Docs / Compliance & policies
Compliance & policies
Establish comprehensive compliance frameworks that meet regulatory requirements while enabling secure AI evaluation operations. Configure policies, controls, and audit procedures that align with your organization's risk profile and regulatory obligations.
Compliance in AI systems requires balancing innovation velocity with regulatory requirements, data protection obligations, and enterprise security policies. Effective compliance frameworks provide clear guidelines while enabling teams to operate efficiently within approved boundaries.
Modern compliance approaches emphasize automation, continuous monitoring, and evidence collection to demonstrate ongoing adherence to regulatory requirements and organizational policies without impeding operational efficiency.
Regulatory Framework Implementation
Implement controls and procedures that address specific regulatory requirements relevant to your industry and geography. Common frameworks include GDPR, HIPAA, SOX, PCI DSS, and industry-specific regulations.
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Identify applicable regulations Assess which regulations apply based on your industry, geography, and data types.
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Map requirements to controls Translate regulatory requirements into specific technical and procedural controls.
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Implement monitoring systems Deploy automated monitoring to ensure continuous compliance with established controls.
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Establish audit procedures Create systematic audit processes to verify compliance and identify improvement areas.
import evaligo
from evaligo.compliance import ComplianceFramework, PolicyEngine
from datetime import datetime, timedelta
class EnterpriseComplianceManager:
"""Manages compliance policies and monitoring for AI evaluation systems"""
def __init__(self, client: evaligo.Client):
self.client = client
self.framework = ComplianceFramework(client)
self.policy_engine = PolicyEngine(client)
def setup_gdpr_compliance(self) -> Dict:
"""Configure GDPR compliance controls"""
gdpr_policies = {
"data_retention": {
"user_data_retention_days": 365,
"evaluation_data_retention_days": 2555, # 7 years
"audit_log_retention_days": 2555,
"automatic_deletion": True,
"deletion_verification": True
},
"data_protection": {
"encryption_in_transit": "TLS_1_3",
"encryption_at_rest": "AES_256",
"key_rotation_days": 90,
"data_classification": "automatic",
"pseudonymization": True
},
"consent_management": {
"explicit_consent_required": True,
"consent_withdrawal_processing": "immediate",
"consent_audit_trail": True,
"purpose_limitation": True
},
"data_subject_rights": {
"right_to_access": True,
"right_to_rectification": True,
"right_to_erasure": True,
"right_to_portability": True,
"automated_response_sla_hours": 72
}
}
# Apply GDPR policies
for policy_category, settings in gdpr_policies.items():
self.policy_engine.create_policy(
name=f"GDPR_{policy_category}",
category="data_protection",
regulations=["GDPR"],
settings=settings,
enforcement_level="mandatory",
violation_handling="automatic_block"
)
# Set up GDPR monitoring
gdpr_monitors = self._configure_gdpr_monitoring()
return {
"policies_configured": len(gdpr_policies),
"monitors_active": len(gdpr_monitors),
"compliance_status": "configured",
"next_audit_date": datetime.now() + timedelta(days=90)
}
def setup_hipaa_compliance(self) -> Dict:
"""Configure HIPAA compliance for healthcare organizations"""
hipaa_policies = {
"administrative_safeguards": {
"security_officer_assigned": True,
"workforce_training_required": True,
"access_management": "role_based",
"incident_response_plan": True,
"business_associate_agreements": True
},
"physical_safeguards": {
"facility_access_controls": True,
"workstation_security": True,
"device_media_controls": True,
"data_center_compliance": "SOC2_Type2"
},
"technical_safeguards": {
"access_control": "minimum_necessary",
"audit_controls": "comprehensive",
"integrity": "data_validation",
"transmission_security": "end_to_end_encryption",
"unique_user_identification": True,
"automatic_logoff": "15_minutes"
}
}
# Apply HIPAA policies
for safeguard_type, controls in hipaa_policies.items():
self.policy_engine.create_policy(
name=f"HIPAA_{safeguard_type}",
category="healthcare_compliance",
regulations=["HIPAA"],
settings=controls,
enforcement_level="strict",
audit_frequency="monthly"
)
return {"hipaa_safeguards_configured": len(hipaa_policies)}
def configure_enterprise_policies(self, organization_config: Dict) -> Dict:
"""Configure organization-specific compliance policies"""
# Data classification policy
data_classification = self.policy_engine.create_policy(
name="Enterprise_Data_Classification",
category="data_governance",
settings={
"classification_levels": ["public", "internal", "confidential", "restricted"],
"automatic_classification": True,
"classification_algorithms": ["content_analysis", "metadata_rules"],
"reclassification_triggers": ["data_access_patterns", "content_changes"],
"retention_by_classification": {
"public": "indefinite",
"internal": "7_years",
"confidential": "5_years",
"restricted": "3_years"
}
}
)
# Access control policy
access_control = self.policy_engine.create_policy(
name="Enterprise_Access_Control",
category="identity_management",
settings={
"authentication": "multi_factor_required",
"session_timeout_minutes": 30,
"concurrent_sessions_limit": 3,
"privileged_access_monitoring": True,
"just_in_time_access": True,
"access_review_frequency": "quarterly"
}
)
return {
"enterprise_policies": 2,
"data_classification_id": data_classification.id,
"access_control_id": access_control.id
}
# Usage example
compliance_manager = EnterpriseComplianceManager(client)
# Setup compliance frameworks
gdpr_setup = compliance_manager.setup_gdpr_compliance()
print(f"GDPR compliance configured: {gdpr_setup['policies_configured']} policies")
# Setup organization-specific policies
org_config = {
"industry": "healthcare",
"regions": ["EU", "US"],
"data_sensitivity": "high"
}
enterprise_setup = compliance_manager.configure_enterprise_policies(org_config)
print(f"Enterprise policies configured: {enterprise_setup['enterprise_policies']}")Continuous Compliance Monitoring
Implement automated compliance monitoring that continuously validates adherence to policies and regulatory requirements. Proactive monitoring enables rapid response to compliance deviations before they become violations.
Compliance Drift: Compliance is not a one-time configuration but requires continuous monitoring. System changes, new data types, and evolving regulations can create compliance gaps if not actively managed.
Video
Audit Preparation and Evidence Collection
Maintain comprehensive audit trails and evidence collection systems that support regulatory examinations and internal compliance reviews. Automated evidence collection reduces audit preparation time and ensures completeness.
Evidence Retention: Configure automated evidence collection that captures the right level of detail for audit purposes without overwhelming storage or creating privacy risks. Focus on demonstrable compliance rather than exhaustive logging.