Enterprise AI and ML for OT Edge Device Optimization
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Edge Devices – OT

Putting breakthrough intelligence into today’s OT edge devices. MicroAI™ is breaking new ground in empowering edge devices with next-generation intelligence, performance, cost-efficiency, and security. MicroAI’s Edge-native AI technology has bridged the gap between cost-prohibitive OT edge device enhancement and affordable AI optimization.

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OPERATIONAL TECHNOLOGY

Edge Devices in OT

Over the past decade, OT edge devices have grown from niche applications to widespread adoption. Edge devices are now integral components within all IIoT-enabled OT infrastructures. Devices such as monitors, sensors, scanners, and converters are being used to connect disparate networks and to translate protocols. Edge-native AI is the next evolution in OT device optimization.

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Escalating Requirements

Industry 4.0 combined with continued developments within IIoT (industrial internet of things), are creating new demands on the functions of OT edge devices. New requirements include:

  • Edge-based intelligence
  • Deeper observability
  • Faster Insights
  • Predictive analytics
  • Improved security
  • Reduced cost

Traditional solutions for edge device management will not meet the demands of the increasingly complex and diverse IIoT-enabled OT ecosystem.

Edge AI Optimizes OT Edge Devices

The volumes of edge device data are increasing at exponential rates. Processing massive volumes of data in the cloud is inefficient and cost prohibitive. MicroAI brings intelligence and computational power closer to the sources of data. The advantages of MicroAI’s Edge-native AI:

  • Autonomous Training

    AI and ML algorithms are embedded and trained at the device level. Data is processed locally and autonomously instead of in the cloud.

  • Increased Speed

    Critical data is processed locally, at the device, eliminating cloud transmission latency. Analytics produced in milliseconds.

  • Deeper Observability

    Penetrating insights into the performance, health, and security of the edge device (ability to respond to conditions 400x faster than previously possible).

  • Predictive Insights

    Recursive analytics that produce predictive insights into future behavior, maintenance requirements, and productivity. Insights that power the evolution to a predictive device management state.

  • Lower Cost

    Reduced cloud dependence directly equates to massive reductions in data costs. Savings in the hundreds of thousands of dollars.

  • Improved Edge Device infrastructure

    Device-specific and device ecosystem intelligence that enables quick identification and corrective action on devices that are performing below their normal state.

OT Edge Device Cyber-Security

IT infrastructure are under constant threat of Zero-day cyber-attack (Ransomware, DDoS, Phishing, Cloud Breach, etc.). These attacks can have far reaching and long-lasting implications.

  • Operational Disruption

    Cyber intrusions can cripple the performance of edge devices for extended periods of time.

  • Loss of Data

    Critical asset and operational data can be held for ransom or permanently lost.

  • Ecosystem Contamination

    Cyber-attacks can expand to penetrate external targets (partners, suppliers, customers).

  • Financial Degradation

    Large-scale attacks can degrade customer confidence, reduce revenue, damage market reputation, and expose the operator to legal risks.

MicroAI’s Edge-native AI solutions provide Zero-Trust cyber-security for IT edge devices:

  • Reduced Cloud Dependence

    Edge-native security allows all critical data to be collected, synthesized, and analyzed locally. Critical IT edge device data is not exposed to cloud transmission, significantly reducing its exposure to cyber-attack.

  • Personalized Security

    Ability to customize security protocols on a device-by-device level to accommodate specific conditions for individual devices or groups of devices.

  • Quicker Alerts and Mitigation

    Localized, device-specific, security that provides quicker notification of security breach and faster activation of mitigation actions.

  • Predictive Security

    Predictive analytics produce actionable insights into future threats. Enables a transition from reactive to predictive security.

  • Simple Integration

    Quickly onboard and validate security protocols into devices within the IT ecosystem. Eliminates the need for expensive external hardware and costly data labeling.

MicroAI Use Cases

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Interested in how MicroAI can benefit you?

 MicroAI AtomML brings big infrastructure intelligence down into a single piece of equipment or device.

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