Enterprise AI needs a doorway before it acts.
AI can change files, call tools, update systems, and start workflows. ZLAR puts a rule in front of those actions. The rule can allow, block, or ask a person.
The hard part is not AI writing. It is AI doing.
The model can be useful and still need a door. When it is about to touch a repo, a ticket, a customer record, a workflow, or a system setting, someone needs to know what rule applies.
Without that door, every new AI capability adds more watching. People hesitate because no one can point to the place where the action is checked.
Bring one real action: what can pass, what must stop, and what needs a yes?
The rule comes before the action.
Allow, block, or ask
ZLAR checks routed actions against signed rules before they proceed.
The yes is separate
Important actions can ask a named person outside the AI's runtime. Silence is not consent.
The action leaves a receipt
The receipt shows what the AI tried to do, what rule was used, and what happened.
Start with one real action.
Pick a concrete action: a repo change, deployment step, sensitive file edit, internal workflow trigger, MCP tool call, or record movement. Define what is allowed, what is blocked, and when a person must say yes.
The point is not to slow every action down. The point is to make the important yes real and leave proof afterward.
Next action
Bring one action you need governed. The useful first conversation is concrete: which action, which rule, which person, which receipt.
Boundary
- ZLAR governs routed/intercepted action surfaces only.
- It does not replace IAM, SIEM, DLP, endpoint controls, model risk, legal review, or security architecture.
- Serious deployment closes bypass paths with sandbox, OS, network, and platform controls.
- Safe Codex wording: "ZLAR can govern Codex CLI-invoked MCP tool calls when those MCP servers are routed through ZLAR."