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

AI-generated 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.
Before you install
The signing key is the root of trust: anyone holding it can forge receipts, so store it in a KMS or vault rather than in code or config. Crypto-shredding destroys encryption keys irreversibly. The library alone does not guarantee regulatory compliance; the README assigns key storage, PII configuration, durable storage, access control, human oversight and clock synchronization to the deployer. Receipts record prompts, outputs and tool arguments, so review PII settings before logging.

Installation

In SourceWeft

  1. Open ai-audit-trail in the dashboard and add it to a workspace.
  2. 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 install and 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

bash
pip install ai-audit-trail                    # Core (Ed25519 + SHA-256 + PII)pip install "ai-audit-trail[redis]"           # + Redis persistencepip install "ai-audit-trail[otel]"            # + OpenTelemetry metricspip install "ai-audit-trail[all]"             # Everything

Requirements: Python 3.11+ | No external services required | Works air-gapped


Quickstart

python
from ai_audit import (    AuditConfig, init_audit_config,    ReceiptCollector, ReceiptStore, ReceiptAction,    verify_chain, get_verify_key_hex,)
# 1. Configure once at startupinit_audit_config(AuditConfig(is_production=False))store = ReceiptStore()
# 2. Wrap every AI requestcollector = ReceiptCollector(trace_id="req-1", tenant_id="acme")collector.set_input("What is our GDPR policy?")collector.add_check("safety", score=0.02, threshold=0.8, fired=False)collector.set_output("Our GDPR policy states that...")collector.set_action(ReceiptAction.ALLOW)collector.emit(store)collector.cleanup()
# 3. Verify tamper-evidenceresult = verify_chain(store.get_by_tenant("acme"), get_verify_key_hex())assert result.valid  # Ed25519 + SHA-256 + hash-chain verified

Architecture Overview

Receipt Creation        Verification & Compliance       Agentic AI Audit─────────────────       ──────────────────────────      ──────────────────ReceiptCollector   ──>  verify_chain()                  ToolCallReceipt  set_input()           build_compliance_summary()      TraceGraph (DAG)  add_check()           build_crosswalk()               BehavioralContract  set_output()          export_evidence_package()       ProvenanceChain  set_action()          SPRTMonitor  emit()                DriftMonitor       │                EpochManager       v  ReceiptStore     ──>  StorageBackend ABC  (in-memory LRU)       InMemoryBackend  + Redis (optional)    (your custom backends)  + AuditBuffer

Core Features

Decision Receipts (Ed25519 + SHA-256 + Hash-Chain)

Every AI pipeline decision produces a Decision Receipt — a cryptographically sealed, hash-chained record:

What's provenHow
Input integritySHA-256 of NFKC-normalized, PII-stripped input
Output integritySHA-256 of generated output
Check resultsOrdered check records with scores and thresholds
DecisionAction taken (ALLOW / REJECT / ESCALATE / ...)
Model provenanceModel ID + config digest
Non-repudiationEd25519 signature (libsodium)
OrderingHash-chain linkage (prev_receipt_hash)

Three-stage verification (< 0.1 ms per receipt):

Ed25519 signature  →  detects forgerySHA-256 self-hash  →  detects corruptionHash-chain link    →  detects insertions / deletions / reordering

PII Redaction (GDPR Art. 17)

Personal data is stripped before hashing — the audit log never contains raw PII.

python
from ai_audit import PiiConfig, PiiMode, PiiType
config = PiiConfig(    enabled_types=frozenset({PiiType.EMAIL, PiiType.PHONE, PiiType.IP}),    mode=PiiMode.REDACT,  # or HASH (SHA-256) or MASK (a***m))collector = ReceiptCollector(tenant_id="acme", pii_config=config)
Mode[email protected] becomes
REDACT[EMAIL]
HASH3d4e5f8a... (deterministic SHA-256)
MASKa***@c***.com

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.

python
from ai_audit.shredding import AESGCMDEKStore, encrypt_field, shred_tenant
dek_store = AESGCMDEKStore()dek_store.create_dek("tenant-acme")
field = encrypt_field("sensitive PII", dek_store, "tenant-acme")shred_tenant("tenant-acme", dek_store)  # Key destroyed — data unrecoverable# Hash-chain remains mathematically intact (hashes ciphertext, not plaintext)

Compliance & Governance

ISO 42001 / NIST AI RMF Crosswalk

Maps receipt data directly to recognized management controls with evidence pointers.

python
from ai_audit.crosswalk import build_crosswalk, nist_function_map
crosswalk = build_crosswalk(receipts, chain_intact=True)for entry in crosswalk:    print(f"[{entry.status}] {entry.framework} {entry.control_id} — {entry.control_name}")
nist = nist_function_map(receipts)print(nist["GOVERN"].coverage)   # 0.0–1.0print(nist["MEASURE"].status)    # PASS / PARTIAL / FAIL

