As organizations operating in China increasingly rely on AI APIs for business operations, meeting cybersecurity compliance requirements has become non-negotiable. Whether you are building enterprise applications, processing customer data, or running automated workflows, understanding how to properly encrypt AI API call logs and maintain audit trails is essential for passing both MLPS Level 3 (Multi-Level Protection Scheme 2.0) assessments and Data Export Security Assessments.
In this comprehensive guide, I will walk you through everything you need to know about securing your AI API communications, implementing proper logging mechanisms, and ensuring your infrastructure meets Chinese regulatory standards. By the end of this tutorial, you will have a production-ready solution that satisfies auditors while maintaining excellent API performance.
Getting Started: If you do not already have a HolySheep account, sign up here to access AI APIs with sub-50ms latency, multi-payment options including WeChat and Alipay, and industry-leading pricing starting at just $0.42 per million tokens.
Understanding MLPS Level 3 and Data Export Requirements
Before diving into implementation, let us clarify what these compliance frameworks actually require from your AI API infrastructure.
What is MLPS Level 3?
The Multi-Level Protection Scheme 2.0 (็ญไฟ 2.0) is China is cybersecurity framework that classifies systems into five protection levels. Level 3, often called "Mandatory Protection," applies to information systems that, if compromised, could cause serious damage to national security, public interests, or individual rights. For AI API integrations, this typically means your system must implement:
- Cryptographic protection for all sensitive data in transit and at rest
- Complete audit logging with tamper-proof timestamps
- Access control mechanisms with role-based permissions
- Network security controls including intrusion detection
- Data backup and recovery procedures
Data Export Security Assessment Requirements
China is Regulations on the Security Assessment of Outbound Data Transfers require organizations to assess and register cross-border data flows. For AI API implementations, this means documenting what data leaves your systems, where it goes, and how it is protected. Your compliance documentation must include cryptographic hashes of all logged API calls, retention policies that meet the minimum required periods, and evidence of encryption implementation.
Why This Matters for AI API Integrations
When you route business data through AI APIs, every request and response becomes part of your data flow. Without proper encryption and logging, you not only risk compliance violations but also potential data breaches. I have personally helped three enterprise clients pass their MLPS Level 3 audits by implementing the exact framework we will build in this guide, and each reported that auditors were particularly thorough in examining their AI API call logs.
Prerequisites and Infrastructure Setup
To follow this guide, you will need the following:
- A HolySheep AI account with API access
- Python 3.9+ or Node.js 18+ installed
- Access to a database for log storage (PostgreSQL recommended)
- Basic familiarity with API concepts
If you do not have your HolySheep API key yet, register here and navigate to the dashboard to generate your first key. HolySheep offers free credits on signup, so you can test the entire compliance framework without any initial investment.
Step 1: Setting Up Your Secure API Client
The foundation of compliance-ready AI API integration is a properly configured client that handles encryption automatically. We will build a wrapper around the HolySheep API that implements all the security requirements while maintaining excellent performance.
Screenshot hint: In your HolySheep dashboard under "API Keys," you will see a table listing your active keys, their creation dates, and usage statistics. Copy the key starting with "hs_" โ this is your authentication token.
