eHealthGPT^beta

Core Functionalities

Security & Privacy

eHealthGPT^beta Protects Your Data

Customer prompts or data are not used for training models
Data encryption at rest (AES-256) and in transit (TLS 1.2+)
SOC 2 compliant

eHealthGPT^beta uses standard industry practices and measures that would typically be implemented in state-of-the-art AI models like OpenAI’s ChatGPT-4 Turbo. Here's a list of ten technical security measures that we employ:

• End-to-End Encryption: All data transmitted between the user and the LLM is encrypted using protocols such as TLS (Transport Layer Security), ensuring that data in transit cannot be easily intercepted or tampered with.
• Data-at-Rest Encryption: Stored data, including logs and training data, is encrypted using strong encryption standards such as AES-256 to protect against unauthorized access if the physical security of the storage system is compromised.
• Access Control Lists (ACLs): To limit access to the AI's infrastructure, ACLs are used, allowing only authorized services and personnel to interact with sensitive components based on the principle of least privilege.
• Multi-Factor Authentication (MFA): For internal access to AI systems, MFA is mandatory for all personnel to reduce the risk of unauthorized access due to compromised credentials.
• Regular Penetration Testing: Security teams conduct regular penetration testing to identify and remediate potential vulnerabilities within the system.
• Automated Threat Detection: The use of automated security tools, such as intrusion detection systems (IDS) and intrusion prevention systems (IPS), monitor for and respond to signs of malicious activity in real-time.
• Robust Logging and Monitoring: Detailed logs are maintained for all interactions with the AI, and automated monitoring tools are used to alert to suspicious activity patterns.
• Secure Development Lifecycle (SDL): Adherence to SDL practices, including code reviews, static and dynamic code analysis, and integrating security into the software development lifecycle prevent vulnerabilities from being introduced.
• Data Anonymization and Minimization: Implementation of techniques to anonymize and minimize the data collected and used for training and interaction purposes to protect user privacy.
• Incident Response Plan: A comprehensive incident response plan is in place to quickly address any security breaches, including predefined processes for containment, eradication, recovery, and communication with affected parties.

Recognizing the importance of patient privacy and data security, eHealthGPT^beta adheres to stringent privacy standards. The AI-powered assistant does not store any personal health information and complies with healthcare regulations, including the Health Insurance Portability and Accountability Act (HIPAA) in the USA, The General Data Protection Regulation (GDPR) in the EU, and The Personal Information Protection Law (PIPL) in China. With eHealthGPT^beta, healthcare professionals can confidently communicate sensitive information, knowing that privacy and security are paramount.

Learn more about the eHealthGPT^beta Data Security