deterministic policy enforcement ahead of its August 2026 launch.

Controlled rollout · Configurable redaction · Design-partner access
Request an enterprise pilot

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