Code security and change assurance for AI-assisted development.
Connect human intent, agent activity, policy decisions and exact code changes into a verifiable record for engineering, security and audit. Find and fix code risks with contextual scanning and PR reviews.
New here? See a sample finding →Secuarden turns an agent-assisted change into a finding your team can act on: what changed, why it matters, and what evidence is still missing.
Review code security with Code Intelligence →The agent changed three files and weakened an authentication boundary. Review is required before merge; the change record links the prompt, model response, reviewer, and final diff.
Start with code security or AI governance. Secuarden brings both into the software delivery conversation, giving developers actionable findings and reviewers a clearer record of how changes were controlled.
Contextual security analysis for repositories and pull requests. Help developers understand code risks and review suggested fixes where they already work.
Understand how AI-assisted changes were produced and governed. Bring agent sessions, human intent and policy outcomes into an evidence record your team can inspect.
Code Intelligence answers the first question. Change Assurance helps answer the second. Together, the product direction is to connect findings, fixes and review evidence to the change that produced them. The available record depends on the integrations and evidence captured in each workflow.
Supported agent activity bound to people, repositories and code changes.
A 0–100 measure of observed evidence completeness and control coverage.
Deterministic permission boundaries, approvals and policy gates.
Capture supported coding-agent activity, verify append-only evidence offline and export an AI Bill of Materials. Policy-gate enforcement joined the platform in August 2026.
Explore Secuarden CLI$ secuarden verify ✓ Audit trail integrity verified $ secuarden bom --since 30d ✓ AI-BOM exported $ secuarden gate --since 24h --fail-on high ✓ Policy gate passed
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 →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.
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.
Use Code Intelligence to find and address code risk. Use Change Assurance to trace AI activity and review the evidence behind a change. Start with either product line.
Bring repository context into security reviews, from the first scan to a suggested fix.
Install the GitHub app, complete onboarding and enable the repositories you want to analyse.
Combine static signals and AI reasoning with repository context to surface code risks and contextual PR feedback.
Review prioritised findings and suggested fixes. Validate changes with tests and the appropriate human review before merging.
Connect developer intent, supported agent activity, policy decisions and code changes into an inspectable record.
Authenticate the human, agent and session. Apply permission boundaries, deterministic policy gates and required approvals before a change is accepted.
Link agent activity and developer intent to the exact code change, detect control signals and calculate CCR™ evidence confidence.
Preserve tamper-evident records across repositories, surface control gaps and produce framework-mapped, audit-ready evidence exports.
A scan helps identify what needs attention. A change record helps explain how the work was governed. Together they support a more informed review; the available connections depend on the integrations and evidence captured in your workflow.
Discuss Change Assurance →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.
Paste any public GitHub repo. We analyse commit patterns, PR metadata, and AI attribution signals — no login required.
This checks AI activity and review signals, rather than vulnerabilities in source code. For contextual code security analysis, install Secuarden Code Intelligence.
Tell us about your engineering environment, the coding agents you use, and the controls you need.
Start the company pilot form