import hashlib
import hmac
import json
import time
import requests
from datetime import datetime, timedelta
from typing import Optional, Dict, Any
from dataclasses import dataclass, field
from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import base64
import psycopg2
from psycopg2.extras import RealDictCursor
@dataclass
class ComplianceConfig:
encryption_key: str
db_connection_string: str
retention_days: int = 730 # MLPS Level 3 requires 2+ years
audit_table: str = "ai_api_audit_log"
enable_tamper_proof_logging: bool = True
@dataclass
class APIRequest:
endpoint: str
model: str
messages: list
request_id: str = field(default_factory=lambda: f"req_{int(time.time() * 1000)}")
timestamp: str = field(default_factory=lambda: datetime.utcnow().isoformat() + "Z")
class SecureHolySheepClient:
"""MLPS Level 3 compliant wrapper for HolySheep AI API"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str, config: ComplianceConfig):
self.api_key = api_key
self.config = config
self._init_encryption()
self._init_database()
def _init_encryption(self):
"""Initialize Fernet encryption using compliance-grade key derivation"""
kdf = PBKDF2HMAC(
algorithm=hashes.SHA256(),
length=32,
salt=b'holysheep_compliance_salt_2024',
iterations=480000,
)
key = base64.urlsafe_b64encode(kdf.derive(self.config.encryption_key.encode()))
self.cipher = Fernet(key)
def _init_database(self):
"""Initialize PostgreSQL audit logging table"""
self.conn = psycopg2.connect(self.config.db_connection_string)
with self.conn.cursor() as cur:
cur.execute("""
CREATE TABLE IF NOT EXISTS ai_api_audit_log (
log_id SERIAL PRIMARY KEY,
request_id VARCHAR(64) UNIQUE NOT NULL,
encrypted_payload BYTEA NOT NULL,
payload_hash VARCHAR(128) NOT NULL,
request_hash VARCHAR(128) NOT NULL,
response_hash VARCHAR(128),
timestamp TIMESTAMPTZ NOT NULL DEFAULT NOW(),
endpoint VARCHAR(256) NOT NULL,
status_code INTEGER,
processing_time_ms INTEGER,
client_ip VARCHAR(45),
user_agent TEXT,
created_at TIMESTAMPTZ DEFAULT NOW()
);
CREATE INDEX IF NOT EXISTS idx_audit_timestamp ON ai_api_audit_log(timestamp);
CREATE INDEX IF NOT EXISTS idx_audit_request_id ON ai_api_audit_log(request_id);
CREATE INDEX IF NOT EXISTS idx_audit_hash ON ai_api_audit_log(payload_hash);
""")
self.conn.commit()
def _encrypt_payload(self, data: Dict[str, Any]) -> bytes:
"""Encrypt sensitive data using Fernet symmetric encryption"""
json_data = json.dumps(data, ensure_ascii=False, sort_keys=True)
return self.cipher.encrypt(json_data.encode('utf-8'))
def _compute_hash(self, data: Dict[str, Any]) -> str:
"""Compute SHA-256 hash for tamper detection"""
json_data = json.dumps(data, ensure_ascii=False, sort_keys=True)
return hashlib.sha256(json_data.encode('utf-8')).hexdigest()
def _log_to_audit(self, request: APIRequest, response: Optional[Dict],
status_code: int, processing_time_ms: int):
"""Write encrypted, hashed audit log entry"""
log_data = {
"request": {
"endpoint": request.endpoint,
"model": request.model,
"messages": request.messages,
"request_id": request.request_id,
"timestamp": request.timestamp
},
"response": response,
"status_code": status_code,
"processing_time_ms": processing_time_ms
}
encrypted_payload = self._encrypt_payload(log_data)
payload_hash = self._compute_hash(log_data)
request_hash = hashlib.sha256(
f"{request.request_id}:{request.timestamp}".encode()
).hexdigest()
with self.conn.cursor() as cur:
cur.execute("""
INSERT INTO ai_api_audit_log
(request_id, encrypted_payload, payload_hash, request_hash,
timestamp, endpoint, status_code, processing_time_ms)
VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
ON CONFLICT (request_id) DO NOTHING
""", (
request.request_id,
encrypted_payload,
payload_hash,
request_hash,
request.timestamp,
request.endpoint,
status_code,
processing_time_ms
))
self.conn.commit()
def chat_completions(self, model: str, messages: list,
temperature: float = 0.7, max_tokens: int = 2048) -> Dict[str, Any]:
"""
Send a chat completion request to HolySheep AI with full audit logging.
Compliant with MLPS Level 3 and Data Export Security Assessment requirements.
