Addressing the Critical AI Data Challenge
As organizations worldwide race to implement artificial intelligence solutions, they’re encountering a significant roadblock: the data bottleneck. According to Riverbed’s comprehensive 2025 Global AI Research, which surveyed 1,200 organizations, an overwhelming 75% plan to establish dedicated AI data repository strategies. More importantly, 90% of these organizations recognize data movement as absolutely vital to their AI success. The research reveals a sobering statistic – only 10% of AI projects successfully transition from pilot mode to enterprise-wide deployment, primarily due to data quality issues and sluggish data transfer capabilities.
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Quantum-Secured Data Acceleration
Riverbed’s response to this challenge comes in the form of their Data Express Service, which leverages post-quantum cryptography (PQC) to securely move petabyte-scale datasets through protected VPN tunnels. This advanced security approach specifically addresses the emerging threat of “harvest-now, decrypt-later” attacks, where encrypted data is intercepted and stored for future decryption once quantum computers achieve sufficient power. The technology builds upon Riverbed’s established SteelHead acceleration platform running RiOS 10 software, creating a robust foundation for secure data transmission.
“The time for preventing harvest-now, decrypt-later is now,” emphasized Aras from Riverbed, highlighting the urgency of addressing quantum-era security threats before they materialize.
Cloud-Optimized Performance Architecture
The service’s architecture represents a significant advancement in cloud data movement technology. Through highly performant data mover instances, instance parallelization, and matched network fabric configurations, Riverbed’s cloud-optimized design delivers substantially higher data retrieval rates, accelerated network movement, and improved data write performance. “The design is tailored for each cloud to ensure maximal performance can be achieved using cloud-specific product adjustments,” Aras explained, emphasizing the customized approach for different cloud environments.
Sachin Menon, Oracle’s vice president of cloud engineering, reinforced the service’s value proposition: “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.”
Comprehensive AI Application Support
The Riverbed service addresses a wide spectrum of AI use cases, including:
- AI model training requiring massive dataset transfers
- Inference operations across distributed environments
- Emerging agentic AI applications with complex data dependencies
- Cross-platform data orchestration for hybrid AI deployments
Multi-Cloud Deployment Strategy
While initially deployed on Oracle Cloud Infrastructure, Riverbed has confirmed that the Data Express Service will eventually orchestrate data movement across all major cloud platforms, including AWS, Azure, and Google Cloud Platform, as well as on-premises data centers. This multi-cloud approach reflects the reality of modern enterprise IT environments, where data often spans multiple platforms and locations.
The timing of this launch coincides with significant industry developments across multiple sectors, demonstrating how technological innovation is driving transformation beyond traditional tech boundaries. Similarly, recent regulatory clarifications and academic funding discussions highlight the broader ecosystem in which AI and data technologies are evolving.
Market Availability and Future Roadmap
Riverbed has announced general availability for the Data Express Service planned for Q4 2025, positioning the solution to address the growing demand for enterprise-grade AI data movement capabilities. As organizations continue to navigate the complexities of AI implementation, services like Riverbed’s Data Express represent critical infrastructure components that could determine the success or failure of AI initiatives across industries.
The emergence of such specialized data movement services signals a maturation of the AI ecosystem, where performance, security, and interoperability become non-negotiable requirements rather than desirable features. As quantum computing capabilities advance and AI applications become more sophisticated, the ability to move massive datasets securely and efficiently may well become the defining competitive advantage for organizations seeking to harness AI’s full potential.
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