Deterministic Trust Infrastructure for AI Systems
Governance that proves itself.
A live control plane governing what AI can do, what AI can see, and what AI can act upon — with cryptographic proof produced as a byproduct of operation.
The Problem
Your governance was built for human speed.
Scan after the fact
Code is scanned after it exists. Violations discovered after a PR is opened. Evidence assembled when the auditor asks.
AI governing AI
One model evaluates another. Confidence scores don't satisfy auditors. Probabilistic oversight is not deterministic control.
Tool fragmentation
AppSec, quality, policy, exceptions, evidence — each with its own config, its own risk model, its own evidence surface.
Evidence fragmentation
Screenshots, tickets, interviews, narrative attestations. Assembled retrospectively. Expensive. Fragile.
The Control Model
Three surfaces. One trust architecture.
Any AI system must be governed across what it can do, what it can see, and what it can act upon.
1. Capability
What AI can do
Deterministic zone-based constraints govern which AI agents can write to which parts of your codebase. Signed policies. Cryptographic attestation.
Avarion →2. Visibility
What AI can see
Context boundary enforcement ensuring AI models only receive data they are authorised to process. Audit-ready access records on every inference.
Vault →3. Execution
What AI can act on
Runtime guardrails controlling which tools, APIs, and external systems an AI agent may invoke — with signed evidence on every action.
Meridian →Cross-Cutting Orchestration
What AI can do over time
Temporal sequence governance — cooldowns, prerequisites, approval gates.
Avarion
What if every AI action in your codebase produced signed, cryptographic proof?
Not as a separate step. Not as a log you reconstruct later. As a byproduct of the action itself.
$ avarion hook pre-commit
ALLOWED ui/admin-report.tsx zone:ui risk:low
ALLOWED tests/report.test.ts zone:tests risk:none
ALLOWED service/user-service.go zone:service logged
WARN auth/session.go zone:auth — rationale required
DENIED payments/billing.go zone:payments — AI write access denied
5 files evaluated · 3 allowed · 1 warn · 1 denied
Attestation: ed25519:a7f3c...9c2e
Bundle: proof_bundle_2026-04-05T14:23:07.json
Policy: governance.yaml@sha256:e4b2f...
Not Retrospective. Continuous.
Compliance by construction.
Evidence is not assembled after the fact. It is produced at the moment of action, structured, signed, and machine-verifiable.
SOC 2
CC-6.1, CC-7.1, CC-8.1
Change control, access governance, audit trail
EU AI Act
Art. 9, 10, 11, 14
Risk management, data governance, documentation
GDPR
Art. 25, 32, 44-49
Data minimization, cross-border transfer, security
Built for the teams who feel it most.
For CISOs & Security Leaders
Continuous compliance evidence without manual assembly.
Every governance decision produces signed proof mapped to SOC 2, EU AI Act, and GDPR controls. No screenshots. No quarterly scramble.
For Engineering Leaders
Governance that runs at developer speed, not against it.
Sub-50ms enforcement at pre-commit. Time-boxed exceptions with auto-expiry. Your teams ship faster because governance is in the path, not in the way.
For GRC & Compliance
Machine-verifiable proof, not narrative attestations.
Structured, signed evidence bundles that auditors can verify independently. Built for continuous auditability, not periodic evidence collection.
The Architectural Shift
“The shift is from policy documents and post-hoc checks to live controls and continuous proof.”
— ByteVerity Whitepaper, April 2026
See it in action.
Run a governed scenario. Generate proof. Export evidence. Under three minutes.