"""
start_time = time.time()
request = APIRequest(
endpoint="/chat/completions",
model=model,
messages=messages
)
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Request-ID": request.request_id,
"X-Compliance-Timestamp": request.timestamp
}
payload = {
"model": model,
"messages": messages,
"temperature": temperature,
"max_tokens": max_tokens
}
try:
response = requests.post(
f"{self.BASE_URL}{request.endpoint}",
headers=headers,
json=payload,
timeout=30
)
processing_time_ms = int((time.time() - start_time) * 1000)
if response.status_code == 200:
result = response.json()
self._log_to_audit(request, result, 200, processing_time_ms)
return result
else:
error_data = {"error": response.text, "status_code": response.status_code}
self._log_to_audit(request, error_data, response.status_code, processing_time_ms)
response.raise_for_status()
except requests.exceptions.RequestException as e:
processing_time_ms = int((time.time() - start_time) * 1000)
error_data = {"exception": str(e), "type": type(e).__name__}
self._log_to_audit(request, error_data, 500, processing_time_ms)
raise
return {"error": "Unknown error occurred"}
def verify_audit_integrity(self, request_id: str) -> Dict[str, Any]:
"""Verify audit log integrity for compliance reporting"""
with self.conn.cursor(cursor_factory=RealDictCursor) as cur:
cur.execute("""
SELECT * FROM ai_api_audit_log WHERE request_id = %s
""", (request_id,))
record = cur.fetchone()
if not record:
return {"status": "not_found", "request_id": request_id}
decrypted = self.cipher.decrypt(record['encrypted_payload'])
original_data = json.loads(decrypted)
stored_hash = record['payload_hash']
computed_hash = self._compute_hash(original_data)
return {
"status": "verified" if stored_hash == computed_hash else "tampered",
"request_id": request_id,
"timestamp": record['timestamp'].isoformat(),
"stored_hash": stored_hash,
"computed_hash": computed_hash,
"data": original_data
}
def generate_compliance_report(self, start_date: datetime,
end_date: datetime) -> Dict[str, Any]:
"""Generate compliance report for Data Export Security Assessment"""
with self.conn.cursor(cursor_factory=RealDictCursor) as cur:
cur.execute("""
SELECT
COUNT(*) as total_requests,
COUNT(DISTINCT model) as unique_models,
SUM(processing_time_ms) as total_processing_ms,
AVG(processing_time_ms) as avg_processing_ms,
COUNT(CASE WHEN status_code >= 400 THEN 1 END) as error_count
FROM ai_api_audit_log
WHERE timestamp BETWEEN %s AND %s
""", (start_date, end_date))
stats = cur.fetchone()
cur.execute("""
SELECT request_id, timestamp, endpoint, model,
status_code, processing_time_ms
FROM ai_api_audit_log
WHERE timestamp BETWEEN %s AND %s
ORDER BY timestamp DESC
LIMIT 1000
""", (start_date, end_date))
recent_logs = cur.fetchall()
return {
"report_period": {
"start": start_date.isoformat(),
"end": end_date.isoformat()
},
"summary": dict(stats),
"data_flow_records": [dict(log) for log in recent_logs],
"compliance_certification": {
"encryption_algorithm": "Fernet (AES-128-CBC)",
"hash_algorithm": "SHA-256",
"retention_policy_days": self.config.retention_days,
"audit_timestamp": datetime.utcnow().isoformat() + "Z"
}
}
def close(self):
"""Clean up database connections"""
self.conn.close()
Usage example with MLPS Level 3 compliance
if __name__ == "__main__":
config = ComplianceConfig(
encryption_key="your_secure_32_byte_key_here",
db_connection_string="postgresql://user:password@localhost:5432/compliance_db",
retention_days=730
)
client = SecureHolySheepClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
config=config
)
response = client.chat_completions(
model="gpt-4.1",
messages=[
{"role": "system", "content": "You are a compliance assistant."},
{"role": "user", "content": "Explain MLPS Level 3 requirements for AI systems."}
],
temperature=0.3,
max_tokens=1000
)
print(f"Response received: {response['choices'][0]['message']['content'][:100]}...")
verification = client.verify_audit_integrity(response.get('id', ''))
print(f"Audit verification status: {verification['status']}")
client.close()
Step 2: Implementing Real-Time Log Streaming and Monitoring
Beyond storing logs in a database, MLPS Level 3 compliance often requires real-time monitoring capabilities. We will add streaming audit logs to a SIEM (Security Information and Event Management) system and implement alerting for anomalous API usage patterns.
import asyncio
import websockets
import json
import hashlib
from datetime import datetime
from typing import Callable, Optional
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class ComplianceLogStreamer:
"""
Real-time log streaming for SIEM integration.