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

python
from ai_audit.report import ComplianceReportGenerator
gen = ComplianceReportGenerator(summary, verify_key_hex=get_verify_key_hex())gen.to_markdown()   # Documentation portalsgen.to_json()       # Automated pipelinesgen.to_html()       # Air-gapped servers

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.

python
from ai_audit.export import export_evidence_package, verify_evidence_package
export_evidence_package(receipts, verify_key_hex, signing_key, "audit_2026.zip")# Bundle: receipts.jsonl + chain_metadata.json + public_key.hex + manifest.json (signed) + verify.py
# Auditor verifies offline:# python -m ai_audit verify audit_2026.zip

Continuous Certification (SPRT)

Sequential Probability Ratio Test — live compliance status per tenant.

python
from ai_audit.sprt import SPRTMonitor
monitor = SPRTMonitor(tenant_id="acme")for receipt in receipts:    state = monitor.update(is_reject=(receipt.action == "reject"))print(state.status)      # CERTIFIED | MONITORING | FLAGGEDprint(state.confidence)  # 0.0–1.0

Drift Detection (Jensen-Shannon Divergence)

Detects behavioral shifts in real-time — pure Python, no scipy required.

python
from ai_audit.drift import DriftMonitor
monitor = DriftMonitor(window_size=100)for receipt in receipts:    state = monitor.update(receipt.action.value)print(state.status)       # STABLE | DRIFTING | CRITICAL_DRIFTprint(state.drift_score)  # JSD 0.0–1.0

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.

python
from ai_audit.contracts import BehavioralContract, Constraint, ContractMonitor
contract = BehavioralContract(    contract_id="safety-v1",    constraints=[        Constraint(name="no_pii_leak", kind="hard", field="action", operator="!=", value="reject"),        Constraint(name="quality", kind="soft", field="checks.safety.score", operator="<=", value=0.1, delta=0.5),    ],)monitor = ContractMonitor(contract)for receipt in receipts:    state = monitor.evaluate(receipt)
print(state.p)                   # Hard constraint satisfaction probabilityprint(state.delta)               # Maximum soft deviation observedprint(state.k)                   # Recovery steps after last violationprint(state.reliability_index)   # Theta: single compliance metric (0.0–1.0)print(state.status)              # COMPLIANT | DEGRADED | VIOLATED

Cryptographic Tool-Call Receipts

Every agent API call Ed25519-signed — no existing framework provides this.

python
from ai_audit.toolcall import seal_tool_call, verify_tool_call_chain
receipt = seal_tool_call(    agent_id="researcher",    tool_name="web_search",    tool_args={"query": "EU AI Act compliance"},    tool_result="Found 5 relevant documents...",    private_key=signing_key,    tenant_id="acme",)assert receipt.verify(signing_key.verify_key)

Multi-Agent Trace-Graphs (DAG)

Audit delegation, handoff, and parallel orchestration — not just linear logs.

python
from ai_audit.tracegraph import TraceGraph
graph = TraceGraph(trace_id="workflow-1", tenant_id="acme")root = graph.add_node(agent_id="orchestrator", action="plan")graph.add_node(agent_id="researcher", action="search", parent_id=root.node_id)graph.add_node(agent_id="writer", action="draft", parent_id=root.node_id)
assert graph.verify_integrity()  # Hash-based tamper detectionassert not graph.has_cycles()    # DAG validationlineage = graph.get_agent_lineage(leaf_node.node_id)  # Root-to-leaf trace

Epistemische Integritat / Unforgeable Provenance

Track WHERE every piece of information came from — proves a decision was not influenced by prompt injection.

python
from ai_audit.provenance import ProvenanceChain, ProvenanceRecord, SourceType
chain = ProvenanceChain(receipt_id="r1", tenant_id="acme")chain.add(ProvenanceRecord(source_type=SourceType.SYSTEM, source_id="prompt", trust_level=1.0, content_hash="..."))chain.add(ProvenanceRecord(source_type=SourceType.DOCUMENT, source_id="doc-123", trust_level=0.8, content_hash="..."))chain.add(ProvenanceRecord(source_type=SourceType.UNKNOWN, source_id="???", trust_level=0.0, content_hash="..."))
summary = chain.trust_summary()print(summary.system_grounded)       # True — has SYSTEM sourceprint(summary.potentially_injected)  # True — has UNKNOWN sourceprint(summary.min_trust)             # 0.0 — weakest link

High-Throughput Architecture

Merkle-Tree Batch Sealing (RFC 6962)

Chain-of-Roots instead of chain-of-receipts — O(log N) verification per batch.

