Introduction
Economic data security represents one of the most critical aspects of financial technology infrastructure due to the sensitive nature of economic indicators, their potential market impact, and the strict regulatory requirements governing their protection. Unlike typical business data security that focuses primarily on preventing unauthorized access, economic data security must address market manipulation risks, insider trading concerns, and the complex international regulations that govern cross-border economic data sharing.
The challenge lies in balancing robust security controls with the analytical accessibility required for effective economic research and policy making. Economic data systems must provide real-time access to authorized analysts while maintaining comprehensive audit trails, implementing dynamic access controls based on data sensitivity and market conditions, and ensuring compliance with evolving privacy regulations across multiple jurisdictions.
Privacy requirements for economic data extend beyond traditional personal data protection to include institutional privacy, market-sensitive information protection, and the unique challenges of protecting aggregated data that might reveal sensitive economic patterns. The privacy framework must address both explicit privacy regulations like GDPR and the implicit privacy requirements that come from handling competitively sensitive economic information.
This guide builds upon the governance foundations from Economic Data Governance and Compliance and provides the security implementation that supports the container orchestration patterns discussed in Container Orchestration for Economic Data Systems. The security controls presented here integrate with the monitoring capabilities from Economic Indicator Alerting and Monitoring Systems.
Data Classification and Protection Levels
Economic data security requires sophisticated classification schemes that account for multiple dimensions of sensitivity including regulatory requirements, market impact potential, competitive sensitivity, and temporal factors. The classification system must dynamically adjust protection levels based on changing market conditions, data aggregation levels, and the specific context in which data is being accessed.
The foundational classification framework distinguishes between different types of economic sensitivity: market-moving indicators that could affect trading decisions, competitive intelligence that provides strategic advantages, regulatory data that must meet specific compliance requirements, and personal economic data that falls under privacy protection laws. Each classification level requires distinct security controls and access procedures.
Temporal sensitivity adds complexity to economic data classification because the same data might have different sensitivity levels at different times. Preliminary economic indicators might be highly sensitive before official release but become public information afterward. The security system must automatically adjust protection levels based on release schedules, market hours, and other temporal factors.
from enum import Enum
from typing import Dict, List, Any, Optional, Set, Union
from dataclasses import dataclass, field
from datetime import datetime, timedelta
import hashlib
import json
import logging
from abc import ABC, abstractmethod
import cryptography
from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import rsa, padding
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
import secrets
import base64
class DataClassification(Enum):
PUBLIC = "public"
INTERNAL = "internal"
CONFIDENTIAL = "confidential"
RESTRICTED = "restricted"
TOP_SECRET = "top_secret"
class SecurityLevel(Enum):
BASIC = "basic"
ENHANCED = "enhanced"
HIGH = "high"
MAXIMUM = "maximum"
class AccessContext(Enum):
RESEARCH = "research"
TRADING = "trading"
REGULATORY = "regulatory"
AUDIT = "audit"
EMERGENCY = "emergency"
@dataclass
class SecurityPolicy:
"""Security policy for economic data"""
policy_id: str
classification: DataClassification
security_level: SecurityLevel
encryption_required: bool
access_logging: bool
data_masking: bool
geographic_restrictions: List[str]
time_restrictions: Optional[Dict[str, Any]] = None
access_approval_required: bool = False
multi_factor_auth_required: bool = True
background_check_required: bool = False
information_barriers: List[str] = field(default_factory=list)
@dataclass
class DataElement:
"""Individual data element with security metadata"""
element_id: str
content: Union[str, bytes, Dict[str, Any]]
classification: DataClassification
sensitivity_tags: Set[str]
created_at: datetime
expires_at: Optional[datetime] = None
owner: str = ""
source_system: str = ""
encryption_key_id: Optional[str] = None
access_history: List[Dict[str, Any]] = field(default_factory=list)
class EconomicDataSecurityFramework:
"""Comprehensive security framework for economic data"""
def __init__(self):
self.security_policies = {}
self.encryption_manager = EncryptionManager()
self.access_control = AccessControlSystem()
self.privacy_engine = PrivacyProtectionEngine()
self.audit_logger = SecurityAuditLogger()
self.threat_detector = ThreatDetectionSystem()
def register_security_policy(self, policy: SecurityPolicy):
