vaig 0.1.0

GCAIaaS Governance Client for secure AI infrastructure

VAIG — VALO AI Integrity Gateway

Runtime governance layer for LLM inference. Apache 2.0.

VAIG sits architecturally outside the model it monitors — it cannot be influenced, overridden, or manipulated by the system it validates. This is the same design principle that makes Safety Instrumented Systems (IEC 61511) reliable in industrial process control.

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What it does

VAIG intercepts every LLM inference and returns a distrust score before the output reaches downstream systems.

from vaig import DistrustEngine

engine = DistrustEngine()
result = engine.evaluate(prompt=prompt, response=response)

# result.level: L0 TRUSTED | L1 MONITOR | L2 WARN | L3 DEGRADE | L4 HALT
# result.score: 0.0 – 1.0
# result.worm_hash: sha256:...

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Architecture

Layer	Component	Role
Runtime	DistrustEngine	8-instrument ensemble, probabilistic distrust scoring
Audit	WORM log	SHA-256 hash-chained, append-only, legally defensible
Control	Halt Switch	Binary HALT state, formally verified (TLA+)
Policy	Distrust Matrix	L0–L4 thresholds, configurable per deployment

Distrust levels

Level	Label	Action
L0	TRUSTED	Pass through
L1	MONITOR	Log and pass
L2	WARN	Flag for review
L3	DEGRADE	Throttle + alert
L4	HALT	Block + WORM entry

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Design principles

Externally positioned. VAIG runs outside the AI stack it governs. A model cannot influence its own validator.

Formally verified halt. The Halt Switch is verified using TLA+ model checking. When the trigger fires, the system stops — provably, regardless of model output.

Tamper-proof audit. Every inference is logged in a SHA-256 hash-chained WORM log. The log cannot be modified retroactively, making it suitable as legal evidence under EU AI Act Article 12.

Architecturally independent. VAIG is a proxy, not a plugin. It requires no changes to the model or the application layer.

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ACS compatibility

VAIG implements the core principles of the Agent Control Standard (ACS):

ACS principle	VAIG
Runtime Interception	Distrust scoring at every inference
Proof of Control	SHA-256 hash-chained WORM audit log
Mandatory Halt	Binary HALT, TLA+-verified (4.78M states, 0 counterexamples)
Contextual Guardrails	Policy-bound distrust matrix per deployment

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Compliance

Designed for:

• EU AI Act Annex III (high-risk AI systems) — audit trail, human oversight, logging
• PLD 2024/2853 — traceable causal chain for product liability
• Insurance-grade auditability — WORM log as Loss Prevention Record

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Installation

pip install vaig

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Benchmarks

Dataset	Domain	Result
MedQA (N=170)	Medical	0% error rate above L0 threshold
LEDGAR (N=300)	Legal	100% of out-of-domain documents flagged

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Status

• DistrustEngine (L0–L4)
• WORM audit log
• Halt Switch (TLA+-verified)
• MedQA / LEDGAR validation
• PyPI release under valo-systems org

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Contact

[e-mail]