Riverbed’s Quantum-Secure Data Express Service Aims to Unlock AI’s Full Potential

The AI Data Movement Challenge

As organizations worldwide race to implement artificial intelligence solutions, they’re encountering a critical bottleneck: moving massive datasets efficiently and securely. According to Riverbed’s comprehensive 2025 Global AI Research, which surveyed 1,200 organizations, an overwhelming 90% identify data movement as vital to their AI success. Despite this recognition, the research reveals that only 10% of AI projects successfully transition from pilot mode to enterprise-wide deployment, primarily due to data quality issues and sluggish data transfer speeds.

The survey further indicates that 75% of organizations plan to establish dedicated AI data repository strategies, highlighting the growing awareness that effective data management is the foundation of successful AI implementation. This strategic shift comes as companies recognize that AI initiatives require not just computational power, but sophisticated data orchestration capabilities.

Riverbed’s Oracle-Powered Solution

Riverbed has responded to this critical market need with its Data Express Service, initially deployed on Oracle Cloud Infrastructure. The service represents a significant advancement in data movement technology specifically designed to address the unique demands of AI workloads. Sachin Menon, Oracle’s vice president of cloud engineering, emphasized the importance of this development, stating: “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.”

Quantum-Secure Data Transfer

One of the most innovative aspects of Riverbed’s solution is its implementation of post-quantum cryptography (PQC) to secure petabyte-scale datasets during transfer through secure VPN tunnels. This approach addresses what Aras from Riverbed describes as the “harvest-now, decrypt-later” security 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 states, highlighting the forward-thinking security measures built into the service. This quantum-resistant security framework ensures that organizations can confidently move sensitive training data and AI models without compromising future security.

Technical Architecture and Performance

The Data Express Service builds upon Riverbed’s established SteelHead acceleration platform running RiOS 10 software, but with significant cloud-optimized enhancements. According to technical specifications, the service delivers substantially higher data retrieval rates, accelerated network data movement, and improved data write performance through several key innovations:

Highly performant data mover instances optimized for cloud environments
Instance parallelization capabilities that scale with workload demands
Matched network fabric configurations tailored to specific cloud providers

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 departure from one-size-fits-all solutions and reflects the nuanced understanding required for modern multi-cloud environments.

Broader Industry Context

The launch of Riverbed’s service occurs alongside other significant industry developments in technology infrastructure. While focused on different sectors, these innovations collectively demonstrate the ongoing evolution of enterprise technology solutions. Similarly, recent technology announcements across various domains show how companies are pushing performance boundaries in their respective fields.

Application Across AI Workflows

Riverbed’s service specifically targets three critical AI use cases that have traditionally struggled with data movement challenges:

AI Model Training: Accelerating the transfer of massive training datasets to computational resources
Inference Operations: Supporting real-time AI applications that require rapid access to updated models
Agentic AI Applications: Enabling emerging autonomous AI systems that demand continuous data synchronization

This comprehensive approach ensures that organizations can support the entire AI lifecycle, from initial development through production deployment and ongoing optimization.

Multi-Cloud Strategy and Availability

While initially deployed on Oracle Cloud Infrastructure, Riverbed has designed the Data Express Service with a multi-cloud future in mind. The company confirmed that the service will eventually orchestrate data movement across AWS, Azure, and Google Cloud Platform, as well as on-premises data centers. This strategic direction aligns with the reality that most enterprises operate in hybrid and multi-cloud environments.

The timing of this launch coincides with other related innovations in enterprise technology, particularly in data management and security. General availability of Riverbed’s Data Express Service is planned for Q4 2025, giving organizations time to prepare their AI data strategies and infrastructure requirements.

Implications for AI Adoption

The introduction of specialized data movement services like Riverbed’s Data Express represents a maturation of the AI infrastructure market. As organizations move beyond experimental AI projects and toward enterprise-wide deployments, the ability to efficiently and securely manage data at scale becomes increasingly critical. This development suggests that the industry is recognizing that AI success depends as much on data logistics as it does on algorithmic innovation.

The convergence of high-performance data movement, quantum-resistant security, and cloud-native architecture positions Riverbed’s solution to potentially accelerate AI adoption across industries by removing one of the most persistent barriers to implementation.

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