Our approach is backed by research from:
Free frameworks, tooling guides, and governance standards for implementing Gen AI coding safely with production-grade safeguards.
Our approach is backed by research from:
You've leveraged AI to build faster than ever. But speed comes with hidden risks that could derail your entire project.
of AI code contains exploitable vulnerabilities like SQL injection, XSS, and insecure API endpoints2
of AI optimizations actually introduce bugs that can cripple your app at scale3
business context means AI makes design choices requiring expensive refactoring later
harder to maintain than human code, slowing future development to a crawl3
AI often generates code that violates GDPR, HIPAA, or SOC2 requirements without proper data handling4
AI frequently suggests hardcoded API keys, passwords, and credentials that end up in version control5
Don't wait for a breach to find out what your AI missed.
See How We Can Help →A multi-layered defense architecture that makes secure AI coding the default path, not an afterthought. Quality becomes a physical constraint, not a policy.
Pre-commit hooks, secret scanning (Gitleaks), dependency hallucination detection, and AI provenance tracking at the developer's workstation.
Policy-as-Code enforcement with OPA/Rego, SonarQube AI Code Assurance gates, SAST/DAST scanning, and Trust Tier governance (T0-T3).
Architectural linters (ArchUnit, Dependency-Cruiser) prevent layer violations and spaghetti dependencies. Mutation and property-based testing verify test integrity.
Opinionated, pre-validated templates via Internal Developer Platforms. Policy engines enforce registry whitelists and non-root execution.
Research-backed documentation for implementing secure AI-driven development.
The complete multi-layered defense architecture for AI-augmented development.
Practical tooling configurations and workflows for secure AI-assisted development.
Formal governance invariants and safety standards for autonomous AI agents.
Comprehensive glossary of emerging risks and attack surfaces in AI-generated code.
Our approach is grounded in extensive research on AI code generation risks and vulnerabilities
As AI coding tools become ubiquitous, the gap between perceived productivity and actual code quality widens. These guides provide the frameworks and tooling needed to validate AI-generated code before it reaches production.
The Sovereign Software Factory is a multi-layered defense architecture that makes secure AI coding the default path. It combines local perimeter controls, CI/CD gates, structural enforcement, and golden paths to ensure code quality at every stage.
Vibe coding refers to the practice of rapidly generating code with AI assistants without proper verification. While it accelerates development, it can introduce dependency hallucinations, logic errors, and insecure defaults that require structured safeguards to catch.
Trust Tiers are a governance framework for gradually increasing AI agent autonomy. T0 is observational (read-only), T1 requires human approval, T2 allows narrow autonomous actions, and T3 is conditional full autonomy with continuous audit.
Key tools include Gitleaks/detect-secrets for secret scanning, SonarQube for code quality, Snyk for vulnerability detection, ArchUnit/Dependency-Cruiser for architecture enforcement, and Stryker/Hypothesis for mutation and property-based testing.
Yes! The frameworks are technology-agnostic and cover JavaScript/TypeScript, Python, Java, Go, and other major languages. Tool recommendations include options for each ecosystem.
Start with the Sovereign Software Factory guide for the complete framework overview, then dive into the Vibe Coding guide for practical tool configurations. The Risks Glossary helps you understand what threats you're defending against.
All resources are free and open. Start with the Sovereign Software Factory framework or browse the complete guide library.