Implementing Continuous Verification
Implementing Continuous Verification
Continuous verification forms the heart of zero-trust architecture. Rather than authenticating once at session start, systems continuously evaluate trust levels based on user behavior, device health, and environmental factors. This dynamic approach can detect account takeovers, insider threats, and other attacks that bypass initial authentication.
Risk-based authentication adjusts security requirements based on real-time risk assessment. Low-risk activities might proceed with minimal friction, while high-risk operations trigger additional verification steps. Machine learning models analyze patterns to identify anomalous behavior that might indicate compromise. This adaptive approach balances security with usability by applying stronger controls only when needed.
# Example: Zero-trust data access system with continuous verification
import asyncio
import hashlib
import json
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import numpy as np
from cryptography.fernet import Fernet
import jwt
class TrustLevel(Enum):
NONE = 0
LOW = 1
MEDIUM = 2
HIGH = 3
CRITICAL = 4
class RiskFactor(Enum):
DEVICE_UNKNOWN = "device_unknown"
LOCATION_UNUSUAL = "location_unusual"
TIME_UNUSUAL = "time_unusual"
BEHAVIOR_ANOMALY = "behavior_anomaly"
FAILED_ATTEMPTS = "failed_attempts"
PRIVILEGE_ESCALATION = "privilege_escalation"
DATA_VOLUME_SPIKE = "data_volume_spike"
@dataclass
class AccessContext:
user_id: str
device_id: str
ip_address: str
location: Dict
timestamp: datetime
session_id: str
authentication_methods: List[str]
risk_factors: List[RiskFactor]
trust_score: float
class ZeroTrustDataAccess:
def __init__(self, config):
self.config = config
self.trust_evaluator = TrustEvaluator()
self.behavior_analyzer = BehaviorAnalyzer()
self.session_manager = SessionManager()
self.data_store = SecureDataStore()
self.audit_logger = AuditLogger()
async def request_data_access(
self,
user_credentials: Dict,
device_info: Dict,
requested_data: str,
operation: str
) -> Tuple[bool, Optional[Dict]]:
"""Process data access request with zero-trust verification"""
# Create access context
context = await self._build_access_context(
user_credentials, device_info
)
# Initial authentication
auth_result = await self._authenticate_user(
user_credentials, context
)
if not auth_result.success:
await self._log_failed_access(context, auth_result.reason)
return False, None
# Continuous trust evaluation
trust_level = await self.trust_evaluator.evaluate(context)
# Check if trust level meets data requirements
required_trust = self._get_required_trust_level(
requested_data, operation
)
if trust_level.value < required_trust.value:
# Attempt to elevate trust through additional verification
elevated = await self._elevate_trust_level(
context, required_trust
)
if not elevated:
await self._log_insufficient_trust(
context, trust_level, required_trust
)
return False, None
# Apply data minimization
minimized_data = await self._apply_data_minimization(
requested_data, context, operation
)
# Create time-limited access token
access_token = await self._create_access_token(
context, minimized_data, operation
)
# Start continuous monitoring
asyncio.create_task(
self._monitor_session(context.session_id)
)
# Log successful access
await self.audit_logger.log_access_granted(
context, minimized_data, trust_level
)
return True, {
'access_token': access_token,
'data_scope': minimized_data['scope'],
'expires_in': self.config['token_lifetime'],
'monitoring_enabled': True
}
async def _authenticate_user(
self,
credentials: Dict,
context: AccessContext
) -> AuthResult:
"""Multi-factor authentication with device verification"""
# Password verification (if provided)
if 'password' in credentials:
if not await self._verify_password(
context.user_id, credentials['password']
):
return AuthResult(False, 'invalid_password')
# Require MFA for all data access
mfa_result = await self._verify_mfa(credentials, context)
if not mfa_result.success:
return mfa_result
# Device trust verification
device_trust = await self._verify_device_trust(
context.device_id, context.user_id
)
if not device_trust.trusted:
context.risk_factors.append(RiskFactor.DEVICE_UNKNOWN)
if device_trust.blocked:
return AuthResult(False, 'device_blocked')
# Behavioral verification
behavior_check = await self.behavior_analyzer.verify_behavior(
context
)
if behavior_check.anomaly_score > self.config['anomaly_threshold']:
context.risk_factors.append(RiskFactor.BEHAVIOR_ANOMALY)
if behavior_check.high_risk:
return AuthResult(False, 'behavioral_anomaly')
return AuthResult(True, 'authenticated')
async def _elevate_trust_level(
self,
context: AccessContext,
required_level: TrustLevel
) -> bool:
"""Attempt to elevate trust through additional verification"""
elevation_methods = {
TrustLevel.MEDIUM: ['sms_verification', 'email_verification'],
TrustLevel.HIGH: ['biometric', 'hardware_token'],
TrustLevel.CRITICAL: ['in_person_verification', 'video_verification']
}
methods = elevation_methods.get(required_level, [])
for method in methods:
if await self._request_additional_verification(
context, method
):
# Re-evaluate trust level
new_trust = await self.trust_evaluator.evaluate(context)
if new_trust.value >= required_level.value:
return True
return False
async def _apply_data_minimization(
self,
requested_data: str,
context: AccessContext,
operation: str
) -> Dict:
"""Apply data minimization based on principle of least privilege"""
# Get user's role and permissions
user_role = await self._get_user_role(context.user_id)
permissions = await self._get_role_permissions(user_role)
# Determine minimum required data
data_schema = self._get_data_schema(requested_data)
