Security and Privacy at AIGP Health

Security Governance

AIGP Health's security framework is built on industry best practices and tailored for AI-powered healthcare solutions. Our security policies and controls are designed to protect sensitive healthcare data while enabling innovation.

Core Security Principles

• Zero Trust Architecture: Access is granted based on strict identity verification and least privilege principles, with continuous monitoring of all interactions.
• Defense in Depth: Multiple layers of security controls protect our AI systems and data across network, application, and data levels.
• AI Security First: Specialized controls for AI model protection, including model versioning, input validation, and output monitoring.
• Continuous Improvement: Regular security assessments and updates to address emerging threats in the AI and healthcare technology landscape.

Compliance Journey

AIGP Health is actively working toward obtaining industry-standard security certifications including SOC 2 Type II and ISO 27001 to demonstrate our commitment to security excellence.

Data Protection

Data at Rest

• All data stores with sensitive information are encrypted using AES-256 encryption
• AI model parameters and training data are protected with additional field-level encryption
• Database encryption ensures data protection even with physical access
• Automated backup encryption for all critical data repositories

Data in Transit

• TLS 1.3 encryption for all data transmission across networks
• HSTS implementation for enhanced web security
• API communications secured with OAuth 2.0 and JWT tokens
• VPN protection for remote team access to development environments

AI Model Security

• Model versioning and integrity verification
• Input sanitization and validation for all AI interactions
• Output filtering to prevent sensitive data exposure
• Adversarial attack protection and monitoring

Product Security

Secure Development Lifecycle

• Security-first approach in all development phases
• Code review requirements for all changes
• Automated security testing in CI/CD pipelines
• Regular security training for development teams

Vulnerability Management

• Static Application Security Testing (SAST) during development
• Dynamic Application Security Testing (DAST) on running applications
• Software Composition Analysis (SCA) for dependency management
• Regular penetration testing by third-party security experts
• Continuous monitoring of the external attack surface

AI-Specific Security Testing

• Model robustness testing against adversarial inputs
• Bias detection and fairness assessments
• Privacy-preserving machine learning validation
• Model performance and drift monitoring

Cloud Infrastructure Security

AWS Security Implementation

• Infrastructure as Code (IaC) with security templates
• AWS Identity and Access Management (IAM) with least privilege access
• VPC isolation and network segmentation
• AWS CloudTrail for comprehensive audit logging
• AWS GuardDuty for threat detection and monitoring

Secret Management

• AWS Secrets Manager for application secrets
• AWS Key Management Service (KMS) for encryption key management
• Hardware Security Module (HSM) protection for sensitive keys
• Automated secret rotation and access logging

Monitoring and Incident Response

• 24/7 security monitoring and alerting
• Automated incident response procedures
• Security Information and Event Management (SIEM) integration
• Regular disaster recovery testing

Remote Work Security

Endpoint Protection

• Mobile Device Management (MDM) for all company devices
• Endpoint Detection and Response (EDR) solutions
• Mandatory disk encryption and screen lock policies
• Automatic security updates and patch management

Secure Remote Access

• Zero Trust Network Access (ZTNA) implementation
• Multi-factor authentication (MFA) for all systems
• Secure DNS filtering and malware protection
• VPN access with modern encryption protocols

Security Awareness

• Comprehensive security onboarding for all team members
• Regular phishing simulation and training
• AI and healthcare-specific security awareness programs
• Incident reporting and response training

Privacy and Compliance

Data Privacy Principles

• Privacy by design in all AI product development
• Data minimization and purpose limitation
• User consent management and transparency
• Right to deletion and data portability support

Healthcare Compliance Readiness

• HIPAA compliance preparation for healthcare data handling
• GDPR compliance for international data processing
• Regular privacy impact assessments
• Data retention and disposal policies

AI Ethics and Responsible AI

• Algorithmic bias testing and mitigation
• Explainable AI implementations
• Fairness and transparency in AI decision-making
• Regular ethical AI audits and assessments