FAQFrequently Asked Questions
What OntoGuard does
Can we start without internal documents?
Yes. The first review can start with representative prompts, responses, API logs, intended action labels, workflow labels, or demo outputs. No internal legal files, policy manuals, production deployment, or model retraining are required to begin.
Is OntoGuard a compliance tool or an authorization layer?
OntoGuard is a semantic authorization layer and Decision Authorization Infrastructure. Compliance readiness is one use case; the broader product governs whether high-stakes AI output is allowed to move, must be blocked, or should be routed to human review.
How to start
What is the fastest first step?
Run an AI Output Risk Scan with 10–25 representative outputs or logs. OntoGuard returns risk, uncertainty, review-burden, autonomy-readiness, and selected proof-packet findings.
What do we receive after an AI Output Risk Scan?
You receive a behavior risk map, review-burden analysis, autonomy-readiness view, training-signal candidates, and selected Decision Authorization Packets for high-risk or representative outputs.
Does OntoGuard require production deployment to start?
No. Initial reviews can run in shadow mode from exported prompts, responses, logs, and workflow labels. Production route integration is only needed for production L5 evidence; see the Capability Boundary.
What it proves
What does OntoGuard prove today?
OntoGuard can produce a controlled Decision Authorization Packet proving that an AI output or proposed state transition was evaluated before release, release was authorized or withheld, refused operation and no-bind status were recorded, replay was shown, route pressure tests ran, and controlled route-enforcement evidence was attached. See the Runtime-Seam Proof and Public Boundary Proof Kit.
What it does not claim
Does OntoGuard prove production non-bypassability today?
Not without production integration evidence. OntoGuard can assert controlled-harness non-bypassability for the controlled proof topology. Production non-bypassability requires a pilot or production route where the real downstream endpoint cannot commit without OntoGuard authorization.
Technical diligence
What is a no-bind receipt?
A no-bind receipt is a compact proof record showing that a blocked or escalated output did not form the protected downstream effect. It binds the attempted movement, Decision API result, release authorization state, refused operation, downstream commit status, protected-effect status, trace ID, manifest hash, and evidence hash.
What is route completeness?
Route completeness describes how much proof exists around a governed AI movement: event governed, no-bind proof, route registered, route tested, route enforced, or route complete. The current proof package supports controlled L4 route enforcement. Production L5 requires real production topology evidence; see the L4 to L5 progression.
Does OntoGuard train the model?
Not in the runtime path. OntoGuard exports review-gated training-signal candidates and improvement signals. It does not perform live fine-tuning, model-weight updates, DPO, RLAIF, PPO, or adapter loading during governance.
What is a Decision Authorization Packet?
A Decision Authorization Packet is a portable PDF and JSON proof artifact showing how a high-stakes AI output was authorized, escalated, or blocked before release. It records the ALLOW, BLOCK, or ESCALATE decision, evidence, reasons, uncertainty, semantic projection, execution-boundary trace, no-bind status, route maturity, human-review routing, audit hashes, and improvement signals.
What is state-transition governance?
State-transition governance asks whether a proposed AI change is authorized, traceable, and safe to commit. The proposal may be an LLM output, tool call, memory write, ontology update, policy mapping, training signal, or multi-agent handoff. Current production route enforcement for tool calls, memory writes, and handoffs requires customer-specific integration evidence.
Do you support tool calls, memory writes, and multi-agent handoffs today?
Not at the same production-enforcement level as governed LLM output release. The Decision API and JSON contract are designed to support these use cases through customer-specific integration. Production evidence requires real downstream routes, token checks, bypass-attempt logs, replay, and outcome closure.
How does OntoGuard use ontology AI?
Ontology-grounded objects, relationships, rules, clause hits, domain scope, evidence, and symbolic traces connect AI proposals to enterprise reality. This semantic layer helps OntoGuard explain why a proposed AI movement was allowed, blocked, or escalated.
What happens when evidence is incomplete?
The packet still exports. OntoGuard routes uncertainty to SAFE_TEMPLATE or HUMAN_REVIEW, records reason codes, preserves audit hashes, flags coverage gaps, and avoids silently blank evidence or failed artifacts.
