Security & Data Governance

Architected for strict source-code isolation and deterministic control

Enterprise codebases are core intellectual property. CodeLoom AI is designed from day one so customer source code is never used to train foundation models and every test execution runs inside an isolated, ephemeral sandbox.

Data Policy

Zero training on customer repositories

Customer source code, AST dependency graphs, and characterization test outputs are never used to train, fine-tune, or improve shared AI models. All model inference calls are stateless.

Execution Boundary

Ephemeral sandboxed test runners

Baseline characterization suites and candidate migration diffs execute inside network-restricted, ephemeral containers that are destroyed immediately after the verification run completes.

Deployment Control

Self-hosted VPC & on-premises runner architecture

For regulated organizations, CodeLoom AI's graph analysis and test execution engine is designed to run entirely inside your own cloud VPC or on-premises CI infrastructure.

Human Governance

No autonomous merges to protected branches

CodeLoom AI never pushes directly to main or production branches. Its sole output is a scoped, test-verified pull request subject to your existing branch protection rules and human code review.

Security & Architecture Inquiries

Evaluating CodeLoom AI for an enterprise or regulated environment?

Reach out directly to our engineering leadership to discuss VPC runner topology, sandbox isolation, and pilot scope.