AI Code Safety Gate
The execution gate for AI-generated code.
AI coding tools are executing. This is the gate.
Teams using AI coding tools need to know which changes should run automatically, which need a human to sign off, and which should stop. PROVE BY GENESIS adds that layer — a gate that returns a clear decision before execution.
The demo shows the decision surface. The private core goes deeper.
Without GENESIS
AI-generated code moves from suggestion to execution without a formal decision record, approval boundary, or evidence trail. Changes run with no governance layer in place.
With GENESIS
AI-generated changes pass through a pre-execution gate. Every change produces a decision: ALLOW, REQUIRE_APPROVAL, or BLOCK. Every decision carries a reason. In the private core, approvals are tracked with structured evidence.
Try the demo
Two-minute check: try Safe Change, Dead-Code Gate, Export Nullification, then paste one snippet of your own and see whether the decision feels right.
This is a public demo — it evaluates simple visible patterns.
This demo is intentionally simple. It shows the GENESIS decision model without exposing the private execution core. It is not a production-grade security engine, not a semantic analyzer, and not a replacement for code review.
The goal is to show the concept clearly, not to overclaim what it does.
The decision model
ALLOW
No known public-demo risk pattern was detected. Cleared only by the limited demo rules.
REQUIRE_APPROVAL
The change is ambiguous or risky. A human approval boundary is created before execution.
BLOCK
The change matches a destructive pattern. It does not reach execution. The reason is recorded.
The value is the approval boundary and evidence record, not just detection. The private GENESIS core provides the execution-control architecture behind this model.
Going deeper
What the private GENESIS core adds
Behind this demo is a private GENESIS core — the deeper execution-control architecture:
→ action
→ safety gate
→ execution decision
→ evidence
→ verified result
- Workflow context and execution policy evaluation
- Approval gates with structured evidence bundles
- Audit trail for every AI-generated execution decision
- Verified result tracking across the full execution cycle
- Product direction: future integration with AI coding workflows such as Cursor, Claude Code, Copilot, and similar agents
Why it matters
The governance gap in AI coding workflows
AI coding tools increase speed. But speed without a control layer creates gaps that are hard to see until something goes wrong.
- An AI agent creates a hotfix at 11pm. It passes tests. It merges automatically. Who approved the execution logic?
- A background agent rewrites an authentication module across 14 files. The diff is large. No REQUIRE_APPROVAL boundary triggered.
- An AI tool modifies a deployment configuration. The change runs in CI. No execution decision was recorded. No evidence trail exists.
GENESIS creates the approval boundary and evidence trail that AI coding workflows currently lack. It's the difference between speed with oversight and speed without it.
Building with AI coding tools?
We'd love to hear what execution-control challenges you're running into.
Email hello@aicodesafety.com