python
from ai_audit.batch import MerkleBatcher
batcher = MerkleBatcher(tenant_id="acme", private_key=key, max_batch_size=2048)for receipt in receipts:    seal = batcher.add(receipt.receipt_id, receipt.seal_payload())    if seal:  # Auto-flushed at 2048 receipts        print(f"Batch sealed: {seal.merkle_root[:16]}...")
assert batcher.verify_chain_of_roots(key.verify_key)

Chain Epochs / Rollover

Prevent unbounded chain growth. Old epochs can be archived or deleted.

python
from ai_audit.epochs import EpochManager
mgr = EpochManager(tenant_id="acme", private_key=key, max_epoch_size=10_000)for receipt in receipts:    seal = mgr.add_receipt(receipt)  # Auto-seals at 10kmgr.seal_epoch()                     # Or explicit rolloverassert mgr.verify_epoch_chain(key.verify_key)

Ring-Buffer with Backpressure

Bounded buffer for high-throughput ingestion — fail-closed, no silent data loss.

python
from ai_audit.buffer import AuditBuffer, AuditBufferFullError
buffer = AuditBuffer(maxsize=50_000)  # ~5 seconds at 10k req/stry:    buffer.put(receipt)except AuditBufferFullError:    # Backpressure — reject the request rather than lose audit data    passbatch = buffer.drain(max_items=2048)

Storage Backend ABCs

Pluggable persistence — bring your own database.

python
from ai_audit.storage import StorageBackend, InMemoryBackend
# Use the reference implementation for dev/testbackend = InMemoryBackend(max_receipts=50_000)
# Or implement your own:class PostgresBackend(StorageBackend):    def write_receipt(self, receipt): ...    def read_receipt(self, receipt_id): ...    def query_by_tenant(self, tenant_id, limit=100): ...    def healthcheck(self) -> bool: ...

OpenTelemetry Instrumentation

Native metrics for SRE dashboards — graceful no-op without OTel SDK.

python
# pip install "ai-audit-trail[otel]"from ai_audit.telemetry import record_seal, record_append, record_drift
record_seal(duration_seconds=0.000045, tenant_id="acme")record_append(tenant_id="acme", async_mode=True)record_drift(score=0.03, tenant_id="acme")

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

bash
python -c "import nacl.signing; print(nacl.signing.SigningKey.generate().encode().hex())"
python
init_audit_config(AuditConfig(is_production=True, signing_key_hex="your-64-char-hex-key"))

KMS Integration

python
from ai_audit import KeyProvider, init_key_provider
class VaultKeyProvider(KeyProvider):    def get_signing_key(self) -> nacl.signing.SigningKey:        secret = vault_client.secrets.kv.read_secret("secret/ai-audit/key")        return nacl.signing.SigningKey(bytes.fromhex(secret["data"]["key"]))    def get_verify_key_hex(self) -> str:        return self.get_signing_key().verify_key.encode().hex()
init_key_provider(VaultKeyProvider())

Redis Persistence

python
import redisstore = ReceiptStore(redis_client=redis.Redis(), use_lua=True)  # Lua mode: 10k+ req/s

Shared Responsibility Model

ResponsibilityLibraryDeployer
Ed25519 + SHA-256 signing and hashingX
Hash-chain integrityX
PII redaction (REDACT/HASH/MASK)X
Merkle-Tree batch sealing (RFC 6962)X
SPRT compliance certificationX
ISO 42001 / NIST AI RMF mappingX
Evidence Package export + verificationX
Crypto-Shredding (AES-256-GCM)X
Agent Behavioral ContractsX
OpenTelemetry metricsX
Secure key storage (HSM/Vault)X
PII type configurationX
Durable storage backendX
Access controls / RBACX
Human oversight (EU AI Act Art. 14)X
Clock synchronization (NTP)X
Incident responseX
Regulatory compliance certificationX

Examples

See the examples/ directory:


Performance

OperationTypical LatencyNotes
seal() (hash + sign)< 100 usEd25519 via libsodium C
verify_chain(1000)< 50 msScales linearly
merkle_root(2048)< 5 msRFC 6962 SHA-256
Memory per receipt~1 KBPydantic V2 + orjson

Run benchmarks: pytest tests/test_benchmark.py -v -s


Project Stats

MetricValue
Tests196
Source modules26
__all__ exports60
Type checkingmypy --strict, 0 errors
Lintingruff, 0 errors
Python versions3.11, 3.12, 3.13
Security scans3 completed, 6 fixes applied
NB validators consulted5 (Architecture, Enterprise, Performance, Agentic, Branding)

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.

If you use ai-audit-trail in production or research, we'd love to hear about it.

Source: README.md at commit fd04629

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  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

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • 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

Other Audit integrations