Our Privacy Commitment
We design AI systems with privacy by default - every model, dataset, and algorithm incorporates differential privacy, secure computation, and decentralized processing frameworks.
Data Anonymization
Every dataset ingested through our systems undergoes advanced tokenization and k-anonymity protections. We employ state-of-the-art homomorphic encryption for model training.
- • Adaptive differential privacy (ε ≤ 0.001 for sensitive datasets)
- • Secure multi-party computation for collaborative training
- • Automated data retention policies aligned with GDPR and HIPAA
Private Inference
All AI inference operations use encrypted computing frameworks including fully homomorphic encryption (FHE) and private computation enclaves.
- • FHE-based inference without data decryption
- • Trusted execution environments (Intel SGX) support
- • Zero-knowledge proofs for model integrity
Privacy Audit Transparency
Annual Security Audits
Independent penetration testing by Kudelski, Mandiant and other leading security firms verify our privacy protections.
Privacy Impact Assessments
We conduct comprehensive privacy risk assessments for all new AI features across our product suite.
Open Source Tools
Our privacy toolkit includes open-source solutions like privacy-preserving ML frameworks and encrypted data exchange protocols.
Current Privacy Initiatives
Innovating at the intersection of AI and data privacy with open-source projects and academic partnerships.
Federated Learning Framework
Enabling model training across decentralized devices without exposing raw data through secure aggregation protocols and encrypted gradient updates.
Privacy Budget Management
Automated ε (epsilon) budget allocation for differential privacy across machine learning workflows maintains privacy guarantees while optimizing model performance.