AI Data Center Case Study
Building a Secure Foundation for AI Data: Enhancing Big Data Transfer Performance and Data Governance Resilience with OmniStor
“AI development requires not only powerful computing, but also secure data governance. With OmniStor, we have overcome transmission bottlenecks in high-latency networks. More importantly, it integrates ‘security’ and ‘performance’ into a single platform.”
In the era of artificial intelligence and big data computing, the speed and security of data movement are key determinants of R&D outcomes. Large-scale AI data computing platforms carry massive model parameters, images, and research datasets. If relying solely on traditional transmission methods, they are often constrained by network latency and cybersecurity vulnerabilities.
Case Summary
- Implements zero-trust governance to strengthen AI data access control, ensuring sensitive data does not leak.
- Overcomes network transmission bottlenecks with an automatic retry mechanism, improving the efficiency and integrity of AI data transfers.
- Provides comprehensive access logging to meet AI data compliance and audit requirements.
Client Overview
The organization is an AI data center providing large-scale computing resources, dedicated to the development and operation of artificial intelligence and cloud computing platforms. Its service users include leading domestic universities, research institutions, and startups. To address the growing demand for data exchange, the center has implemented OmniStor as its core platform for secure data management and high-speed data transfer.
Customer Challenges
While operating a large-scale AI platform, the organization faced several critical data management pain points:
1. Security concerns over sensitive data flow
AI training datasets often contain highly sensitive information, including proprietary and mission-critical data. During frequent data exchanges, the lack of end-to-end encryption and robust identity verification mechanisms significantly increases the risk of data leakage and malicious tampering.
Efficiency limitations of traditional transmission protocols
AI training requires frequent movement of massive datasets across distributed nodes. Traditional TCP-based transmission suffers from severe latency and throughput degradation under cross-region networks or packet loss conditions, leading to inefficient utilization of high-cost computing resources while waiting for data transfers.
3. Lack of unified data governance and audit trails
Due to the complexity of AI workloads, diverse data sources, and multiple teams managing different projects, there was previously no automated mechanism to track “who accessed what data and when.” This made it difficult to meet compliance and audit requirements for big data governance under security regulations.
Solutions
To build a secure and high-efficiency AI development environment, the center implemented the OmniStor platform, integrating data transmission and security governance into a unified solution.
- Zero-Trust Security Architecture: Protecting Critical Research Assets
OmniStor serves as a data security integration hub within AI computing environments, implementing multi-layer protections across identity, files, and devices to ensure AI data remains secure and controllable during upload and sharing. Through API key authentication and TLS encryption, only authorized computing nodes or researchers can access the data, with end-to-end encryption applied throughout transmission. After transfer completion, automatic verification is performed to ensure data integrity, preventing tampering and safeguarding the accuracy of AI training outcomes.
- High-Performance Transfer Technology: Accelerating AI R&D Workflows
OmniStor supports multiple acceleration protocols, including both UDP and TCP, dynamically adapting to network conditions. In real-world testing, it delivers stable throughput exceeding 1 Gbps on a single connection, significantly speeding up model parameter synchronization and large-scale dataset loading. With built-in automatic retry and interruption recovery mechanisms, it ensures data integrity even under unstable network conditions. OmniStor provides comprehensive technical support, eliminating data movement as a bottleneck between AI platform nodes.
- Tracking and Monitoring for AI Data Governance
OmniStor provides complete access logs and audit trails, ensuring that data provenance and ownership can be verified throughout AI training and inference processes. All actions are fully traceable and auditable, enabling organizations to maintain consistent and accountable data governance while adopting AI applications.。
Implementation Outcomes
This deployment has established a strong foundation of digital resilience for a national-level AI computing platform. On the performance side, it successfully overcomes long-distance data transmission bottlenecks, enabling smoother cross-institutional research collaboration and significantly shortening AI model development cycles.
On the security governance side, OmniStor integrates data transmission into the overall cybersecurity control framework. Through detailed audit logs and encryption technologies, the center not only ensures data confidentiality and integrity but also gains robust risk traceability capabilities.The system has now become a critical foundation supporting AI research institutions in data analytics and big data asset protection, successfully implementing an intelligent defense architecture that balances both high-speed data transfer and comprehensive data governance.
Key Application Outcomes
- AI Data Access Control:Provides granular, hierarchical permission governance for AI data access and research projects, ensuring that sensitive training datasets are only accessible to authorized personnel.
- High-Performance Data Transfer:Supports multiple acceleration technologies including UDP and TCP. Real-world testing demonstrates stable single-connection throughput exceeding 1 Gbps, significantly reducing the time required for PB-scale data synchronization.
- Robust Data Security Protection:Built-in API key authentication, end-to-end encryption, and integrity verification ensure that critical research assets remain protected from threats during node-to-node transmission.
- Comprehensive Audit and Compliance Monitoring:Delivers detailed access and transmission logs to meet stringent big data governance and cybersecurity compliance requirements of national-level institutions.