A New Category of Risk
When your developers start using AI coding assistants, you've introduced a new category of risk into your software delivery process. This isn't theoretical — it's practical, immediate, and often unaddressed:
- Intellectual property risk: AI models trained on open-source code may generate output with licensing implications
- Quality risk: AI-generated code may introduce subtle bugs that pass superficial review
- Compliance risk: Regulated industries may need to demonstrate human oversight of AI-assisted development
- Security risk: AI tools may inadvertently introduce vulnerabilities or expose sensitive context
Most organizations are managing these risks informally — through developer judgment and ad-hoc guidelines. That works at small scale. It doesn't work when AI becomes embedded across your entire SDLC.
Building a Governance Framework That Works
Effective AI governance for engineering teams balances three competing forces: enabling innovation, managing risk, and maintaining development velocity. A framework that's too restrictive kills adoption. One that's too permissive accumulates risk.
Acceptable Use Policies
Start with clear, practical policies that developers can actually follow. Define what AI tools are approved, what tasks they can be used for, and what review requirements apply to AI-assisted output. Avoid vague principles — engineers need specific, actionable guidelines.
Risk-Based Review Tiers
Not all AI-assisted code carries equal risk. Establish review tiers based on the risk profile of the code being generated:
- Low risk: Boilerplate, test scaffolding, documentation — standard review process
- Medium risk: Business logic, API implementations — enhanced review with AI-awareness
- High risk: Security-sensitive code, authentication, data handling — mandatory human review with security sign-off
Audit Trail Design
For regulated organizations, the ability to demonstrate what was AI-assisted and what was human-authored becomes important. Design your development workflow to capture this information naturally — through commit metadata, PR templates, and CI pipeline annotations — without adding friction.
The Governance Maturity Curve
Organizations typically progress through three stages of AI governance maturity:
- Reactive: Policies created in response to incidents or compliance questions
- Structured: Proactive framework with defined policies, review processes, and monitoring
- Optimized: Governance embedded into toolchain and workflows, continuously adapted based on data
Most organizations are still at stage one. Moving to stage two — a structured, proactive governance framework — is the highest-impact investment for managing AI risk in engineering.
Don't Let Perfect Be the Enemy of Good
You don't need a perfect governance framework before enabling AI in your SDLC. But you do need a deliberate one. Start with acceptable use policies and risk-based review tiers, then iterate as your understanding of the risks matures. The biggest governance risk is having no framework at all.