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Dispel Launches Industrial Data Streaming to Power AI Productivity, Unlock Predictive Maintenance, and Accelerate Patching at Scale

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New capability enables secure, real-time data streaming from legacy OT systems to modern analytics platforms – driving uptime, AI-readiness, and operational resilience.

AUSTIN, TX / ACCESS Newswire / July 15, 2025 / Dispel, the leader in Secure Remote Access (SRA) for Operational Technology (OT), today announced the launch of Industrial Data Streaming, a new capability built into the Dispel Zero Trust Engine platform. The release marks a major advancement in secure, real-time data transfer for industrial environments-bridging the gap between legacy systems and modern AI-driven operations, while accelerating OT efficiency, resilience, and insight at scale.

Dispel Announces Industrial Data Streaming
Industrial Data Streaming, a new capability built into the Dispel Zero Trust Engine platform

Industrial teams in manufacturing, energy, utilities, and OEM sectors generate massive volumes of data from sensors, HMIs, control systems, and machines. But without a secure, scalable way to stream that data in real time, predictive maintenance (PdM) stalls, patching slows, and AI initiatives remain siloed.

Dispel’s Industrial Data Streaming solves this challenge-enabling real-time transfer of logs and sensor data, supporting secure patching, proactive maintenance, supply chain optimization, and continuous operational insights across distributed industrial environments. This leads to greater operational efficiency and faster time-to-value across distributed environments.

“Operational efficiency and productivity are powered by secure access to real-time data,” said Ethan Schmertzler, Co-CEO of Dispel. “We designed Industrial Data Streaming to help organizations cut downtime, standardize operations, and turn AI potential into real-world results.”

Industrial Data Streaming: Secure, Scalable, Real-Time

Today’s industrial environments produce vast volumes of data-but most still rely on siloed data, delaying decision-making and limiting AI readiness.

Dispel’s Industrial Data Streaming helps organizations:

  • Standardize operations – Unify data across sites for faster rollups and enterprise-wide visibility

  • Simplify updates – Push patches and configurations across fleets securely

  • Enable predictive maintenance – Stream real-time data to PdM and AI platforms

  • Scale with ease – Route data from multiple sources to many destinations

  • Accelerate compliance – Centralize data for easier audits and reporting

  • Support any data type – Format- and protocol-agnostic transport layer

  • Modernize legacy systems – Extend infrastructure life and unlock new value

Today’s industrial environments are investing heavily in AI and digital transformation initiatives. But without reliable access to real-time OT data, predictive insights and automated updates, backup transfers, and real time calibration remain out of reach.

Dispel removes that barrier-streaming secure, actionable data from OT environments to analytics tools, patching systems, and AI platforms in real time.

To learn more about Industrial Data Streaming, or to request a demo, visit dispel.com

About Dispel
Dispel redefines how industrial organizations connect to OT. The Dispel Zero Trust Engine establishes a secure, scalable connectivity layer across all makes, models, and generations of equipment-enabling OT secure remote access, industrial data streaming, and integrated threat monitoring, even in the most complex environments. Dispel OTFusion accelerates digital transformation by eliminating silos and unifying fragmented OT DMZs. Founded in 2015, Dispel pioneered network-level Moving Target Defense (MTD), holds 43+ patents, and today protects over $500 billion in manufactured goods annually while securing remote access for 54 million utility users worldwide. Designed for how OT really works. Learn more at dispel.com

Contact Information

Mark Lennon
press@dispel.com

SOURCE: Dispel

View the original press release on ACCESS Newswire

Staff

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