Prevent AI from rewriting trusted code — selective AI assistance

Many teams already experience the unintended side effects of in-editor AI assistants: automated refactors, suggested edits, or copilot-style completions that rewrite critical, security-sensitive, or IP-bearing code without clear provenance. This problem is most acute for enterprise engineering orgs, regulated industries, and open-source maintainers — collectively addressing a potential user base within the 25M developers driving an $18.0B market for dev tools and AI assistants. The product would expose "selective AI assistance": an IDE extension and gateway that enforces per-file or per-module trust levels, applies policy-as-code to permit read-only or review-required AI suggestions, attaches cryptographic provenance and audit trails to any AI-originated change, and integrates with Git and CI/CD to block or require approvals for risky rewrites. It would be offered as both a SaaS policy engine for large teams and a deployable on-premises appliance to satisfy compliance needs. The timing is favorable because rapid adoption of LLM-driven tooling is increasing accidental automated changes, while enterprises are concurrently demanding governance — reflected in a market score of 92/100 and a revenue potential of 88/100 for focused solutions. To stand out you should prioritize low-friction workflows (minimal clicks to elevate or lock files), deep IDE and VCS integrations, and verifiable provenance (signed AI outputs) as a baseline, while also providing flexible policy templates for SRE, security, and legal teams. Be honest about challenges: convincing developers to accept occasional restrictions, supporting a long tail of editors and CI systems, and keeping pace with evolving models and threat vectors will require sustained engineering and partner investment.