End-to-end encryption
Data is encrypted in transit (TLS 1.2+) and at rest (AES-256). Encryption keys are managed under separation-of-duties policies.
At AgenticLab, security and privacy are foundational requirements — not optional add-ons. ThesisMentor AI is currently in internal-prototype stage; this page summarizes the **design principles** and standards we are working toward as we prepare for real-world deployment. Third-party certifications (ISO 27001, SOC 2, etc.) are not yet in place; we will publish updates transparently as those certifications are achieved.
Data is encrypted in transit (TLS 1.2+) and at rest (AES-256). Encryption keys are managed under separation-of-duties policies.
The system enforces RBAC with four primary roles. Every access is logged for audit purposes.
Customers retain full ownership of their data. Export, anonymization, and deletion requests are honored in line with GDPR principles.
Automated periodic backups with verified recovery points. Disaster Recovery procedures are tested on a regular cadence.
Every AI interaction is logged. Customer content is not used to train foundation models.
Designed to operate on enterprise-grade cloud infrastructure (Google Cloud / AWS / Azure) — leveraging the provider's infrastructure-level SOC 2 and ISO 27001 certifications.
We apply industry-standard security practices throughout our product development lifecycle:
To report a security issue, request compliance information, or discuss a Data Processing Agreement (DPA), please contact:
hmphuc@agenticlab.vn