
ai-audit-trail
io.github.sundsoffice-techPublished by GitHub account sundsoffice-techv0.4.7Updated Oct 27, 2026
Tamper-evident audit trails for AI agents — Ed25519-signed, hash-chained, EU AI Act ready.
The publisher's own server: audit.domains - Domain Valuation
Overview
Local Python MCP server exposing ai-audit-trail: Ed25519-signed, hash-chained audit receipts for AI agent decisions and tool calls.
- What you can do
- Create and seal decision receipts for AI requests, with input, output, check results, action and model provenance
- Verify tamper-evidence of receipt chains and tool-call chains via signature, self-hash and hash-chain checks
- Redact or hash PII, encrypt fields with per-tenant keys, and shred keys for erasure
- Map receipts to ISO 42001, NIST AI RMF and EU AI Act controls, and export signed evidence packages for offline audit
- Monitor agent behavior with contracts, trace-graphs, provenance chains, SPRT certification and drift detection
- Batch-seal receipts with Merkle trees, manage chain epochs, and emit OpenTelemetry metrics
- When to use it
- For teams running LLM agents or pipelines that must show auditors what the AI did, why, and that the record was not altered, especially where EU AI Act, ISO 42001 or NIST AI RMF evidence is expected. Useful when you want self-hosted, offline-verifiable audit records rather than a SaaS logging platform.
- Requirements
- Runs as a local stdio process started with the ai-audit-mcp command from the PyPI package ai-audit-trail. Needs Python 3.11 or newer; no external services, accounts, API keys or environment variables are declared. Optional extras add Redis persistence and OpenTelemetry. Production use expects a persistent Ed25519 signing key, typically supplied through a KMS or Vault key provider.
Installation
In SourceWeft
- Open ai-audit-trail in the dashboard and add it to a workspace.
- Enable the server for the chats that should use its tools.
Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.
Other MCP clients
Follow the launch instructions in the repository.
README
ai-audit-trail
[PyPI version] [Python] [License: MIT] [CI] [Downloads] [Tests] [mypy] [Ruff] [OpenSSF Scorecard]
Prove what your AI did, why, and that nobody changed the record.
Tamper-evident Decision Receipts with Ed25519 signatures, SHA-256 hash-chains, and formal compliance mappings. No blockchain, no SaaS, no lock-in. Self-hosted, offline-verifiable, Python-native.
Why this exists
The EU AI Act becomes mandatory for high-risk AI systems in August 2026. It requires tamper-evident logs proving every decision was made correctly (Art. 12). Most teams are solving this with normal logging — which is neither tamper-evident nor legally defensible in an audit.
ai-audit-trail closes this gap with cryptographic receipts that any auditor can verify offline, without accessing your systems. Same principle as a blockchain — without the blockchain overhead, the SaaS dependency, or the vendor lock-in.
Who is this for
- Regulated AI teams (FinTech, HealthTech, LegalTech, InsurTech) who must prove compliance
- Enterprise platform teams deploying LLM agents with tool access
- Security and compliance officers who need audit-ready evidence packages
- Developers who want
pip installand 3 lines of code, not a platform migration
What this library provides
ai-audit-trail provides the technical building blocks that support EU AI Act, ISO 42001, and NIST AI RMF compliance. It does not, by itself, guarantee regulatory compliance — compliance is an organizational obligation that extends beyond any single software component. See our Shared Responsibility Model below.
Installation
Requirements: Python 3.11+ | No external services required | Works air-gapped
Quickstart
Architecture Overview
Core Features
Decision Receipts (Ed25519 + SHA-256 + Hash-Chain)
Every AI pipeline decision produces a Decision Receipt — a cryptographically sealed, hash-chained record:
Three-stage verification (< 0.1 ms per receipt):
PII Redaction (GDPR Art. 17)
Personal data is stripped before hashing — the audit log never contains raw PII.
Crypto-Shredding (GDPR Right to Erasure)
Encrypt PII fields with per-tenant AES-256-GCM keys. Destroy the key = data permanently unreadable, hash-chain intact.
