AIR Trust

The flight recorder for autonomous AI agents. Record, replay, enforce, audit.

One proxy swap. Complete coverage. Runs locally.

# Before
client = OpenAI(base_url="https://api.openai.com/v1")

# After — everything else in your code stays identical
client = OpenAI(
    base_url="http://localhost:8080/v1",
    default_headers={"X-Gateway-Key": "your-key"}
)

Every LLM call now generates a signed, tamper-evident, replayable audit record. No SDK changes. No refactoring. No performance impact.

What You Get

Audit chain — every call produces an HMAC-SHA256 chained .air.json record, written asynchronously. Tamper with one record and every record after it breaks.

Quantum-safe signing — the chain is signed with ML-DSA-65 (FIPS 204 / Dilithium3). Keys are generated locally and never leave your machine. Post-quantum secure today.

Evidence bundle — one command packages the audit chain, scan results, and ML-DSA-65 signatures into a self-verifying .air-evidence ZIP. An auditor runs python verify.py and gets PASS/FAIL in two seconds. No pip install needed on their end.

PII and injection scanning — 20 weighted patterns across 5 attack categories detected before the prompt reaches the model. Configurable sensitivity. Auto-blocking.

EU AI Act gap analysis — 48 checks across Articles 9, 10, 11, 12, 14, 15. Maps to ISO 42001, NIST AI RMF, and Colorado SB 24-205. One scan, four frameworks, one report.

Replay — load any past episode from the audit chain, verify the HMAC signature, and replay every step with timestamps. Incident reconstruction without guesswork.

Framework trust layers — drop-in wrappers for LangChain, CrewAI, OpenAI Agents SDK, Anthropic, AutoGen, Google ADK, and Haystack. Same audit chain, native integration.

Quickstart

pip install air-blackbox

# Run your first gap analysis — works on any Python AI project
air-blackbox comply --scan . -v

# Find undeclared model calls hiding in helpers and utilities
air-blackbox discover

# Replay any recorded episode
air-blackbox replay

# Generate a signed evidence package for audit or regulator review
air-blackbox export

Full stack (Gateway + Episode Store + Policy Engine + observability):

git clone https://github.com/airblackbox/air-platform.git
cd air-platform
cp .env.example .env      # add OPENAI_API_KEY
make up                   # running in ~8 seconds

• Traces → localhost:16686 (Jaeger)
• Metrics → localhost:9091 (Prometheus)
• Episodes → localhost:8081 (Episode Store API)

How It Fits Your Stack

Your Agent
    │
    ▼
AIR Gateway          ← swap base_url here
    │
    ├── PII + injection scan      (before prompt reaches model)
    ├── HMAC audit record         (async, zero latency impact)
    ├── ML-DSA-65 signing         (keys never leave your machine)
    │
    ▼
LLM Provider         ← OpenAI / Anthropic / Azure / local
    │
    ▼
AIR Record           ← tamper-evident .air.json
    │
    ▼
Evidence Bundle      ← self-verifying .air-evidence ZIP

Works with any OpenAI-compatible API. Same format, same integration, regardless of provider.

Why Not Just Log Everything?

You probably already have logging. The problems logging doesn't solve:

Tamper-evidence — anyone with write access to your log store can alter a record. HMAC chains make alteration detectable. ML-DSA-65 signatures prove who signed and when.

Prompt reconstruction — most logging captures responses but not the full prompt context, tool calls, and intermediate reasoning. AIR records the complete episode.

Compliance structure — EU AI Act Article 12 requires tamper-evident logs with specific retention and audit access guarantees. Raw logs don't satisfy that. Evidence bundles do.

Secrets leaking into traces — every team that builds their own logging eventually discovers credentials in their observability backend. AIR strips and vault-encrypts API keys before writing any record.

Framework Trust Layers

# LangChain
pip install air-langchain-trust

# CrewAI
pip install air-crewai-trust

# OpenAI Agents SDK (included in gateway)
from air_blackbox.trust import OpenAITrustLayer

Each trust layer produces the same HMAC audit chain as the gateway, natively, inside the framework's execution loop. Pick the integration that fits your stack.

Validated By

• Julian Risch (deepset) — public validation on LinkedIn and GitHub issue #10810
• Piero Molino (Ludwig maintainer) — merged EU AI Act compliance changes driven by AIR scan results
• arXiv AEGIS (March 2026) — independent researchers published the identical interception-layer architecture for AI agent governance
• McKinsey State of AI Trust 2026 — trust infrastructure named as the critical agentic AI category

Contributing

See CONTRIBUTING.md.

False positive on a compliance check? Correct it — your correction flows into training data for the fine-tuned scanner model. The scanner gets smarter with every fix your team submits.

Good first issues: labeled good first issue — mostly new compliance checks and framework integrations.

License

Apache-2.0 — airblackbox.ai

This is not a certified compliance test. It is a starting point to identify potential gaps.

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If this helps you prepare for EU AI Act enforcement, star the repo — it helps other teams find it.