The Sovereign Software Factory
A Strategic Framework for Securing AI-Augmented Development
Executive Summary
The Sovereign Software Factory is an adversarial validation system where architectural integrity is mathematically enforced rather than left to developer discipline. In the era of "vibe coding," AI is free to generate, but our architecture must rigidly constrain what is allowed to persist.
The Challenge of AI-Generated Code
Research consistently shows that 30-50% of AI-generated code contains vulnerabilities. The core risks include:
- Dependency Hallucination: LLMs suggest plausible but non-existent packages, enabling supply chain attacks
- Insecure Defaults: Wildcard CORS, hardcoded secrets, deprecated cryptography
- Logic Hallucinations: Syntactically valid but functionally incorrect code for edge cases
- Architectural Erosion: AI violates layered boundaries to achieve local goals
The 4-Layer Defense Model
Layer 1: Local Perimeter
Verification begins at the source to prevent defects from entering the repository.
- Secret Scanning:
detect-secretsorgitleaksto block hardcoded credentials - Dependency Verification:
dep-hallucinatororlettucedetectto verify package existence - Provenance Tracking:
Co-authored-by: AI-Assistanttrailers for forensic analysis - IDE Integration: Snyk Code, SonarLint for real-time vulnerability feedback
Layer 2: CI/CD Citadel
The CI server serves as the authoritative gatekeeper with policy-as-code enforcement.
- Zero Technical Debt: Tolerance for new issues in AI-generated code is zero
- 100% Security Hotspot Review: Mandatory human review for auth/crypto code
- Duplication Threshold: <3% to prevent AI-driven code bloat
- Quality Gates: SonarQube AI Code Assurance policy
Layer 3: Structural Enforcement
Architecture-as-Code to mathematically enforce boundaries.
- ArchUnit (Java): Assert domain models never depend on infrastructure adapters
- Dependency-Cruiser (JS): Prevent circular dependencies and server-side leaks
- Deply (Python): Enforce naming conventions and prevent import cycles
Layer 4: Advanced Verification
Meta-testing to catch what unit tests miss.
- Mutation Testing (Stryker/Pitest): Inject "mutants" to verify test suite actually catches breaks
- Property-Based Testing (Hypothesis): Verify invariants across thousands of random inputs
Trust Tier Framework (T0-T3)
Trust in AI systems must be earned through validated performance.
| Tier | Description | Examples | Advancement Criteria |
|---|---|---|---|
| T0: Observational | Read-only; agent provides recommendations | Triage suggestions, incident summaries | Baseline performance audit |
| T1: Approval-Gated | Actions require explicit human confirmation | Rollback proposals, feature-flag tuning | 85% accuracy over 30 decisions |
| T2: Narrow Autonomy | Agent acts within predefined safety envelopes | Auto-rollback if canary <20% traffic | 90% alignment over 50 actions |
| T3: Conditional Full Autonomy | Full capability with continuous adversarial audit | Automated promotions, kill-switch tuning | 95% success; 0 policy violations over 100 deploys |
Golden Paths & Policy-as-Code
We move from trust-based governance to Golden Paths — opinionated, task-specific templates with built-in compliance.
package cicd.governance
# Default deny
default allow = false
# Allow autonomous rollback if confidence is high
allow {
input.action == "rollback"
input.confidence >= 0.8
input.environment == "canary"
}
# Hard Constraint: Block on critical CVEs
deny[msg] {
input.action == "promote"
input.security_scan.critical_cves > 0
msg := "Deployment blocked: reachable critical CVEs detected."
}Proven Results
Case study results from React 19 microservice migration:
CISO Strategic Checklist
- ✓ Define "Paved Roads" as recommended tools; mandate "Golden Paths" for all vibe coding
- ✓ Vibe coding is strictly permitted only on Golden Paths where security is handled by the platform
- ✓ Every application has a clear human owner
- ✓ Maintain immutable logs of AI prompts, models, and policy outcomes via JSON trace IDs
- ✓ Configure OPA Policy Engine to increase confidence thresholds during critical release windows