required_fields = self._get_required_fields_for_operation(
operation, data_schema
)
# Filter based on permissions
allowed_fields = [
field for field in required_fields
if self._has_field_permission(permissions, field)
]
# Apply additional restrictions based on context
if RiskFactor.LOCATION_UNUSUAL in context.risk_factors:
# Remove highly sensitive fields for unusual locations
allowed_fields = [
f for f in allowed_fields
if not self._is_highly_sensitive(f)
]
# Apply time-based restrictions
if self._is_outside_business_hours(context.timestamp):
allowed_fields = self._apply_time_restrictions(allowed_fields)
return {
'original_request': requested_data,
'allowed_fields': allowed_fields,
'excluded_fields': list(
set(required_fields) - set(allowed_fields)
),
'scope': self._calculate_data_scope(allowed_fields),
'restrictions': self._get_applied_restrictions(context)
}
async def _monitor_session(self, session_id: str):
"""Continuous session monitoring for anomalies"""
monitoring_interval = self.config['monitoring_interval']
while await self.session_manager.is_active(session_id):
session = await self.session_manager.get_session(session_id)
if not session:
break
# Re-evaluate trust periodically
current_trust = await self.trust_evaluator.evaluate(
session.context
)
# Check for trust degradation
if current_trust.value < session.initial_trust.value:
await self._handle_trust_degradation(
session, current_trust
)
# Monitor data access patterns
access_pattern = await self._analyze_access_pattern(session_id)
if access_pattern.suspicious:
await self._handle_suspicious_activity(
session, access_pattern
)
# Check session health
if not await self._verify_session_health(session):
await self._terminate_session(
session_id, 'health_check_failed'
)
break
await asyncio.sleep(monitoring_interval)
async def _handle_trust_degradation(
self,
session: Session,
new_trust: TrustLevel
):
"""Handle degradation in trust level during session"""
degradation_level = session.initial_trust.value - new_trust.value
if degradation_level >= 2:
# Severe degradation - terminate immediately
await self._terminate_session(
session.id, 'severe_trust_degradation'
)
elif degradation_level >= 1:
# Moderate degradation - restrict access
await self._restrict_session_access(session, new_trust)
await self._request_reauthentication(session)
else:
# Minor degradation - increase monitoring
await self._increase_monitoring_frequency(session)
class DataMinimizationEngine:
"""Engine for implementing data minimization principles"""
def __init__(self, config):
self.config = config
self.schema_registry = SchemaRegistry()
self.classification_engine = DataClassificationEngine()
self.retention_manager = RetentionManager()
async def minimize_stored_data(
self,
data: Dict,
purpose: str,
retention_period: Optional[int] = None
) -> Dict:
"""Minimize data before storage based on purpose"""
# Classify data fields
classification = await self.classification_engine.classify(data)
# Determine required fields for purpose
required_fields = self._get_required_fields(purpose)
# Remove unnecessary fields
minimized_data = {
k: v for k, v in data.items()
if k in required_fields or self._is_mandatory_field(k)
}
# Apply data transformation for privacy
transformed_data = await self._apply_privacy_transforms(
minimized_data, classification
)
# Set retention period
if retention_period is None:
retention_period = self._get_minimum_retention(
purpose, classification
)
# Create minimization record
minimization_record = {
'original_fields': list(data.keys()),
'retained_fields': list(transformed_data.keys()),
'removed_fields': list(
set(data.keys()) - set(transformed_data.keys())
),
'purpose': purpose,
'retention_days': retention_period,
'minimization_date': datetime.utcnow().isoformat(),
'techniques_applied': self._get_applied_techniques(
data, transformed_data
)
}
return {
'data': transformed_data,
'minimization_record': minimization_record,
'retention_until': (
datetime.utcnow() + timedelta(days=retention_period)
).isoformat()
}
async def _apply_privacy_transforms(
self,
data: Dict,
classification: Dict
) -> Dict:
"""Apply privacy-preserving transformations"""
transformed = {}
for field, value in data.items():
field_class = classification.get(field, 'general')
if field_class == 'identifier':
# Pseudonymize identifiers
transformed[field] = await self._pseudonymize(value)
elif field_class == 'quasi_identifier':
# Generalize quasi-identifiers
transformed[field] = self._generalize(field, value)
elif field_class == 'sensitive':
# Encrypt sensitive data
transformed[field] = await self._encrypt_field(value)
elif field_class == 'statistical':
# Add noise for statistical data
transformed[field] = self._add_differential_privacy(value)
else:
# Keep as-is for general data
transformed[field] = value
return transformed
def _generalize(self, field: str, value: any) -> any:
"""Generalize data to reduce identification risk"""
generalization_rules = {
'age': lambda v: f"{(v // 5) * 5}-{(v // 5) * 5 + 4}",
'zipcode': lambda v: v[:3] + "**",
'ip_address': lambda v: '.'.join(v.split('.')[:2] + ['0', '0']),
'timestamp': lambda v: v.replace(second=0, microsecond=0),
'salary': lambda v: f"{(v // 10000) * 10000}-{(v // 10000 + 1) * 10000}"
}
if field in generalization_rules:
return generalization_rules[field](value)
return value
async def implement_progressive_data_deletion(
self,
data_id: str,
deletion_schedule: List[Dict]
):
"""Progressively delete data fields over time"""
for stage in deletion_schedule:
await self._schedule_deletion_stage(
data_id,
stage['fields'],
stage['after_days']
)
# Create deletion audit trail
await self.audit_logger.log_progressive_deletion(
data_id, deletion_schedule
)