Compliance & Governance
ISO 42001 / NIST AI RMF Crosswalk
Maps receipt data directly to recognized management controls with evidence pointers.
ISO 42001 Controls: A.6.2.8 (Logging), A.7.5 (Provenance), A.6.2.6 (Performance), A.8.4 (Output), A.5.3 (Risk) NIST AI RMF: GOVERN, MAP, MEASURE, MANAGE — with quantitative coverage scores
EU AI Act Compliance Reports
Covers Art. 9 (Risk), Art. 12 (Record-Keeping), Art. 13 (Transparency), Art. 17 (Quality), Art. 18 (Logging).
Evidence Package Export (Offline Verification)
Self-contained signed ZIP for external auditors — no system access required.
Continuous Certification (SPRT)
Sequential Probability Ratio Test — live compliance status per tenant.
Drift Detection (Jensen-Shannon Divergence)
Detects behavioral shifts in real-time — pure Python, no scipy required.
Agentic AI Audit (World-First)
Agent Behavioral Contracts
Formal (p, delta, k)-Satisfaction with Reliability Index Theta — transforms EU AI Act requirements into testable specifications.
Cryptographic Tool-Call Receipts
Every agent API call Ed25519-signed — no existing framework provides this.
Multi-Agent Trace-Graphs (DAG)
Audit delegation, handoff, and parallel orchestration — not just linear logs.
Epistemische Integritat / Unforgeable Provenance
Track WHERE every piece of information came from — proves a decision was not influenced by prompt injection.
High-Throughput Architecture
Merkle-Tree Batch Sealing (RFC 6962)
Chain-of-Roots instead of chain-of-receipts — O(log N) verification per batch.
Chain Epochs / Rollover
Prevent unbounded chain growth. Old epochs can be archived or deleted.
Ring-Buffer with Backpressure
Bounded buffer for high-throughput ingestion — fail-closed, no silent data loss.
Storage Backend ABCs
Pluggable persistence — bring your own database.
OpenTelemetry Instrumentation
Native metrics for SRE dashboards — graceful no-op without OTel SDK.
Metrics: ai_audit.seal_duration_seconds, ai_audit.append_total, ai_audit.redis_fallback_total, ai_audit.chain_break_total, ai_audit.drift_score, ai_audit.buffer_size, ai_audit.epoch_sealed_total
Production Setup
Persistent Signing Key
KMS Integration
Redis Persistence
Shared Responsibility Model
Examples
See the examples/ directory:
- end_to_end_audit.py — Full lifecycle: receipts, verification, crosswalk, evidence export
- fastapi_middleware.py — FastAPI audit middleware pattern
- langchain_callback.py — LangChain callback handler
Performance
Run benchmarks: pytest tests/test_benchmark.py -v -s
Project Stats
Security
See SECURITY.md for the full threat model, vulnerability reporting process, and supported versions.
See CONTRIBUTING.md for architecture invariants, the shared responsibility model, and contribution guidelines.
License
MIT — free for commercial use.
Created and maintained by
S&S Connect — Building trust infrastructure for autonomous AI systems.
- Maintainer: Fabrice (@sundsoffice-tech)
- Repository: github.com/sundsoffice-tech/ai-audit-trail
- PyPI: pypi.org/project/ai-audit-trail
- Issues & Feedback: GitHub Issues
If you use ai-audit-trail in production or research, we'd love to hear about it.
Source: README.md at commit fd04629
Tools
0Version history
1- v0.4.7LatestSep 16, 2026
Score
50% of the applicable checks met
How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.
- Reached in the last 30 days25 pts
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- Auth declared10 pts
Declare the credential headers in the registry entry, or keep the endpoint answering the probe so its auth can be classified.
- Tools listed15 pts
Answer tools/list without a credential, or ship a package that starts in a sandbox without one.
- DescriptionRequired15 pts
- Logo10 pts
- Documentation15 pts
- License10 pts
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