securecod.eu

Detailed workflow

How securecod.eu works, step by step.

This page shows how deterministic security tools, AI-assisted analysis, deployment control, and evidence fit together.

Start with the overview Review onboarding Back to landing
End-to-end flow from repository onboarding through security gateways, knowledge database, deployment control, Kubernetes runtime, and final reports.

Overview

One controlled flow from repository install to runtime facts.

Every stage adds system evidence before the next release decision.

  1. Customer connects a repository through GitHub App installation access.
  2. Security Gateway runs traditional scanners for exposure, secrets, and hygiene.
  3. AI helps analyze security logs, findings, and follow-up actions.
  4. Deployment Gateway enforces policy, approvals, and rollout control.
  5. Kubernetes exposes health, state, and rollback signals.
  6. Operators review evidence and use AI only after facts exist.
Platform overview from customer repository and GitHub App through security gateways, knowledge database, deployment control, Kubernetes runtime, and evidence reports.

Repository onboarding

GitHub App gives a safer repository access model than personal token reuse.

The customer installs the GitHub App, selects repository scope, and can revoke access by removing the installation.

  1. Install the GitHub App in the customer GitHub organization.
  2. Select the repositories that should be visible to securecod.eu.
  3. Use installation-scoped access for runtime jobs and clone actions.
  4. Revoke access by removing the installation when the relationship ends.
Customer repository onboarding through GitHub App, scoped permissions, ephemeral clone, and revoke path.

Security Gateway flow

Traditional security tools run before release decisions.

Security Gateway turns scanner output into explicit findings that operators can triage.

Traditional security tools scan AI-generated code, produce logs and findings, AI analyzes those findings, operators decide, and deployment gate controls release.

Step 1

Exposure scan

Check whether repository visibility or configuration widens risk.

Step 2

Secret checks

Surface risky files, tokens, or configuration patterns before rollout.

Step 3

Hygiene rules

Run deterministic repository checks instead of relying on vague AI confidence.

Step 4

AI-assisted triage

Use AI to explain logs, findings, risk level, and follow-up decisions.

Knowledge Database flow

Use verified project context instead of reconstructing it each time.

The knowledge layer turns repo materials, docs, and prior sessions into a traceable source base.

Input

Index trusted sources

Repository context, docs, and prior sessions become queryable inputs.

Retrieval

Pull context on demand

Operators retrieve evidence-backed project context instead of relying on memory.

Control

Ground AI-assisted work

Verified sources reduce hallucinated assumptions in follow-up tasks.

Deployment Gateway flow

Deployment stays deterministic from trigger to rollout state.

The platform treats deployment as a governed workflow, not an untracked shell side effect.

Deployment gateway controls approved changes, policy checks, Kubernetes runtime, rollout state, and evidence trail.

Start

Controlled trigger

Deployment begins through an explicit gateway path.

Policy

Checks and approvals

Required rules and operator approvals can block rollout.

Runtime

Rollout tracking

Health, state, and rollback signals stay visible during execution.

Evidence

Linked outputs

Logs, findings, and deployment status remain attached to the same path.

Why this matters

Release control is only useful if runtime state stays visible after the trigger. securecod.eu keeps policy, rollout state, and evidence in one governed path.

  • No hidden shell-based deploy step
  • Approvals stay visible to operators
  • Rollout facts remain reviewable after execution

AI Playground and operator control

Operators review, AI assists.

AI explains scanner logs, findings, and recommendations after deterministic evidence exists.

Human operators

Own the decision path

Operators review findings, rollout state, and approvals before acting on the result.

AI assistance

Works after evidence exists

AI helps interpret findings and next actions, but it does not replace security tools.

Artifacts and audit

Every important outcome should leave evidence.

The platform should expose not only whether something ran, but what findings, approvals, and deployment facts resulted from it.

Evidence report combines security findings, AI analysis summary, deployment status, commit evidence, and operator notes.

Logs and findings

Security results and runtime output stay tied to the workflow path.

effective_commit_sha

Operators can see what commit actually drove the result.

Approval state

Decision points remain visible after the workflow ends.

Deployment status

Rollout outcome and follow-up remain reviewable.

Runtime model

Predictable Kubernetes keeps failure modes explainable.

Whether the runtime is a compact single-node footprint or a lightweight multi-node setup, the goal stays the same: deterministic rollout, health checks, and explicit rollback behavior.

Option

Single-node

Useful for simpler, compact environments where operational surface must stay small.

Option

Lightweight multi-node

Useful when stronger runtime separation and more resilient rollout behavior are needed.