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Last updated: October 10, 2025

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  • Installation

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  • REST API
  • Python SDK

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  • Cloud Deployment
  • Security

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  • FAQ
  • Changelog

Security Overview

Comprehensive security guide for LangTrain deployments. Learn about enterprise-grade data protection, access control, audit logging, and compliance requirements for production AI systems.

Security Architecture

LangTrain implements a defense-in-depth security model with multiple layers of protection. Security is built into every component from the ground up, not added as an afterthought.

Core Security Principles:
- Zero Trust Architecture: Never trust, always verify
- Principle of Least Privilege: Minimal access rights
- Data Minimization: Collect and process only necessary data
- Security by Design: Security embedded in development lifecycle
- Continuous Monitoring: Real-time threat detection and response

Our security framework follows industry standards including NIST Cybersecurity Framework, ISO 27001, and CIS Controls.
Code Example
from langtrain.security import SecurityManager, EncryptionService
from langtrain.compliance import ComplianceManager

# Initialize comprehensive security manager
security = SecurityManager(
    # Encryption configuration
    encryption_level="AES-256-GCM",
    key_rotation_interval="30d",
    key_management="HSM",          # Hardware Security Module
    
    # Audit and monitoring
    audit_level="detailed",
    real_time_monitoring=True,
    anomaly_detection=True,
    
    # Compliance settings
    compliance_mode=["SOC2", "GDPR", "HIPAA"],
    data_residency="EU",           # Geographic data restrictions
    retention_policy="7y",         # Data retention period
)

# Configure field-level encryption
encryption = EncryptionService(
    encryption_scope="field",      # Field-level vs full-disk
    key_derivation="PBKDF2-SHA256",
    secure_deletion=True,          # Cryptographic erasure
    format_preserving=True,        # Maintain data format
)

# Enable advanced security monitoring
security.enable_monitoring([
    "unauthorized_access_attempts",
    "data_access_patterns",
    "privilege_escalation",
    "data_exfiltration_detection",
    "model_poisoning_attempts",
    "adversarial_input_detection"
])

# Set up compliance reporting
compliance = ComplianceManager()
compliance.configure_reporting(
    frameworks=["SOC2", "GDPR"],
    schedule="monthly",
    automated_evidence_collection=True
)

Access Control & Authentication

LangTrain provides enterprise-grade access control with multi-layered authentication and fine-grained authorization. Our identity and access management (IAM) system supports integration with existing enterprise identity providers.

Authentication Methods:
- Multi-Factor Authentication (MFA): TOTP, SMS, biometrics
- Single Sign-On (SSO): SAML 2.0, OAuth 2.0, OpenID Connect
- API Key Management: Rotating keys with expiration policies
- Certificate-based Authentication: mTLS for service-to-service

Authorization Features:
- Role-Based Access Control (RBAC): Predefined and custom roles
- Attribute-Based Access Control (ABAC): Context-aware permissions
- Resource-level permissions: Fine-grained model and data access
- Time-based access: Temporary permissions with auto-expiry
Code Example
from langtrain.auth import AuthenticationManager, RoleManager
from langtrain.iam import PolicyEngine

# Configure authentication
auth_manager = AuthenticationManager(
    # Multi-factor authentication
    mfa_required=True,
    mfa_methods=["totp", "sms", "biometric"],
    
    # SSO integration
    sso_providers={
        "okta": {
            "saml_endpoint": "https://company.okta.com/saml",
            "certificate_path": "/certs/okta.pem"
        },
        "azure_ad": {
            "tenant_id": "your-tenant-id",
            "client_id": "your-client-id"
        }
    },
    
    # Session management
    session_timeout="8h",
    concurrent_sessions=1,
    idle_timeout="30m"
)

# Define role-based permissions
role_manager = RoleManager()

# Create custom roles with granular permissions
role_manager.create_role("ml_engineer", permissions=[
    "models.train",
    "models.evaluate", 
    "datasets.read",
    "experiments.create"
])

role_manager.create_role("data_scientist", permissions=[
    "models.train",
    "models.deploy",
    "datasets.read",
    "datasets.create",
    "experiments.manage"
])

role_manager.create_role("admin", permissions=["*"])

# Configure attribute-based access control
policy_engine = PolicyEngine()
policy_engine.add_policy(
    name="sensitive_data_access",
    condition="user.clearance_level >= dataset.classification_level",
    effect="allow"
)

policy_engine.add_policy(
    name="geographic_restriction",
    condition="user.location in dataset.allowed_regions",
    effect="allow"
)

# API key management with rotation
api_keys = auth_manager.create_api_key(
    user_id="user123",
    permissions=["models.inference"],
    expiry_days=30,
    auto_rotate=True,
    rate_limit="1000/hour"
)

Data Protection & Privacy

Protecting sensitive training data and model outputs is critical for AI systems. LangTrain implements multiple layers of data protection including encryption, anonymization, and differential privacy.

