luma-audit

Deterministic governance for AI actions. No LLM in the decision path.

What it does

luma-audit is a deterministic execution-control layer that approves or rejects AI actions before they happen.

• Deterministic - same input, same output. Always.
• Outside the model - a layer the LLM cannot bypass.
• Auditable - every decision is hash-chained.

It does not filter text, detect toxicity, or replace authentication. It audits actions.

Install

pip install luma-audit

30-second example

from luma_audit import LUMA

auditor = LUMA()
auditor.avaliar('ler_ficheiro')      # -> aprovada
auditor.avaliar('apagar_ficheiro')   # -> rejeitada
auditor.avaliar('acao_desconhecida') # -> rejeitada (unknown = high risk)

What it does NOT do

• Does not filter text (that is a prompt guardrail - different problem)
• Does not detect toxicity
• Does not replace authentication
• Does not use any LLM in the decision path

Architecture

src/luma_audit/
    auditor.py       # LUMA class + Veredito enum
    trajetoria.py    # Sessao, AuditorTrajetoria, Decisao, hash chain
    canal.py         # CanalUnico, MensagemFunil
    funil.py         # FunilUnico, FILA_DIR
    cli.py           # luma CLI entrypoint

Tests

22 red-team tests: hash chain integrity + tampering, quarantine escalation, flood detection, atomic writes, corrupted proposal rejection, executor revalidation.

Philosophy

Guardrails based on prompts compete against the model's weights - and lose. The only layer that cannot be bypassed is the one running outside the model, at the point of execution.

luma-audit is that layer.

License

MIT. Community Edition. Enterprise Edition available commercially.