"""Register a security policy for data classification"""
self.security_policies[policy.classification] = policy
logging.info(f"Registered security policy for {policy.classification.value}")
def secure_data_element(self, element: DataElement) -> DataElement:
"""Apply security controls to data element"""
policy = self.security_policies.get(element.classification)
if not policy:
raise ValueError(f"No security policy found for classification {element.classification}")
secured_element = element
# Apply encryption if required
if policy.encryption_required:
secured_element = self.encryption_manager.encrypt_element(secured_element)
# Apply data masking if required
if policy.data_masking:
secured_element = self.privacy_engine.apply_data_masking(secured_element)
# Log security application
self.audit_logger.log_security_event({
'event_type': 'data_secured',
'element_id': element.element_id,
'classification': element.classification.value,
'security_controls_applied': {
'encryption': policy.encryption_required,
'masking': policy.data_masking,
'access_logging': policy.access_logging
},
'timestamp': datetime.utcnow()
})
return secured_element
def authorize_data_access(self, user_id: str, element_id: str,
access_context: AccessContext) -> Dict[str, Any]:
"""Authorize access to secured data element"""
# Get data element
element = self._get_data_element(element_id)
if not element:
return {'authorized': False, 'reason': 'Element not found'}
# Get security policy
policy = self.security_policies.get(element.classification)
if not policy:
return {'authorized': False, 'reason': 'No security policy found'}
# Check access controls
access_result = self.access_control.check_access(
user_id, element, access_context, policy
)
if not access_result['authorized']:
# Log access denial
self.audit_logger.log_security_event({
'event_type': 'access_denied',
'user_id': user_id,
'element_id': element_id,
'classification': element.classification.value,
'reason': access_result['reason'],
'timestamp': datetime.utcnow()
})
return access_result
# Check for suspicious access patterns
threat_assessment = self.threat_detector.assess_access_request(
user_id, element, access_context
)
if threat_assessment['risk_level'] == 'high':
# Require additional authorization for high-risk access
additional_auth_result = self._require_additional_authorization(
user_id, element_id, threat_assessment
)
if not additional_auth_result['authorized']:
return additional_auth_result
# Log successful access authorization
self.audit_logger.log_security_event({
'event_type': 'access_authorized',
'user_id': user_id,
'element_id': element_id,
'classification': element.classification.value,
'access_context': access_context.value,
'risk_level': threat_assessment['risk_level'],
'timestamp': datetime.utcnow()
})
return {
'authorized': True,
'access_token': self._generate_access_token(user_id, element_id),
'restrictions': access_result.get('restrictions', []),
'expires_at': datetime.utcnow() + timedelta(hours=1)
}
def retrieve_secured_data(self, access_token: str) -> Dict[str, Any]:
"""Retrieve data using authorized access token"""
# Validate access token
token_validation = self._validate_access_token(access_token)
if not token_validation['valid']:
return {'success': False, 'reason': 'Invalid access token'}
user_id = token_validation['user_id']
element_id = token_validation['element_id']
# Get secured element
element = self._get_data_element(element_id)
if not element:
return {'success': False, 'reason': 'Element not found'}
# Decrypt if necessary
if element.encryption_key_id:
decrypted_element = self.encryption_manager.decrypt_element(element)
else:
decrypted_element = element
# Apply privacy protection based on user context
protected_data = self.privacy_engine.apply_privacy_protection(
decrypted_element, user_id
)
# Log data access
self.audit_logger.log_security_event({
'event_type': 'data_accessed',
'user_id': user_id,
'element_id': element_id,
'classification': element.classification.value,
'access_token': access_token[:8] + '...', # Partial token for audit
'timestamp': datetime.utcnow()
})
# Update access history
element.access_history.append({
'user_id': user_id,
'accessed_at': datetime.utcnow(),
'access_token': access_token
})
return {
'success': True,
'data': protected_data,
'classification': element.classification.value,
'restrictions': token_validation.get('restrictions', [])
}
def _get_data_element(self, element_id: str) -> Optional[DataElement]:
"""Get data element by ID (mock implementation)"""
# In production, this would query the actual data store
return DataElement(
element_id=element_id,
content={'sample': 'economic data'},
classification=DataClassification.CONFIDENTIAL,
sensitivity_tags={'market-moving', 'preliminary'},
created_at=datetime.utcnow(),
owner='economic-data-system'
)
def _require_additional_authorization(self, user_id: str, element_id: str,
threat_assessment: Dict[str, Any]) -> Dict[str, Any]:
"""Require additional authorization for high-risk access"""
# In production, this would integrate with approval workflows
return {
'authorized': True,
'additional_auth_method': 'supervisor_approval',
'auth_reference': 'AUTH-12345'
}
def _generate_access_token(self, user_id: str, element_id: str) -> str:
"""Generate secure access token"""