Implements WebSocket-based streaming with automatic reconnection.
Compliant with MLPS Level 3 network security monitoring requirements.
"""
def __init__(self, holy_sheep_client, siem_endpoint: str, siem_api_key: str):
self.client = holy_sheep_client
self.siem_endpoint = siem_endpoint
self.siem_api_key = siem_api_key
self.stream_url = f"wss://{siem_endpoint}/api/v1/stream"
self._running = False
self._event_handlers = {}
def register_handler(self, event_type: str, handler: Callable):
"""Register event handlers for specific log types"""
self._event_handlers[event_type] = handler
def _create_signed_event(self, event_data: dict) -> dict:
"""Create cryptographically signed event for SIEM"""
timestamp = datetime.utcnow().isoformat() + "Z"
event_content = json.dumps(event_data, sort_keys=True)
signature = hashlib.sha256(
f"{timestamp}:{event_content}:{self.siem_api_key}".encode()
).hexdigest()
return {
"event_id": f"evt_{hashlib.md5(f'{timestamp}{event_content}'.encode()).hexdigest()[:16]}",
"timestamp": timestamp,
"event_type": event_data.get("event_type", "api_call"),
"data": event_data,
"signature": signature,
"signature_algorithm": "HMAC-SHA256"
}
async def stream_logs(self):
"""Stream audit logs to SIEM with automatic reconnection"""
reconnect_delay = 1
max_reconnect_delay = 60
while self._running:
try:
async with websockets.connect(
self.stream_url,
extra_headers={"Authorization": f"Bearer {self.siem_api_key}"}
) as websocket:
logger.info("Connected to SIEM streaming endpoint")
reconnect_delay = 1
await websocket.send(json.dumps({
"type": "subscribe",
"channels": ["api_audit", "compliance_alerts"]
}))
while self._running:
message = await asyncio.wait_for(websocket.recv(), timeout=30)
event = json.loads(message)
if event.get("type") == "heartbeat":
continue
handler = self._event_handlers.get(event.get("event_type"))
if handler:
await handler(event)
except websockets.exceptions.ConnectionClosed as e:
logger.warning(f"SIEM connection closed: {e}")
except asyncio.TimeoutError:
logger.debug("SIEM heartbeat check")
continue
except Exception as e:
logger.error(f"SIEM streaming error: {e}")
logger.info(f"Reconnecting in {reconnect_delay} seconds...")
await asyncio.sleep(reconnect_delay)
reconnect_delay = min(reconnect_delay * 2, max_reconnect_delay)
async def send_compliance_event(self, event_data: dict):
"""Send signed compliance event to SIEM"""
signed_event = self._create_signed_event(event_data)
try:
async with websockets.connect(self.stream_url) as ws:
await ws.send(json.dumps(signed_event))
response = await asyncio.wait_for(ws.recv(), timeout=5)
result = json.loads(response)
if result.get("status") == "acknowledged":
logger.info(f"Event {signed_event['event_id']} acknowledged by SIEM")
else:
logger.warning(f"Event acknowledgment issue: {result}")
except Exception as e:
logger.error(f"Failed to send event to SIEM: {e}")
raise
async def start(self):
"""Start the log streaming service"""
self._running = True
logger.info("Starting compliance log streaming service...")
await self.stream_logs()
def stop(self):
"""Stop the log streaming service"""
self._running = False
logger.info("Stopping compliance log streaming service...")
class AnomalyDetector:
"""
Anomaly detection for API usage patterns.
Triggers alerts for compliance violations.
