Need to Govern AI-Generated Code
Morgan needs to ensure AI coding assistants follow security policies when generating code, with attestation of compliance.
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Persona Story:
Morgan, the security & compliance lead, needs to ensure AI coding assistants follow security policies when generating code, with attestation of compliance.
Problem Context
- Developers are using AI coding assistants that generate code without security guardrails
- IDE-based security scanning has only ~20% adoption—developers find it too noisy
- Traditional CI/CD pipeline gates catch issues too late
Problem Impact
- AI-generated code introduces vulnerabilities that aren’t caught until production
- Security teams have no visibility into whether AI followed secure coding practices
- 200 days average to fix vulnerabilities discovered in production
Naftiko Today
- Executable YAML specifications replace AI-generated integration code entirely, removing the vulnerability surface of hand-written or AI-generated glue code
- JSON Schema validation and Spectral ruleset (15 rules) enforce structural and security compliance on every capability spec before deployment
- outputParameters normalization layer ensures raw API payloads never reach the LLM, acting as a governance boundary between external data and AI agents
- External bindings for secrets/tokens/env vars prevent credentials from being hardcoded in specs, eliminating a common AI-generated code vulnerability
Naftiko Tomorrow
- Tool annotations (Second Alpha) with readOnly/destructive/idempotent markers would let security teams classify and restrict what AI agents can do
- MCP auth (Second Alpha) would provide standardized authentication governance for AI agent interactions
- Conditional steps with if/for-each/parallel-join (Second Alpha) would replace complex AI-generated orchestration logic with governed, declarative flow control
- Resiliency patterns like circuit breaker, rate limiter, and fallback (First Beta) would add production-safe guardrails that AI-generated code typically lacks