Data Protection Features:
- Encryption at Rest: AES-256 with customer-managed keys
- Encryption in Transit: TLS 1.3 with perfect forward secrecy
- Data Anonymization: PII detection and automatic redaction
- Differential Privacy: Statistical privacy for training data
- Secure Multi-party Computation: Collaborative training without data sharing
- Federated Learning: Train models without centralizing data

Privacy Controls:
- Data Lineage Tracking: Complete audit trail of data usage
- Right to be Forgotten: GDPR-compliant data deletion
- Consent Management: Granular consent tracking and enforcement
- Data Minimization: Automated identification of unnecessary data
Code Example
from langtrain.privacy import (
    DataProtectionManager, 
    DifferentialPrivacy,
    PIIDetector,
    ConsentManager
)

# Initialize data protection
data_protection = DataProtectionManager(
    # Encryption settings
    encryption_key_source="customer_managed",
    key_rotation_schedule="90d",
    
    # Privacy settings
    differential_privacy=True,
    privacy_budget=1.0,
    noise_multiplier=1.1,
    
    # Data handling
    automatic_pii_redaction=True,
    data_lineage_tracking=True,
    secure_deletion=True
)

# Configure differential privacy for training
dp = DifferentialPrivacy(
    epsilon=1.0,              # Privacy budget
    delta=1e-5,               # Failure probability
    noise_mechanism="gaussian",
    clipping_norm=1.0,        # Gradient clipping
    sampling_rate=0.01        # Batch sampling rate
)

# Train with differential privacy
model = langtrain.train(
    dataset=sensitive_dataset,
    privacy_engine=dp,
    max_grad_norm=1.0,
    noise_multiplier=1.1
)

# PII detection and redaction
pii_detector = PIIDetector(
    detection_types=[
        "email", "phone", "ssn", "credit_card", 
        "ip_address", "person_name", "address"
    ],
    confidence_threshold=0.95,
    redaction_method="masking"  # or "synthetic", "removal"
)

# Process data with automatic PII handling
cleaned_data = pii_detector.process_dataset(
    raw_dataset,
    preserve_format=True,
    audit_redactions=True
)

# Consent management for GDPR compliance
consent_manager = ConsentManager()

# Track user consent
consent_manager.record_consent(
    user_id="user123",
    data_types=["training_data", "model_outputs"],
    purposes=["model_improvement", "research"],
    consent_date="2024-01-01",
    expiry_date="2025-01-01"
)

# Enforce consent in data processing
if consent_manager.has_valid_consent(user_id, "training_data"):
    # Process user data
    process_user_data(user_data)
else:
    # Handle lack of consent
    handle_consent_required(user_id)

Threat Detection & Response

LangTrain includes advanced threat detection capabilities specifically designed for AI/ML systems. Our security operations center (SOC) monitors for both traditional cyber threats and AI-specific attacks.

AI-Specific Threat Detection:
- Model Poisoning: Detection of malicious training data
- Adversarial Attacks: Real-time detection of adversarial inputs
- Model Extraction: Protection against model theft attempts
- Data Poisoning: Identification of corrupted datasets
- Backdoor Detection: Scanning for hidden triggers in models

Traditional Security Monitoring:
- Intrusion Detection: Network and host-based monitoring
- Behavioral Analytics: User and entity behavior analysis
- Threat Intelligence: Integration with external threat feeds
- Automated Response: Incident response automation
Code Example
from langtrain.security import (
    ThreatDetector, 
    IncidentResponse,
    SecurityMonitor,
    AdversarialDefense
)

# Configure comprehensive threat detection
threat_detector = ThreatDetector(
    # AI-specific threats
    model_poisoning_detection=True,
    adversarial_input_detection=True,
    data_drift_monitoring=True,
    backdoor_scanning=True,
    