token_data = {
'user_id': user_id,
'element_id': element_id,
'issued_at': datetime.utcnow().isoformat(),
'nonce': secrets.token_hex(16)
}
# In production, this would use proper JWT or similar token format
token_string = json.dumps(token_data)
return base64.b64encode(token_string.encode()).decode()
def _validate_access_token(self, access_token: str) -> Dict[str, Any]:
"""Validate access token"""
try:
token_string = base64.b64decode(access_token.encode()).decode()
token_data = json.loads(token_string)
# Check token expiration (1 hour)
issued_at = datetime.fromisoformat(token_data['issued_at'])
if datetime.utcnow() - issued_at > timedelta(hours=1):
return {'valid': False, 'reason': 'Token expired'}
return {
'valid': True,
'user_id': token_data['user_id'],
'element_id': token_data['element_id']
}
except Exception as e:
return {'valid': False, 'reason': f'Token validation failed: {e}'}
class EncryptionManager:
"""Manages encryption for economic data"""
def __init__(self):
self.encryption_keys = {}
self.key_rotation_schedule = {}
self._initialize_master_keys()
def _initialize_master_keys(self):
"""Initialize master encryption keys"""
# Generate master key for symmetric encryption
self.master_key = Fernet.generate_key()
self.symmetric_cipher = Fernet(self.master_key)
# Generate RSA key pair for asymmetric encryption
self.private_key = rsa.generate_private_key(
public_exponent=65537,
key_size=4096
)
self.public_key = self.private_key.public_key()
def encrypt_element(self, element: DataElement) -> DataElement:
"""Encrypt data element based on classification"""
if element.classification in [DataClassification.RESTRICTED, DataClassification.TOP_SECRET]:
# Use asymmetric encryption for highest security
encrypted_content = self._encrypt_asymmetric(element.content)
encryption_method = 'rsa-4096'
else:
# Use symmetric encryption for other classifications
encrypted_content = self._encrypt_symmetric(element.content)
encryption_method = 'aes-256-gcm'
# Generate key ID for tracking
key_id = hashlib.sha256(f"{element.element_id}{datetime.utcnow()}".encode()).hexdigest()[:16]
# Store encryption metadata
self.encryption_keys[key_id] = {
'element_id': element.element_id,
'encryption_method': encryption_method,
'created_at': datetime.utcnow(),
'classification': element.classification.value
}
# Create encrypted element
encrypted_element = DataElement(
element_id=element.element_id,
content=encrypted_content,
classification=element.classification,
sensitivity_tags=element.sensitivity_tags,
created_at=element.created_at,
expires_at=element.expires_at,
owner=element.owner,
source_system=element.source_system,
encryption_key_id=key_id,
access_history=element.access_history
)
return encrypted_element
def decrypt_element(self, element: DataElement) -> DataElement:
"""Decrypt data element"""
if not element.encryption_key_id:
return element # Not encrypted
key_info = self.encryption_keys.get(element.encryption_key_id)
if not key_info:
raise ValueError(f"Encryption key not found: {element.encryption_key_id}")
if key_info['encryption_method'] == 'rsa-4096':
decrypted_content = self._decrypt_asymmetric(element.content)
elif key_info['encryption_method'] == 'aes-256-gcm':
decrypted_content = self._decrypt_symmetric(element.content)
else:
raise ValueError(f"Unknown encryption method: {key_info['encryption_method']}")
# Create decrypted element
decrypted_element = DataElement(
element_id=element.element_id,
content=decrypted_content,
classification=element.classification,
sensitivity_tags=element.sensitivity_tags,
created_at=element.created_at,
expires_at=element.expires_at,
owner=element.owner,
source_system=element.source_system,
encryption_key_id=None, # Remove encryption metadata
access_history=element.access_history
)
return decrypted_element
def _encrypt_symmetric(self, content: Any) -> bytes:
"""Encrypt content using symmetric encryption"""
content_bytes = json.dumps(content).encode() if not isinstance(content, bytes) else content
return self.symmetric_cipher.encrypt(content_bytes)
def _decrypt_symmetric(self, encrypted_content: bytes) -> Any:
"""Decrypt content using symmetric encryption"""
decrypted_bytes = self.symmetric_cipher.decrypt(encrypted_content)
try:
return json.loads(decrypted_bytes.decode())
except json.JSONDecodeError:
return decrypted_bytes
def _encrypt_asymmetric(self, content: Any) -> bytes:
"""Encrypt content using asymmetric encryption"""
content_bytes = json.dumps(content).encode() if not isinstance(content, bytes) else content
# RSA has size limitations, so use hybrid encryption for large content
if len(content_bytes) > 446: # RSA-4096 can encrypt up to 446 bytes
# Generate random AES key
aes_key = secrets.token_bytes(32)
# Encrypt content with AES
cipher = Cipher(algorithms.AES(aes_key), modes.GCM(secrets.token_bytes(12)))
encryptor = cipher.encryptor()
ciphertext = encryptor.update(content_bytes) + encryptor.finalize()
# Encrypt AES key with RSA
encrypted_aes_key = self.public_key.encrypt(
aes_key,
padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA256()),
algorithm=hashes.SHA256(),
label=None
)
)
# Combine encrypted key and content
return encrypted_aes_key + encryptor.tag + ciphertext
else:
# Direct RSA encryption for small content
return self.public_key.encrypt(
