AI Audit Trail

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "ai-audit-trail": {
      "command": "npx",
      "args": [
        "-y",
        "ai-audit-trail"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

ai-audit-trail

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

Terminal

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

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

Quickstart

server.ts

from ai_audit import (
    AuditConfig, init_audit_config,
    ReceiptCollector, ReceiptStore, ReceiptAction,
    verify_chain, get_verify_key_hex,
)

# 1. Configure once at startup
init_audit_config(AuditConfig(is_production=False))
store = ReceiptStore()

# 2. Wrap every AI request
collector = 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-evidence
result = verify_chain(store.get_by_tenant("acme"), get_verify_key_hex())
assert result.valid  # Ed25519 + SHA-256 + hash-chain verified

Architecture Overview

Code

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 proven	How
Input integrity	SHA-256 of NFKC-normalized, PII-stripped input
Output integrity	SHA-256 of generated output
Check results	Ordered check records with scores and thresholds
Decision	Action taken (ALLOW / REJECT / ESCALATE / ...)
Model provenance	Model ID + config digest
Non-repudiation	Ed25519 signature (libsodium)
Ordering	Hash-chain linkage (prev_receipt_hash)

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

Code

Ed25519 signature  →  detects forgery
SHA-256 self-hash  →  detects corruption
Hash-chain link    →  detects insertions / deletions / reordering

PII Redaction (GDPR Art. 17)

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

server.ts

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	`alice@corp.com` becomes
`REDACT`	`[EMAIL]`
`HASH`	`3d4e5f8a...` (deterministic SHA-256)
`MASK`	`a*@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.

server.ts

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.

server.ts

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)   # 0.0–1.0
print(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

server.ts

from ai_audit.report import ComplianceReportGenerator

gen = ComplianceReportGenerator(summary, verify_key_hex=get_verify_key_hex())
gen.to_markdown()   # Documentation portals
gen.to_json()       # Automated pipelines
gen.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.

server.ts

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.

server.ts

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 | FLAGGED
print(state.confidence)  # 0.0–1.0

Drift Detection (Jensen-Shannon Divergence)

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

server.ts

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_DRIFT
print(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.

server.ts

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)

Read the full README →View source on GitHub →

Related MCP Servers

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Frequently Asked Questions about AI Audit Trail

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "ai-audit-trail": { "command": "npx", "args": ["-y", "ai-audit-trail"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

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More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to AI Audit Trail →Install in Claude Desktop Install in Cursor Install in VS Code

AI Audit Trail

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

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to Cursor Add to VS Code

Manual Client & Custom JSON ConfigExpand JSON ▾

Client Config & Setup

Choose your client or environment

Target File:~/Library/Application Support/Claude/claude_desktop_config.json

claude_desktop_config.json

{
  "mcpServers": {
    "ai-audit-trail": {
      "command": "npx",
      "args": [
        "-y",
        "ai-audit-trail"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Documentation Overview

ai-audit-trail

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

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

Installation

Terminal

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

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

Quickstart

server.ts

from ai_audit import (
    AuditConfig, init_audit_config,
    ReceiptCollector, ReceiptStore, ReceiptAction,
    verify_chain, get_verify_key_hex,
)

# 1. Configure once at startup
init_audit_config(AuditConfig(is_production=False))
store = ReceiptStore()

# 2. Wrap every AI request
collector = 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-evidence
result = verify_chain(store.get_by_tenant("acme"), get_verify_key_hex())
assert result.valid  # Ed25519 + SHA-256 + hash-chain verified

Architecture Overview

Code

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 proven	How
Input integrity	SHA-256 of NFKC-normalized, PII-stripped input
Output integrity	SHA-256 of generated output
Check results	Ordered check records with scores and thresholds
Decision	Action taken (ALLOW / REJECT / ESCALATE / ...)
Model provenance	Model ID + config digest
Non-repudiation	Ed25519 signature (libsodium)
Ordering	Hash-chain linkage (prev_receipt_hash)

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

Code

Ed25519 signature  →  detects forgery
SHA-256 self-hash  →  detects corruption
Hash-chain link    →  detects insertions / deletions / reordering

PII Redaction (GDPR Art. 17)

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

server.ts

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	`alice@corp.com` becomes
`REDACT`	`[EMAIL]`
`HASH`	`3d4e5f8a...` (deterministic SHA-256)
`MASK`	`a*@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.

server.ts

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.

server.ts

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)   # 0.0–1.0
print(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

server.ts

from ai_audit.report import ComplianceReportGenerator

gen = ComplianceReportGenerator(summary, verify_key_hex=get_verify_key_hex())
gen.to_markdown()   # Documentation portals
gen.to_json()       # Automated pipelines
gen.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.

server.ts

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.

server.ts

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 | FLAGGED
print(state.confidence)  # 0.0–1.0

Drift Detection (Jensen-Shannon Divergence)

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

server.ts

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_DRIFT
print(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.

server.ts

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)

Read the full README →View source on GitHub →

Related MCP Servers

View all in Developer Tools View all alternatives

Reviews

No reviews yet — be the first to share how this listing worked for you.

Frequently Asked Questions about AI Audit Trail

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "ai-audit-trail": { "command": "npx", "args": ["-y", "ai-audit-trail"] } }

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Technical Specs & Signals

CategoryDeveloper Tools

27Quality signal: Emerging · 27/100How this signal is calculated ▾

Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20

Documentation & tools11/30

Adoption & activity1/15

Community engagement0/10

★ Featured

Moxie Docs MCP

MCP & Agent Skills for Automated Documentation, and codebase conventions + context

Explore Server →

Own this project?

Free dofollow backlink: add your website and place the AllMCPs badge on it — no claim needed. We detect it automatically and keep it verified as long as the badge stays live.

Claim & get free dofollow

Share & Embed

Add our SVG badge (dark/light directory styles) or embeddable widget to your site.

Explore more

More in 💻 Developer Tools →Best MCP servers for Developers →Alternatives to AI Audit Trail →Install in Claude Desktop Install in Cursor Install in VS Code