20240028009. SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE-BASED SECURITY POLICY DEVELOPMENT simplified abstract (Rockwell Automation Technologies, Inc.)

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SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE-BASED SECURITY POLICY DEVELOPMENT

Organization Name

Rockwell Automation Technologies, Inc.

Inventor(s)

Timothy C. Mirth of Hudson OH (US)

Taryl J. Jasper of Concord Township OH (US)

Terence S. Tenorio of Solon OH (US)

Thaddeus A. Palus of Denver CO (US)

SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE-BASED SECURITY POLICY DEVELOPMENT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240028009 titled 'SYSTEMS AND METHODS FOR ARTIFICIAL INTELLIGENCE-BASED SECURITY POLICY DEVELOPMENT

Simplified Explanation

The method described in the patent application involves the following steps:

  • Receiving data from an enterprise network, which includes design artifacts and run time data of one or more industrial automation systems operated by the enterprise.
  • Inputting the data into a machine learning-based security policy development engine.
  • The security policy development engine generates a set of recommended security policies for the enterprise based on the data.
  • Receiving the set of recommended security policies for the industrial automation systems.
  • Transmitting the set of recommended security policies to the enterprise.

Potential applications of this technology:

  • Enhancing the security of industrial automation systems in enterprises.
  • Improving the protection of sensitive data and assets in industrial automation systems.
  • Streamlining the process of developing security policies for industrial automation systems.

Problems solved by this technology:

  • Traditional methods of developing security policies for industrial automation systems may be time-consuming and prone to human error.
  • Ensuring the security of industrial automation systems is crucial to prevent unauthorized access, data breaches, and potential disruptions in operations.

Benefits of this technology:

  • Automation of the security policy development process saves time and reduces the risk of human error.
  • Machine learning-based approach can analyze large amounts of data and provide more accurate and effective security policies.
  • Improved security measures help protect industrial automation systems from cyber threats and potential financial losses.


Original Abstract Submitted

a method includes receiving, from an enterprise network, data associated with one or more industrial automation systems operated by an enterprise, wherein the data includes design artifacts of the one or more industrial automation systems, run time data collected from the one or more industrial automation systems, or both, inputting the data to a machine learning-based security policy development engine to generate a set of recommended security policies for the enterprise based on the data, receiving the set of recommended security policies for the one or more industrial automation systems output by the security policy development engine, wherein the set of recommended security policies define access, use, or both, of the one or more industrial automation systems operated by the enterprise; and transmitting the set of recommended security policies to the enterprise.

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