Engineering-grade lab for designing and building bounded proofs, prototypes, deployment patterns, and reusable accelerators across source code management, CI/CD, security, compliance, and AI-enabled workflows.
This repository is a practical engineering lab for producing bounded, reviewable, reusable artifacts across modern software delivery systems.
The lab is designed to support:
- Source code management patterns
- CI/CD automation prototypes
- Security engineering controls
- Compliance evidence generation
- Cloud and platform deployment patterns
- AI-enabled engineering workflows
- Reusable accelerators for delivery, governance, and operations
This repository is not a place for abstract demos. Every artifact must be bounded, testable, documented, and suitable for engineering review.
Every proof, prototype, deployment pattern, or accelerator must define its operating boundary.
A bounded artifact clearly states:
- What it does
- What it does not do
- Required inputs
- Expected outputs
- Required permissions
- Security assumptions
- Compliance assumptions
- Validation method
- Known limitations
- Reuse model
- Design safe prototypes before production implementation.
- Validate source-control, CI/CD, security, compliance, and AI workflow patterns.
- Package reusable accelerators for repeatable engineering use.
- Capture reviewable evidence for controls, decisions, and validation.
- Make engineering assumptions explicit.
- Prevent prototypes from being mistaken for production systems.
The central lab registry is maintained at:
registry/lab-registry.md
The registry tracks each lab's category, status, validation state, evidence artifacts, and accelerator path.
| Category | Purpose |
|---|---|
| Source Control | Repository structure, pull request governance, CODEOWNERS, branch strategy, release traceability. |
| CI/CD | Build, test, scan, package, deploy, rollback, and promotion workflow prototypes. |
| Security | DevSecOps controls, secret scanning, dependency checks, SAST, SBOM, token scoping, and secure defaults. |
| Compliance | Evidence models, control traceability, approval records, audit trail patterns, and policy exception tracking. |
| Deployment Patterns | Reference deployment patterns, promotion gates, rollback models, environment boundaries, and drift checks. |
| AI Workflows | Human-reviewed AI workflows for engineering review, summarization, evidence preparation, and runbook support. |
bounded-engineering-lab/
├── README.md
├── LICENSE
├── CONTRIBUTING.md
├── SECURITY.md
├── CODEOWNERS
├── .github/
│ ├── workflows/
│ │ ├── ci.yml
│ │ ├── security-scan.yml
│ │ └── compliance-evidence.yml
│ ├── ISSUE_TEMPLATE/
│ │ ├── lab-proposal.md
│ │ ├── prototype-request.md
│ │ └── security-review.md
│ └── PULL_REQUEST_TEMPLATE.md
├── docs/
│ ├── lab-charter.md
│ ├── engineering-standards.md
│ ├── control-boundaries.md
│ ├── validation-model.md
│ └── evidence-model.md
├── labs/
│ ├── source-control/
│ ├── cicd/
│ ├── security/
│ ├── compliance/
│ ├── deployment-patterns/
│ └── ai-workflows/
├── accelerators/
│ ├── github-actions/
│ ├── policy-templates/
│ ├── evidence-generators/
│ ├── deployment-blueprints/
│ └── ai-workflow-prompts/
├── examples/
│ ├── minimal/
│ ├── reference-implementation/
│ └── enterprise-pattern/
├── scripts/
│ ├── validate-lab.sh
│ ├── generate-evidence.sh
│ └── check-boundaries.sh
└── tests/
├── workflow-tests/
├── policy-tests/
└── evidence-tests/
Each lab should follow this structure:
labs/<category>/<lab-name>/
├── README.md
├── design.md
├── boundaries.md
├── implementation/
├── tests/
├── evidence/
├── risks.md
└── next-steps.md
Each lab README must include:
- Lab name
- Status
- Category
- Objective
- Scope
- Out of scope
- Inputs
- Outputs
- Validation
- Security boundaries
- Compliance mapping
- Reuse model
| Status | Meaning |
|---|---|
| Draft | Concept exists; not yet implemented. |
| In Progress | Implementation or validation is underway. |
| Validated | Lab has repeatable validation and reviewable evidence. |
| Accelerator Candidate | Validated lab is ready to be packaged for reuse. |
| Deprecated | Artifact is no longer recommended. |
A lab is engineering-grade only when it has:
- Explicit scope and non-scope
- Reproducible setup instructions
- Automated validation where practical
- Security boundary documentation
- Evidence artifacts
- Known limitations
- Reviewable implementation
- No hidden production assumptions
- No uncontrolled secrets
- No autonomous AI authority
Idea
-> Lab Proposal
-> Bounded Prototype
-> Validated Pattern
-> Reusable Accelerator
-> Reference Implementation
This repository does not:
- Ship unreviewed production systems
- Replace formal security review
- Replace compliance audits
- Store production secrets
- Grant AI systems autonomous authority
- Operate as a live deployment control plane
- Deploy production infrastructure by default
The v1 Source of Truth for this lab is maintained at:
docs/SOURCE_OF_TRUTH.md
This document records the lab purpose, governance model, validation closure, current artifacts, and recommended next phase.
Clone the repository and run the full local validation, index generation, and evidence workflow:
git clone https://github.com/S3curethecloud/bounded-engineering-lab.git
cd bounded-engineering-lab
make all
Expected result:
Repository structure validation passed.
Boundary validation passed.
Markdown fence validation passed.
AI boundary validation passed.
Lab registry validation passed.
Lab metadata validation passed.
Generated lab index validation passed.
Evidence summary generated.
Key generated artifacts:
registry/generated/lab-index.jsonl
evidence-output/repository-evidence-summary.md
- Open a lab proposal issue.
- Define the intended boundary.
- Create the lab folder under the correct category.
- Add design, boundary, risk, validation, and evidence files.
- Run local validation:
./scripts/validate-lab.sh
./scripts/check-boundaries.sh
./scripts/generate-evidence.sh- Open a pull request using the repository PR template.
This project is licensed under the Apache License 2.0. See LICENSE.