content_bytes,
padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA256()),
algorithm=hashes.SHA256(),
label=None
)
)
def _decrypt_asymmetric(self, encrypted_content: bytes) -> Any:
"""Decrypt content using asymmetric encryption"""
try:
# Try direct RSA decryption first
decrypted_bytes = self.private_key.decrypt(
encrypted_content,
padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA256()),
algorithm=hashes.SHA256(),
label=None
)
)
return json.loads(decrypted_bytes.decode())
except Exception:
# Try hybrid decryption
# Extract encrypted AES key (first 512 bytes for RSA-4096)
encrypted_aes_key = encrypted_content[:512]
tag = encrypted_content[512:528] # GCM tag is 16 bytes
ciphertext = encrypted_content[528:]
# Decrypt AES key with RSA
aes_key = self.private_key.decrypt(
encrypted_aes_key,
padding.OAEP(
mgf=padding.MGF1(algorithm=hashes.SHA256()),
algorithm=hashes.SHA256(),
label=None
)
)
# Decrypt content with AES
cipher = Cipher(algorithms.AES(aes_key), modes.GCM(secrets.token_bytes(12), tag))
decryptor = cipher.decryptor()
decrypted_bytes = decryptor.update(ciphertext) + decryptor.finalize()
return json.loads(decrypted_bytes.decode())
def rotate_encryption_keys(self):
"""Rotate encryption keys for enhanced security"""
# Store old keys for decryption of existing data
old_master_key = self.master_key
old_private_key = self.private_key
# Generate new keys
self.master_key = Fernet.generate_key()
self.symmetric_cipher = Fernet(self.master_key)
self.private_key = rsa.generate_private_key(
public_exponent=65537,
key_size=4096
)
self.public_key = self.private_key.public_key()
# Schedule re-encryption of existing data
self.key_rotation_schedule[datetime.utcnow()] = {
'old_master_key': old_master_key,
'old_private_key': old_private_key,
'rotation_reason': 'scheduled_rotation'
}
logging.info("Encryption keys rotated successfully")
class AccessControlSystem:
"""Advanced access control for economic data"""
def __init__(self):
self.access_policies = {}
self.user_attributes = {}
self.information_barriers = {}
self.temporal_restrictions = {}
def check_access(self, user_id: str, element: DataElement,
context: AccessContext, policy: SecurityPolicy) -> Dict[str, Any]:
"""Check if user has access to data element"""
# Check basic authorization
basic_auth = self._check_basic_authorization(user_id, element, context)
if not basic_auth['authorized']:
return basic_auth
# Check information barriers
barrier_check = self._check_information_barriers(user_id, element, policy)
if not barrier_check['authorized']:
return barrier_check
# Check temporal restrictions
temporal_check = self._check_temporal_restrictions(user_id, element, policy)
if not temporal_check['authorized']:
return temporal_check
# Check geographic restrictions
geo_check = self._check_geographic_restrictions(user_id, element, policy)
if not geo_check['authorized']:
return geo_check
# Determine access restrictions
restrictions = self._determine_access_restrictions(user_id, element, context, policy)
return {
'authorized': True,
'restrictions': restrictions,
'access_level': self._determine_access_level(user_id, element, context)
}
def _check_basic_authorization(self, user_id: str, element: DataElement,
context: AccessContext) -> Dict[str, Any]:
"""Check basic user authorization"""
user_attrs = self.user_attributes.get(user_id, {})
# Check user clearance level
user_clearance = user_attrs.get('clearance_level', 'public')
required_clearance = self._map_classification_to_clearance(element.classification)
if not self._clearance_sufficient(user_clearance, required_clearance):
return {
'authorized': False,
'reason': f"Insufficient clearance: {user_clearance} < {required_clearance}"
}
# Check context-specific permissions
user_contexts = user_attrs.get('authorized_contexts', [])
if context.value not in user_contexts:
return {
'authorized': False,
'reason': f"User not authorized for context: {context.value}"
}
return {'authorized': True}
def _check_information_barriers(self, user_id: str, element: DataElement,
policy: SecurityPolicy) -> Dict[str, Any]:
"""Check information barrier restrictions"""
if not policy.information_barriers:
return {'authorized': True}
user_attrs = self.user_attributes.get(user_id, {})
user_barriers = user_attrs.get('information_barriers', [])
# Check if user is subject to any barriers that would prevent access
for barrier in policy.information_barriers:
if barrier in user_barriers:
# Check if this specific data conflicts with user's barrier
if self._barrier_blocks_access(barrier, element):
return {
'authorized': False,
'reason': f"Information barrier violation: {barrier}"
}
return {'authorized': True}
def _check_temporal_restrictions(self, user_id: str, element: DataElement,
policy: SecurityPolicy) -> Dict[str, Any]:
"""Check temporal access restrictions"""
if not policy.time_restrictions:
return {'authorized': True}
current_time = datetime.utcnow()
restrictions = policy.time_restrictions
# Check market hours restriction
if 'market_hours_only' in restrictions and restrictions['market_hours_only']:
if not self._is_market_hours(current_time):
return {
'authorized': False,
'reason': 'Access restricted to market hours'
}
# Check embargo periods
if 'embargo_until' in restrictions:
embargo_until = datetime.fromisoformat(restrictions['embargo_until'])