    # Traditional security threats
    intrusion_detection=True,
    behavioral_analytics=True,
    threat_intelligence_feeds=[
        "mitre_attack", "cve_database", "ai_threat_db"
    ],
    
    # Detection sensitivity
    sensitivity_level="high",
    false_positive_threshold=0.05
)

# Set up adversarial defense
adversarial_defense = AdversarialDefense(
    detection_methods=[
        "input_transformation",
        "statistical_analysis", 
        "ensemble_voting"
    ],
    response_actions=[
        "reject_input",
        "sanitize_input",
        "flag_for_review"
    ]
)

# Configure automated incident response
incident_response = IncidentResponse()

# Define response playbooks
incident_response.create_playbook(
    name="model_poisoning_detected",
    triggers=["high_confidence_poisoning_alert"],
    actions=[
        "isolate_affected_models",
        "revert_to_previous_checkpoint",
        "notify_security_team",
        "initiate_forensic_analysis"
    ],
    escalation_time="15m"
)

incident_response.create_playbook(
    name="adversarial_attack_detected", 
    triggers=["adversarial_input_confirmed"],
    actions=[
        "block_source_ip",
        "enhance_input_filtering",
        "collect_attack_samples",
        "update_defense_models"
    ]
)

# Real-time security monitoring
monitor = SecurityMonitor()
monitor.start_monitoring(
    components=["api_endpoints", "training_jobs", "data_pipelines"],
    metrics=["request_patterns", "resource_usage", "error_rates"],
    alert_thresholds={
        "failed_auth_attempts": 5,
        "unusual_data_access": 10,
        "model_performance_drop": 0.1
    }
)

# Integration with SIEM systems
monitor.configure_siem_integration(
    siem_type="splunk",
    endpoint="https://siem.company.com/api",
    format="cef",
    real_time_streaming=True
)

Compliance & Governance

LangTrain provides comprehensive compliance management tools to help organizations meet regulatory requirements and internal governance standards. Our platform supports automated compliance monitoring and reporting.

Supported Frameworks:
- SOC 2 Type II: Controls for security, availability, processing integrity
- GDPR: EU data protection regulation compliance
- HIPAA: Healthcare data protection (with BAA support)
- ISO 27001: Information security management systems
- PCI DSS: Payment card industry data security
- FedRAMP: US federal cloud security requirements

Governance Features:
- Policy Management: Centralized policy definition and enforcement
- Risk Assessment: Automated security risk evaluation
- Compliance Dashboards: Real-time compliance status monitoring
- Audit Trail: Immutable logs for compliance audits
- Automated Reporting: Scheduled compliance reports
Code Example
from langtrain.compliance import (
    ComplianceFramework,
    PolicyManager, 
    RiskAssessment,
    AuditLogger
)

# Configure compliance frameworks
compliance = ComplianceFramework(
    active_frameworks=["SOC2", "GDPR", "HIPAA"],
    
    # SOC 2 configuration
    soc2_controls={
        "CC6.1": "logical_access_controls",
        "CC6.2": "authentication_credentials", 
        "CC6.3": "authorized_access_changes",
        "CC7.1": "data_transmission_controls"
    },
    
    # GDPR configuration  
    gdpr_settings={
        "data_protection_officer": "dpo@company.com",
        "lawful_basis_tracking": True,
        "breach_notification_time": "72h",
        "consent_management": True
    },
    
    # HIPAA configuration
    hipaa_settings={
        "covered_entity": True,
        "business_associate_agreement": True,
        "minimum_necessary_standard": True,
        "breach_threshold": 500
    }
)

# Define and enforce policies
policy_manager = PolicyManager()

# Data handling policies
policy_manager.create_policy(
    name="data_retention",
    description="Automatic data deletion after retention period",
    rules=[
        "training_data.max_age = 7_years",
        "logs.max_age = 3_years", 
        "backups.max_age = 10_years"
    ],
    enforcement="automatic"
)

policy_manager.create_policy(
    name="cross_border_transfer",
    description="Restrictions on international data transfers",
    rules=[
        "pii_data.allowed_regions = ['EU', 'US']",
        "transfer_mechanism = 'standard_contractual_clauses'",
        "adequacy_decision_required = True"
    ],
    enforcement="blocking"
)

