Phase 1: Realistically assess your own situation
The roadmap doesn't start with a tool decision, but with an honest assessment: the central insight from "The Productivity Myth: What AI Coding Tools Actually Deliver" – benefit depends heavily on context, not on industry promises. The three context factors from "Where AI Coding Tools Really Help — and Where They Don't" (task type, codebase, experience level) supply the concrete assessment frame for this.
Phase 2: Establish review mode as the standard
From "Why More Than Half of AI-Generated Code Has a Security Flaw" follows the second phase: have every generated code section checked in an explicit second step for the six known vulnerability classes, instead of blindly trusting the first result.
Phase 3: Secure the supply chain
The third phase implements "Slopsquatting: When AI Invents Software Packages": verify every suggested package name, pin versions, run hash checks – before an invented dependency turns into a real vulnerability.
Phase 4: Governance instead of case-by-case decisions
The fourth phase anchors the first three phases structurally, as described in "Governance Instead of Chaos: A Policy for AI Coding Tools": usage guidelines, an approval process like for any other vendor tool, a review threshold, automated scans, and mandatory training – instead of making every decision individually and without documentation.
Practice section: Phase 5 – the security check before every go-live
The last phase doesn't apply just once, but to every single prototype-to-production transition: the four-point checklist from "From Prototype to Production: The Security Check Before Go-Live" – dependency audit, security scan, review threshold, data-flow check. The roadmap therefore doesn't end with a one-time project close-out, but with a recurring check that runs again for every new release.