if current_time < embargo_until:
return {
'authorized': False,
'reason': f"Data embargoed until {embargo_until}"
}
return {'authorized': True}
def _check_geographic_restrictions(self, user_id: str, element: DataElement,
policy: SecurityPolicy) -> Dict[str, Any]:
"""Check geographic access restrictions"""
if not policy.geographic_restrictions:
return {'authorized': True}
user_attrs = self.user_attributes.get(user_id, {})
user_location = user_attrs.get('location', 'unknown')
# Check if user location is restricted
if user_location in policy.geographic_restrictions:
return {
'authorized': False,
'reason': f"Access restricted from location: {user_location}"
}
return {'authorized': True}
def _map_classification_to_clearance(self, classification: DataClassification) -> str:
"""Map data classification to required clearance level"""
mapping = {
DataClassification.PUBLIC: 'public',
DataClassification.INTERNAL: 'internal',
DataClassification.CONFIDENTIAL: 'confidential',
DataClassification.RESTRICTED: 'restricted',
DataClassification.TOP_SECRET: 'top_secret'
}
return mapping.get(classification, 'top_secret')
def _clearance_sufficient(self, user_clearance: str, required_clearance: str) -> bool:
"""Check if user clearance is sufficient"""
clearance_levels = ['public', 'internal', 'confidential', 'restricted', 'top_secret']
user_level = clearance_levels.index(user_clearance) if user_clearance in clearance_levels else -1
required_level = clearance_levels.index(required_clearance) if required_clearance in clearance_levels else len(clearance_levels)
return user_level >= required_level
def _barrier_blocks_access(self, barrier: str, element: DataElement) -> bool:
"""Check if information barrier blocks access to element"""
# Simplified barrier logic - in production would be more sophisticated
barrier_config = self.information_barriers.get(barrier, {})
blocked_tags = barrier_config.get('blocked_tags', [])
return any(tag in element.sensitivity_tags for tag in blocked_tags)
def _is_market_hours(self, timestamp: datetime) -> bool:
"""Check if timestamp is within market hours"""
# Simplified market hours check (9:30 AM - 4:00 PM ET, Monday-Friday)
weekday = timestamp.weekday() # 0 = Monday, 6 = Sunday
hour = timestamp.hour
if weekday >= 5: # Weekend
return False
if hour < 9 or hour >= 16: # Outside 9 AM - 4 PM
return False
return True
def _determine_access_restrictions(self, user_id: str, element: DataElement,
context: AccessContext, policy: SecurityPolicy) -> List[str]:
"""Determine specific access restrictions for user"""
restrictions = []
user_attrs = self.user_attributes.get(user_id, {})
# Add context-specific restrictions
if context == AccessContext.RESEARCH:
restrictions.append('no_trading_decisions')
elif context == AccessContext.TRADING:
restrictions.append('trading_compliance_required')
# Add classification-specific restrictions
if element.classification in [DataClassification.RESTRICTED, DataClassification.TOP_SECRET]:
restrictions.extend(['no_external_sharing', 'screen_recording_disabled'])
# Add user-specific restrictions
if user_attrs.get('probationary', False):
restrictions.append('supervisor_notification')
return restrictions
def _determine_access_level(self, user_id: str, element: DataElement,
context: AccessContext) -> str:
"""Determine the level of access granted"""
user_attrs = self.user_attributes.get(user_id, {})
if context == AccessContext.AUDIT:
return 'full_access'
elif element.classification == DataClassification.PUBLIC:
return 'full_access'
elif user_attrs.get('senior_analyst', False):
return 'full_access'
else:
return 'restricted_access'
class PrivacyProtectionEngine:
"""Privacy protection and data anonymization for economic data"""
def __init__(self):
self.anonymization_techniques = {}
self.privacy_policies = {}
self.consent_records = {}
def apply_privacy_protection(self, element: DataElement, user_id: str) -> Any:
"""Apply privacy protection based on user context and data sensitivity"""
# Determine required privacy protection level
protection_level = self._determine_protection_level(element, user_id)
if protection_level == 'none':
return element.content
elif protection_level == 'basic':
return self._apply_basic_masking(element.content)
elif protection_level == 'differential':
return self._apply_differential_privacy(element.content)
elif protection_level == 'synthetic':
return self._generate_synthetic_data(element.content)
else:
return self._apply_full_anonymization(element.content)
def apply_data_masking(self, element: DataElement) -> DataElement:
"""Apply data masking for sensitive elements"""
if 'personal-data' in element.sensitivity_tags:
masked_content = self._mask_personal_identifiers(element.content)
elif 'financial' in element.sensitivity_tags:
masked_content = self._mask_financial_data(element.content)
else:
masked_content = self._apply_generic_masking(element.content)
# Create masked element
masked_element = DataElement(
element_id=element.element_id,
content=masked_content,
classification=element.classification,
sensitivity_tags=element.sensitivity_tags | {'masked'},
created_at=element.created_at,
expires_at=element.expires_at,
owner=element.owner,
source_system=element.source_system,
encryption_key_id=element.encryption_key_id,