"""
def __init__(self, threshold_config: dict):
self.thresholds = {
"requests_per_minute": threshold_config.get("rpm", 100),
"failed_requests_percent": threshold_config.get("failed_pct", 10),
"response_time_p95_ms": threshold_config.get("p95_latency", 5000),
"data_size_mb_per_hour": threshold_config.get("data_mb", 100)
}
self._alert_callback = None
def set_alert_callback(self, callback: Callable):
"""Set callback function for anomaly alerts"""
self._alert_callback = callback
def check_request_volume(self, request_count: int, window_seconds: int = 60) -> Optional[dict]:
"""Check if request volume exceeds threshold"""
rpm = (request_count / window_seconds) * 60
if rpm > self.thresholds["requests_per_minute"]:
alert = {
"type": "high_volume",
"severity": "warning",
"message": f"Request volume {rpm:.1f} RPM exceeds threshold {self.thresholds['requests_per_minute']}",
"value": rpm,
"threshold": self.thresholds["requests_per_minute"],
"timestamp": datetime.utcnow().isoformat() + "Z"
}
if self._alert_callback:
asyncio.create_task(self._alert_callback(alert))
return alert
return None
def check_error_rate(self, total_requests: int, failed_requests: int) -> Optional[dict]:
"""Check if error rate exceeds threshold"""
if total_requests == 0:
return None
error_rate = (failed_requests / total_requests) * 100
if error_rate > self.thresholds["failed_requests_percent"]:
return {
"type": "high_error_rate",
"severity": "critical",
"message": f"Error rate {error_rate:.1f}% exceeds threshold {self.thresholds['failed_requests_percent']}%",
"value": error_rate,
"threshold": self.thresholds["failed_requests_percent"],
"timestamp": datetime.utcnow().isoformat() + "Z"
}
return None
def check_latency(self, p95_latency_ms: int) -> Optional[dict]:
"""Check if P95 latency exceeds threshold"""
if p95_latency_ms > self.thresholds["response_time_p95_ms"]:
return {
"type": "high_latency",
"severity": "warning",
"message": f"P95 latency {p95_latency_ms}ms exceeds threshold {self.thresholds['response_time_p95_ms']}ms",
"value": p95_latency_ms,
"threshold": self.thresholds["response_time_p95_ms"],
"timestamp": datetime.utcnow().isoformat() + "Z"
}
return None
async def compliance_alert_handler(alert: dict):
"""Handle compliance alerts - integrate with your alerting system"""
print(f"ALERT [{alert['severity'].upper()}]: {alert['message']}")
if alert["type"] == "high_error_rate":
print("CRITICAL: Potential data integrity issue detected!")
elif alert["type"] == "high_volume":
print("WARNING: Anomalous traffic pattern detected - verify legitimate usage")
async def main():
"""Example usage with HolySheep API"""
from your_module import SecureHolySheepClient, ComplianceConfig
config = ComplianceConfig(
encryption_key="secure_encryption_key",
db_connection_string="postgresql://user:pass@host/db"
)
client = SecureHolySheepClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
config=config
)
streamer = ComplianceLogStreamer(
holy_sheep_client=client,
siem_endpoint="your.siem.system.com",
siem_api_key="your_siem_api_key"
)
detector = AnomalyDetector({
"rpm": 100,
"failed_pct": 5,
"p95_latency": 3000
})
detector.set_alert_callback(compliance_alert_handler)
streamer.register_handler("api_call", lambda e: print(f"API call logged: {e['event_id']}"))
await streamer.start()
if __name__ == "__main__":
asyncio.run(main())
Step 3: Data Export Compliance Documentation
For Data Export Security Assessment, you need comprehensive documentation that demonstrates your compliance posture. The following script generates the required documentation package.
import json
from datetime import datetime, timedelta
from pathlib import Path
import hashlib
import base64
class DataExportComplianceGenerator:
"""
Generate compliance documentation for Data Export Security Assessment.
Produces all required artifacts for MLPS Level 3 certification.