# Automated risk assessment
risk_assessment = RiskAssessment()
risk_report = risk_assessment.evaluate(
    scope="full_platform",
    frameworks=["NIST", "ISO27001"],
    assessment_type="quarterly",
    
    risk_categories=[
        "data_security",
        "access_control", 
        "business_continuity",
        "vendor_management",
        "incident_response"
    ]
)

# Continuous compliance monitoring
compliance.start_monitoring(
    check_frequency="daily",
    automated_remediation=True,
    
    # Compliance metrics
    track_metrics=[
        "access_review_completion",
        "security_training_completion", 
        "vulnerability_remediation_time",
        "incident_response_time",
        "backup_success_rate"
    ]
)

# Generate compliance reports
compliance_report = compliance.generate_report(
    framework="SOC2",
    period="2024-Q1",
    include_evidence=True,
    format="pdf",
    
    # Custom attestations
    attestations={
        "management_review": "2024-01-15",
        "independent_audit": "2024-02-01",
        "penetration_test": "2024-01-20"
    }
)

# Audit logging for compliance
audit_logger = AuditLogger(
    immutable_storage=True,
    encryption=True,
    digital_signatures=True,
    retention_period="10y"
)

# Log all security-relevant events
audit_logger.log_event(
    event_type="user_authentication",
    user_id="admin@company.com",
    timestamp="2024-01-15T10:30:00Z",
    result="success",
    additional_data={
        "ip_address": "10.0.1.100",
        "user_agent": "Mozilla/5.0...",
        "mfa_method": "totp"
    }
)

Security Best Practices

Follow these security best practices to maximize the protection of your LangTrain deployment. These recommendations are based on industry standards and real-world deployment experience.

Infrastructure Security:
- Use private networks and VPCs for all components
- Implement network segmentation and micro-segmentation
- Enable Web Application Firewall (WAF) protection
- Use container security scanning and runtime protection
- Implement secrets management with rotation

Operational Security:
- Regular security assessments and penetration testing
- Incident response plan testing and updates
- Security awareness training for all team members
- Vulnerability management and patch management
- Backup and disaster recovery testing
Code Example
# Security hardening checklist for production deployment

# 1. Network Security
network_config = {
    "vpc_isolation": True,
    "private_subnets_only": True,
    "network_acls": "restrictive",
    "security_groups": "least_privilege",
    "waf_enabled": True,
    "ddos_protection": True
}

# 2. Infrastructure hardening
infrastructure_security = {
    "container_scanning": True,
    "runtime_protection": True,
    "host_intrusion_detection": True,
    "file_integrity_monitoring": True,
    "privileged_container_restrictions": True
}

# 3. Secrets management
from langtrain.security import SecretsManager

secrets = SecretsManager(
    provider="aws_secrets_manager",  # or "vault", "azure_kv"
    encryption="AES-256",
    rotation_schedule="30d",
    access_logging=True
)

# Store sensitive configuration
secrets.store_secret(
    name="database_password",
    value="super_secure_password",
    tags={"environment": "production", "service": "database"}
)

# 4. Security monitoring setup
monitoring_config = {
    "log_aggregation": "centralized",
    "siem_integration": True,
    "real_time_alerts": True,
    "behavioral_analytics": True,
    "threat_hunting": "automated"
}

# 5. Backup and disaster recovery
backup_config = {
    "backup_frequency": "4h",
    "backup_encryption": True,
    "cross_region_replication": True,
    "point_in_time_recovery": True,
    "disaster_recovery_testing": "monthly"
}

# 6. Regular security tasks (automation recommended)
security_tasks = [
    "vulnerability_scanning_weekly",
    "access_review_monthly", 
    "penetration_testing_quarterly",
    "security_training_quarterly",
    "incident_response_drill_biannual",
    "compliance_audit_annual"
]

# 7. Deployment security checklist
deployment_checklist = {
    "secure_defaults": True,
    "unnecessary_services_disabled": True,
    "debug_mode_disabled": True,
    "error_messages_sanitized": True,
    "security_headers_enabled": True,
    "rate_limiting_configured": True,
    "input_validation_comprehensive": True,
    "output_encoding_enabled": True
}

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Security ArchitectureAccess Control & AuthenticationData Protection & PrivacyThreat Detection & ResponseCompliance & GovernanceSecurity Best Practices