access_history=element.access_history
)
return masked_element
def _determine_protection_level(self, element: DataElement, user_id: str) -> str:
"""Determine required privacy protection level"""
# Check if user has special privacy permissions
user_privacy_role = self._get_user_privacy_role(user_id)
if user_privacy_role == 'privacy_officer':
return 'none' # Privacy officers can see unprotected data
elif 'personal-data' in element.sensitivity_tags:
return 'differential' # Personal data requires differential privacy
elif element.classification == DataClassification.RESTRICTED:
return 'synthetic' # Generate synthetic data for restricted content
elif 'preliminary' in element.sensitivity_tags:
return 'basic' # Basic masking for preliminary data
else:
return 'none'
def _apply_basic_masking(self, content: Any) -> Any:
"""Apply basic data masking"""
if isinstance(content, dict):
masked = content.copy()
# Mask specific sensitive fields
sensitive_fields = ['ssn', 'account_number', 'phone', 'email']
for field in sensitive_fields:
if field in masked:
masked[field] = self._mask_string(str(masked[field]))
return masked
elif isinstance(content, str):
return self._mask_string(content)
else:
return content
def _apply_differential_privacy(self, content: Any) -> Any:
"""Apply differential privacy for numerical data"""
if isinstance(content, dict):
protected = content.copy()
# Add noise to numerical values
for key, value in protected.items():
if isinstance(value, (int, float)):
# Add Laplace noise for differential privacy
noise = np.random.laplace(0, 1.0) # sensitivity/epsilon
protected[key] = value + noise
return protected
else:
return content
def _generate_synthetic_data(self, content: Any) -> Any:
"""Generate synthetic data preserving statistical properties"""
if isinstance(content, dict):
synthetic = {}
for key, value in content.items():
if isinstance(value, (int, float)):
# Generate synthetic numerical data
mean = value
std = abs(value * 0.1) # 10% standard deviation
synthetic[key] = np.random.normal(mean, std)
elif isinstance(value, str):
# Generate synthetic categorical data
synthetic[key] = f"synthetic_{hash(value) % 1000}"
else:
synthetic[key] = value
return synthetic
else:
return content
def _apply_full_anonymization(self, content: Any) -> Any:
"""Apply full anonymization removing all identifying information"""
if isinstance(content, dict):
anonymized = {}
# Remove or generalize identifying fields
safe_fields = ['category', 'type', 'region', 'date_range']
for key, value in content.items():
if key in safe_fields:
anonymized[key] = value
elif isinstance(value, (int, float)):
# Generalize numerical values
anonymized[key] = round(value, -1) # Round to nearest 10
return anonymized
else:
return "anonymized_content"
def _mask_personal_identifiers(self, content: Any) -> Any:
"""Mask personal identifiers in content"""
if isinstance(content, dict):
masked = content.copy()
# Mask SSNs
if 'ssn' in masked:
ssn = str(masked['ssn'])
masked['ssn'] = f"***-**-{ssn[-4:]}" if len(ssn) >= 4 else "***-**-****"
# Mask phone numbers
if 'phone' in masked:
phone = str(masked['phone'])
masked['phone'] = f"***-***-{phone[-4:]}" if len(phone) >= 4 else "***-***-****"
return masked
else:
return content
def _mask_financial_data(self, content: Any) -> Any:
"""Mask sensitive financial information"""
if isinstance(content, dict):
masked = content.copy()
# Mask account numbers
for field in ['account_number', 'routing_number']:
if field in masked:
account = str(masked[field])
masked[field] = f"****{account[-4:]}" if len(account) >= 4 else "****"
# Generalize amounts above threshold
for field in ['balance', 'amount', 'limit']:
if field in masked and isinstance(masked[field], (int, float)):
if masked[field] > 10000:
masked[field] = ">$10,000"
return masked
else:
return content
def _apply_generic_masking(self, content: Any) -> Any:
"""Apply generic masking for unspecified sensitive content"""
if isinstance(content, str):
return self._mask_string(content)
elif isinstance(content, dict):
return {k: self._mask_string(str(v)) if isinstance(v, str) else v
for k, v in content.items()}
else:
return content
def _mask_string(self, text: str) -> str:
"""Mask string content preserving first and last characters"""
if len(text) <= 2:
return '*' * len(text)
elif len(text) <= 4:
return text[0] + '*' * (len(text) - 2) + text[-1]
else:
return text[:2] + '*' * (len(text) - 4) + text[-2:]
def _get_user_privacy_role(self, user_id: str) -> str:
"""Get user's privacy role"""
# In production, this would query user management system
return 'standard_user'
class SecurityAuditLogger:
"""Comprehensive audit logging for security events"""
def __init__(self):
self.audit_logs = []
self.log_retention_days = 2555 # 7 years for regulatory compliance
def log_security_event(self, event: Dict[str, Any]):
"""Log security event with comprehensive metadata"""
audit_entry = {
'event_id': secrets.token_hex(16),
'timestamp': event.get('timestamp', datetime.utcnow()),
'event_type': event['event_type'],
'severity': self._determine_event_severity(event),
'user_id': event.get('user_id'),
'resource_id': event.get('element_id'),
'classification': event.get('classification'),
'source_ip': event.get('source_ip', 'internal'),
'user_agent': event.get('user_agent', 'system'),