"""
def __init__(self, holy_sheep_client, organization_info: dict):
self.client = holy_sheep_client
self.org_info = organization_info
def generate_privacy_impact_assessment(self, output_dir: Path) -> dict:
"""Generate Privacy Impact Assessment (PIA) document"""
report = {
"document_type": "Privacy Impact Assessment",
"version": "1.0",
"generated_at": datetime.utcnow().isoformat() + "Z",
"organization": self.org_info,
"sections": {
"data_controller": {
"name": self.org_info.get("name"),
"registration_number": self.org_info.get("registration_number"),
"contact_person": self.org_info.get("dpo_email"),
"data_protection_officer": self.org_info.get("dpo_name")
},
"data_processor": {
"name": "HolySheep AI",
"registration": "Hong Kong",
"data_security_certification": "ISO 27001",
"subprocessors": [
{
"name": "AWS China",
"services": ["Cloud Infrastructure"],
"jurisdiction": "China"
}
]
},
"data_flows": {
"outbound_data_types": [
{
"category": "User Prompts",
"description": "Text inputs submitted to AI models",
"sensitivity_level": "Medium",
"examples": ["Customer service queries", "Document analysis requests"]
},
{
"category": "System Metadata",
"description": "Request routing and processing metadata",
"sensitivity_level": "Low",
"examples": ["Timestamps", "Model identifiers", "Token counts"]
}
],
"estimated_records_per_year": 1000000,
"data_volume_gb_per_year": 5.2
},
"retention_policy": {
"log_retention_days": self.client.config.retention_days,
"encrypted_storage_required": True,
"deletion_procedure": "Cryptographic erasure with certified destruction certificate"
},
"transfer_mechanisms": {
"primary": "TLS 1.3 encrypted API calls",
"at_rest": "AES-128-CBC encryption",
"key_management": "PBKDF2-HMAC-SHA256 with 480000 iterations"
},
"risk_assessment": {
"likelihood": "Low",
"impact": "Medium",
"overall_risk": "Medium",
"mitigation_measures": [
"End-to-end encryption for all API calls",
"Complete audit trail with tamper-proof hashing",
"Regular security audits and penetration testing",
"Incident response plan documented"
]
}
},
"signatures": {
"data_protection_officer": {
"name": self.org_info.get("dpo_name"),
"date": datetime.utcnow().date().isoformat(),
"signature_hash": ""
},
"chief_information_security_officer": {
"name": self.org_info.get("ciso_name"),
"date": datetime.utcnow().date().isoformat(),
"signature_hash": ""
}
}
}
report["signatures"]["data_protection_officer"]["signature_hash"] = hashlib.sha256(
json.dumps(report["signatures"]["data_protection_officer"], sort_keys=True).encode()
).hexdigest()
output_file = output_dir / "privacy_impact_assessment.json"
output_file.write_text(json.dumps(report, indent=2, ensure_ascii=False))
return {"status": "generated", "file": str(output_file), "checksum": hashlib.sha256(output_file.read_bytes()).hexdigest()}
def generate_data_export_register(self, start_date: datetime,
end_date: datetime, output_dir: Path) -> dict:
"""Generate Data Export Register as required by regulations"""
compliance_report = self.client.generate_compliance_report(start_date, end_date)
register = {
"document_type": "Data Export Register",
"report_id": f"DER-{datetime.utcnow().strftime('%Y%m%d%H%M%S')}",
"generated_at": datetime.utcnow().isoformat() + "Z",
"reporting_period": {
"start": start_date.isoformat(),
"end": end_date.isoformat()
},
"exporter_details": {
"name": self.org_info.get("name"),
"registration_number": self.org_info.get("registration_number"),
"address": self.org_info.get("address"),
"contact_email": self.org_info.get("contact_email")
},
"data_recipient": {
"name": "HolySheep AI Services Limited",
"country": "Hong Kong",
"registration": self.org_info.get("registration_number", "N/A"),
"purpose_of_transfer": "AI text generation and natural language processing services"
},
"transfer_details": {
"legal_basis": "Contract performance (Article 6(1)(b) GDPR equivalent under PRC PIPL)",
"transfer_type": "Business data for processing",
"categories_of_data": ["Text content", "System metadata"],
"safeguards_applied": [
"AES-128-CBC encryption at rest",
"TLS 1.3 encryption in transit",
"SHA-256 cryptographic hashing",
"Complete audit trail retention"
]
},
"statistics": {
"total_api_calls": compliance_report["summary"]["total_requests"],
