The AI Data Movement Challenge
As organizations race to implement artificial intelligence solutions, they’re encountering a critical bottleneck that threatens to derail their initiatives. According to Riverbed’s comprehensive 2025 Global AI Research, while 75% of 1,200 surveyed organizations plan to establish AI data repository strategies, a staggering 90% identify data movement as vital to their AI success. The research reveals a concerning statistic: only one in ten AI projects progresses beyond pilot mode into enterprise-wide deployment, primarily due to data quality issues and sluggish data movement capabilities.
The challenge isn’t merely technical—it’s becoming a strategic business imperative. As companies accumulate massive datasets for training and inference operations, the ability to move this information securely and efficiently between cloud environments and on-premises infrastructure has emerged as a decisive factor in AI project success. This challenge is particularly acute for organizations working with petabyte-scale datasets that require both speed and robust security during transfer.
Riverbed’s Oracle-Powered Solution
Riverbed’s response to this growing challenge comes in the form of its Data Express Service, now deployed on Oracle Cloud Infrastructure (OCI). The service represents a significant advancement in data movement technology, specifically engineered to address the unique demands of AI workloads. According to Sachin Menon, Oracle’s vice president of cloud engineering, “Customers are looking for faster, more secure ways to move massive datasets so they can bring AI initiatives to life. With Riverbed Data Express Service deployed on OCI, organizations will be able to accelerate time to value, reduce costs, and help ensure that their data remains protected.”
The timing of this solution aligns with broader industry developments in AI infrastructure and data management. As enterprises increasingly rely on distributed computing environments, the need for optimized data movement solutions has never been more critical.
Quantum-Secured Data Transfer
Perhaps the most innovative aspect of Riverbed’s approach is its integration of post-quantum cryptography (PQC) to secure data transfers. This forward-looking security measure addresses what Aras from Riverbed describes as the “harvest-now, decrypt-later” threat—where encrypted data is intercepted and stored for future decryption once quantum computers become sufficiently powerful.
“The time for preventing harvest-now, decrypt-later is now,” Aras emphasizes. The service utilizes secure VPN tunnels enhanced with PQC to move petabyte-scale datasets while ensuring customer data remains protected throughout the transfer process. This security approach reflects the growing importance of critical infrastructure protection in an increasingly interconnected digital landscape.
Technical Architecture and Performance
The Data Express Service builds upon Riverbed’s established SteelHead acceleration platform running RiOS 10 software, but introduces several cloud-optimized enhancements. According to technical specifications, the service delivers “much higher data retrieval, data movement across the network, and data write rates” through several key innovations:
- Highly performant data mover instances
- Instance parallelization capabilities
- Matched network fabric configurations
Aras explains that “the design is tailored for each cloud, to ensure maximal performance can be achieved using cloud-specific product adjustments.” This cloud-aware architecture represents a significant evolution from traditional data movement solutions that often struggle with the unique characteristics of different cloud environments.
The technology’s performance characteristics align with broader related innovations in high-performance computing infrastructure, though applied specifically to data transfer challenges.
Multi-Cloud Strategy and Deployment
While initially deployed on Oracle Cloud Infrastructure, Riverbed has designed the Data Express Service with a comprehensive multi-cloud strategy. The service will orchestrate data movement across AWS, Azure, and Google Cloud Platform, as well as on-premises data centers. This approach acknowledges the reality that most enterprises operate in hybrid and multi-cloud environments, requiring seamless data mobility across different platforms.
The planned general availability in Q4 2025 positions Riverbed to address what many analysts predict will be a period of accelerated AI adoption across industries. This timeline coincides with other significant market trends in technology infrastructure and deployment.
Use Cases and Enterprise Impact
The Riverbed service specifically targets use cases spanning AI model training, inference operations, and emerging agentic AI applications. For organizations struggling to move training data to GPU-rich environments or distribute models across global deployments, the Data Express Service could significantly reduce latency and improve overall AI system performance.
The solution arrives amid increasing regulatory complexity, including recent technology workforce considerations that impact how organizations structure their AI initiatives. By addressing both the performance and security aspects of data movement, Riverbed aims to remove key barriers to AI implementation at scale.
As enterprises continue to navigate the complexities of AI deployment, solutions like Riverbed’s Data Express Service represent a critical piece of the infrastructure puzzle. With its combination of quantum-resistant security, cloud-optimized performance, and multi-cloud capabilities, the service addresses what has become one of the most persistent challenges in enterprise AI—getting the right data to the right place at the right time, securely and efficiently. For more detailed coverage of this development, see the comprehensive analysis of Riverbed’s Oracle-powered solution.
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Note: Featured image is for illustrative purposes only and does not represent any specific product, service, or entity mentioned in this article.