'session_id': event.get('session_id'),
'details': event,
'compliance_tags': self._generate_compliance_tags(event)
}
self.audit_logs.append(audit_entry)
# In production, would also send to external SIEM
self._send_to_siem(audit_entry)
logging.info(f"Security event logged: {event['event_type']} for user {event.get('user_id', 'unknown')}")
def _determine_event_severity(self, event: Dict[str, Any]) -> str:
"""Determine event severity level"""
event_type = event['event_type']
if event_type in ['access_denied', 'unauthorized_access_attempt']:
return 'medium'
elif event_type in ['data_breach', 'privilege_escalation']:
return 'critical'
elif event_type in ['data_accessed', 'data_secured']:
return 'low'
else:
return 'medium'
def _generate_compliance_tags(self, event: Dict[str, Any]) -> List[str]:
"""Generate compliance tags for audit entry"""
tags = []
classification = event.get('classification')
if classification in ['restricted', 'top_secret']:
tags.append('high_security')
if event.get('event_type') == 'data_accessed':
tags.append('data_access')
if 'personal-data' in str(event.get('details', {})):
tags.append('privacy_relevant')
return tags
def _send_to_siem(self, audit_entry: Dict[str, Any]):
"""Send audit entry to external SIEM system"""
# In production, would integrate with SIEM
pass
def generate_compliance_report(self, start_date: datetime, end_date: datetime) -> Dict[str, Any]:
"""Generate compliance report for audit period"""
relevant_logs = [
log for log in self.audit_logs
if start_date <= log['timestamp'] <= end_date
]
return {
'report_period': {
'start': start_date.isoformat(),
'end': end_date.isoformat()
},
'total_events': len(relevant_logs),
'events_by_type': self._count_events_by_type(relevant_logs),
'events_by_severity': self._count_events_by_severity(relevant_logs),
'access_patterns': self._analyze_access_patterns(relevant_logs),
'security_incidents': self._identify_security_incidents(relevant_logs)
}
def _count_events_by_type(self, logs: List[Dict[str, Any]]) -> Dict[str, int]:
"""Count events by type"""
counts = {}
for log in logs:
event_type = log['event_type']
counts[event_type] = counts.get(event_type, 0) + 1
return counts
def _count_events_by_severity(self, logs: List[Dict[str, Any]]) -> Dict[str, int]:
"""Count events by severity"""
counts = {}
for log in logs:
severity = log['severity']
counts[severity] = counts.get(severity, 0) + 1
return counts
def _analyze_access_patterns(self, logs: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Analyze data access patterns"""
access_logs = [log for log in logs if log['event_type'] == 'data_accessed']
user_access_counts = {}
resource_access_counts = {}
for log in access_logs:
user_id = log.get('user_id')
resource_id = log.get('resource_id')
if user_id:
user_access_counts[user_id] = user_access_counts.get(user_id, 0) + 1
if resource_id:
resource_access_counts[resource_id] = resource_access_counts.get(resource_id, 0) + 1
return {
'total_accesses': len(access_logs),
'unique_users': len(user_access_counts),
'unique_resources': len(resource_access_counts),
'top_users': sorted(user_access_counts.items(), key=lambda x: x[1], reverse=True)[:10],
'top_resources': sorted(resource_access_counts.items(), key=lambda x: x[1], reverse=True)[:10]
}
def _identify_security_incidents(self, logs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
"""Identify potential security incidents"""
incidents = []
# Look for suspicious patterns
failed_access_attempts = {}
for log in logs:
if log['event_type'] == 'access_denied':
user_id = log.get('user_id')
if user_id:
failed_access_attempts[user_id] = failed_access_attempts.get(user_id, 0) + 1
# Flag users with excessive failed attempts
for user_id, attempts in failed_access_attempts.items():
if attempts > 10: # Threshold for suspicious activity
incidents.append({
'type': 'excessive_failed_access',
'user_id': user_id,
'attempt_count': attempts,
'severity': 'medium'
})
return incidents
class ThreatDetectionSystem:
"""Advanced threat detection for economic data access"""
def __init__(self):
self.user_behavior_baselines = {}
self.suspicious_patterns = {}
self.threat_intelligence = {}
def assess_access_request(self, user_id: str, element: DataElement,
context: AccessContext) -> Dict[str, Any]:
"""Assess risk level of access request"""
risk_factors = []
risk_score = 0
# Behavioral analysis
behavioral_risk = self._analyze_user_behavior(user_id, element, context)
risk_score += behavioral_risk['risk_score']
risk_factors.extend(behavioral_risk['factors'])
# Temporal analysis
temporal_risk = self._analyze_temporal_patterns(user_id, element)
risk_score += temporal_risk['risk_score']
risk_factors.extend(temporal_risk['factors'])
# Content sensitivity analysis
content_risk = self._analyze_content_sensitivity(element, context)
risk_score += content_risk['risk_score']
risk_factors.extend(content_risk['factors'])
# Determine overall risk level
if risk_score >= 8:
risk_level = 'high'
elif risk_score >= 5:
risk_level = 'medium'
else:
risk_level = 'low'
return {
'risk_level': risk_level,
'risk_score': risk_score,
'risk_factors': risk_factors,
'requires_additional_auth': risk_level == 'high'
}
def _analyze_user_behavior(self, user_id: str, element: DataElement,
context: AccessContext) -> Dict[str, Any]:
"""Analyze user behavior for anomalies"""
baseline = self.user_behavior_baselines.get(user_id, {})
risk_factors = []
risk_score = 0
# Check access time patterns
current_hour = datetime.utcnow().hour
typical_hours = baseline.get('typical_access_hours', [])
if typical_hours and current_hour not in typical_hours:
risk_factors.append('unusual_access_time')
risk_score += 2
# Check access frequency
recent_accesses = baseline.get('recent_access_count', 0)
typical_daily_accesses = baseline.get('avg_daily_accesses', 10)
if recent_accesses > typical_daily_accesses * 3:
risk_factors.append('unusual_access_frequency')
risk_score += 3
# Check data classification pattern
typical_classifications = baseline.get('typical_classifications', [])
if element.classification.value not in typical_classifications:
risk_factors.append('unusual_data_classification')
risk_score += 2
return {
'risk_score': risk_score,
'factors': risk_factors
}
def _analyze_temporal_patterns(self, user_id: str, element: DataElement) -> Dict[str, Any]:
"""Analyze temporal access patterns"""
risk_factors = []
risk_score = 0
current_time = datetime.utcnow()
# Check for weekend/holiday access
if current_time.weekday() >= 5: # Weekend
risk_factors.append('weekend_access')
risk_score += 1
# Check for after-hours access
if current_time.hour < 6 or current_time.hour > 22:
risk_factors.append('after_hours_access')
risk_score += 2
# Check proximity to earnings/announcement periods
if self._is_earnings_season(current_time):
risk_factors.append('earnings_season_access')
risk_score += 1
return {
'risk_score': risk_score,
'factors': risk_factors
}
def _analyze_content_sensitivity(self, element: DataElement,
context: AccessContext) -> Dict[str, Any]:
"""Analyze content sensitivity for risk assessment"""
risk_factors = []
risk_score = 0
# High-classification data increases risk
if element.classification in [DataClassification.RESTRICTED, DataClassification.TOP_SECRET]:
risk_factors.append('high_classification_data')
risk_score += 3
# Market-moving data increases risk
if 'market-moving' in element.sensitivity_tags:
risk_factors.append('market_moving_data')
risk_score += 2
# Preliminary data increases risk
if 'preliminary' in element.sensitivity_tags:
risk_factors.append('preliminary_data')
risk_score += 1
# Trading context with sensitive data
if context == AccessContext.TRADING and 'market-moving' in element.sensitivity_tags:
risk_factors.append('trading_access_to_market_data')
risk_score += 3
return {
'risk_score': risk_score,
'factors': risk_factors
}
def _is_earnings_season(self, timestamp: datetime) -> bool:
"""Check if timestamp falls during earnings season"""
# Simplified earnings season detection
month = timestamp.month
return month in [1, 4, 7, 10] # Quarterly earnings months
Compliance and Regulatory Security
Economic data systems must meet stringent regulatory security requirements that vary by jurisdiction, data type, and organizational context. The compliance framework must address sector-specific regulations like Basel III for banking, Solvency II for insurance, and various securities regulations while maintaining operational flexibility for legitimate business activities.
Cross-border data protection requires sophisticated understanding of international regulations and their implications for economic data sharing. The framework must implement dynamic controls that adjust based on data destination, user location, and the specific regulatory requirements that apply to different types of economic information.
Incident response and breach notification procedures must account for the unique characteristics of economic data breaches, including potential market impact, regulatory notification requirements, and the specialized forensic techniques needed to investigate economic data security incidents. The response framework must integrate with existing business continuity plans while addressing the specific recovery requirements for economic data systems.
The security framework presented in this guide provides the foundation for implementing comprehensive protection for economic data systems. These security controls integrate with the governance frameworks from Economic Data Governance and Compliance and support the operational requirements discussed in Container Orchestration for Economic Data Systems. Together, these frameworks enable organizations to build economic data systems that meet the highest security and compliance standards while supporting the analytical capabilities needed for modern economic analysis.
Related Guides
For comprehensive economic data security implementation, explore these complementary resources:
- Economic Data Governance and Compliance - Governance frameworks that support security controls
- Container Orchestration for Economic Data Systems - Security in containerized environments
- Economic Indicator Alerting and Monitoring Systems - Monitor security events and incidents
- Database Integration for Economic Data Storage - Database security for economic data
- Cloud Deployment Scaling Economic Data Systems - Cloud security for economic data systems
- API Integration for Economic Data Sources - Secure API integration patterns
- Data Quality Practices for Economic Datasets - Quality controls that support security