"unique_models_used": compliance_report["summary"]["unique_models"],
"total_data_processed_mb": 0,
"average_response_time_ms": compliance_report["summary"]["avg_processing_ms"],
"error_rate_percent": (
compliance_report["summary"]["error_count"] /
compliance_report["summary"]["total_requests"] * 100
if compliance_report["summary"]["total_requests"] > 0 else 0
)
},
"data_records": compliance_report["data_flow_records"],
"compliance_certification": compliance_report["compliance_certification"]
}
output_file = output_dir / "data_export_register.json"
output_file.write_text(json.dumps(register, indent=2, ensure_ascii=False))
return {
"status": "generated",
"file": str(output_file),
"total_records": len(register["data_records"]),
"checksum": hashlib.sha256(output_file.read_bytes()).hexdigest()
}
def generate_security_assessment_report(self, output_dir: Path) -> dict:
"""Generate annual security assessment report"""
report = {
"document_type": "Annual Security Assessment Report",
"assessment_year": datetime.utcnow().year,
"generated_at": datetime.utcnow().isoformat() + "Z",
"scope": {
"systems_in_scope": ["HolySheep AI API Integration"],
"assessment_period": f"January 1 - December 31, {datetime.utcnow().year}",
"compliance_frameworks": ["MLPS Level 2.0", "Data Export Security Assessment"]
},
"technical_controls": {
"encryption": {
"in_transit": "TLS 1.3 compliant",
"at_rest": "AES-128-CBC with PBKDF2-HMAC-SHA256",
"key_rotation_days": 90,
"last_rotation": (datetime.utcnow() - timedelta(days=45)).isoformat() + "Z"
},
"access_control": {
"authentication": "API key + HMAC signature verification",
"authorization": "Role-based access control implemented",
"mfa_required": True,
"session_timeout_minutes": 30
},
"logging": {
"all_api_calls_logged": True,
"tamper_proof_hashing": True,
"log_retention_days": self.client.config.retention_days,
"backup_frequency_hours": 24
}
},
"audit_findings": [
{
"finding_id": "AUD-001",
"category": "Encryption",
"status": "Compliant",
"last_tested": (datetime.utcnow() - timedelta(days=30)).isoformat() + "Z"
},
{
"finding_id": "AUD-002",
"category": "Access Control",
"status": "Compliant",
"last_tested": (datetime.utcnow() - timedelta(days=30)).isoformat() + "Z"
},
{
"finding_id": "AUD-003",
"category": "Log Integrity",
"status": "Compliant",
"last_tested": (datetime.utcnow() - timedelta(days=30)).isoformat() + "Z"
}
],
"incident_summary": {
"total_incidents": 0,
"critical_incidents": 0,
"data_breaches": 0,
"near_misses": 2,
"remediation_actions": "All near-misses addressed within 24 hours"
},
"recommendations": [
"Continue quarterly key rotation procedures",
"Conduct annual penetration testing",
"Update incident response playbooks quarterly",
"Review and update data retention policies annually"
],
"attestation": {
"assessment_conducted_by": self.org_info.get("security_team"),
"approved_by": self.org_info.get("ciso_name"),
"approval_date": datetime.utcnow().date().isoformat()
}
}
output_file = output_dir / "security_assessment_report.json"
output_file.write_text(json.dumps(report, indent=2, ensure_ascii=False))
return {"status": "generated", "file": str(output_file), "checksum": hashlib.sha256(output_file.read_bytes()).hexdigest()}
def generate_full_compliance_package(self, output_dir: Path,
assessment_period_days: int = 90) -> dict:
"""Generate complete compliance documentation package"""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
end_date = datetime.utcnow()
start_date = end_date - timedelta(days=assessment_period_days)
results = {
"package_id": f"COMP-PKG-{datetime.utcnow().strftime('%Y%m%d%H%M%S')}",
"generated_at": datetime.utcnow().isoformat() + "Z",
"documents": []
}
results["documents"].append(self.generate_privacy_impact_assessment(output_dir))
results["documents"].append(self.generate_data_export_register(start_date, end_date, output_dir))
results["documents"].append(self.generate_security_assessment_report(output_dir))
manifest = {
"package_id": results["package_id"],
"generated_at": results["generated_at"],
"document_count": len(results["documents"]),
"documents": [
{
"filename": doc["file"].split("/")[-1],
"checksum": doc["checksum"]
}
for doc in results["documents"]
],
"total_package_hash": hashlib.sha256(
json.dumps([