ai-audit-trail

io.github.sundsoffice-tech由 GitHub 账号 sundsoffice-tech 发布v0.4.7更新于 Oct 27, 2026

Tamper-evident audit trails for AI agents — Ed25519-signed, hash-chained, EU AI Act ready.

发布方自己的服务:audit.domains - Domain Valuation

概览

AI 生成的概览

本地 Python MCP 服务器,提供 ai-audit-trail:为 AI 智能体的决策与工具调用生成 Ed25519 签名、哈希链式防篡改审计凭证。

可以做什么
  • 为 AI 请求创建并封存决策凭证,记录输入、输出、检查结果、执行动作与模型来源
  • 通过签名、自哈希与哈希链校验凭证链和工具调用链的防篡改状态
  • 对个人数据做脱敏或哈希,按租户加密字段,并可销毁密钥实现擦除
  • 将凭证映射到 ISO 42001、NIST AI RMF 与 EU AI Act 控制项,并导出可离线核验的签名证据包
  • 用行为契约、追踪图、来源链、SPRT 认证与漂移检测监控智能体行为
  • 以 Merkle 树批量封存凭证、管理链纪元,并输出 OpenTelemetry 指标
适用场景
适合运行 LLM 智能体或流水线、需要向审计方证明 AI 做了什么、为何如此、记录未被篡改的团队,尤其是需要 EU AI Act、ISO 42001 或 NIST AI RMF 证据的场景。也适合希望自托管、可离线核验审计记录而非依赖 SaaS 日志平台的情况。
运行要求
作为本地 stdio 进程运行,通过 PyPI 包 ai-audit-trail 提供的 ai-audit-mcp 命令启动。需要 Python 3.11 或更高版本;未声明任何外部服务、账号、API 密钥或环境变量。可选扩展提供 Redis 持久化与 OpenTelemetry。生产环境通常需要持久化的 Ed25519 签名密钥,一般通过 KMS 或 Vault 密钥提供器注入。
安装前请注意
签名密钥是信任根:持有者可伪造凭证,应存放在 KMS 或密钥库中,而非代码或配置里。加密擦除会不可逆地销毁密钥。该库本身不保证合规,README 将密钥存储、PII 配置、持久存储、访问控制、人工监督与时钟同步列为部署方责任。凭证会记录提示词、输出与工具参数,记录前请确认 PII 设置。

安装

在 SourceWeft 中

  1. 打开 控制台中的 ai-audit-trail,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

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.

来源:README.md,提交 fd04629

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  1. v0.4.7最新Sep 16, 2026

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