AI Security Policy Engine for Proactive OT Network Protection
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Solution Overview
Problem
Existing security policies for operational technology (OT) networks are often manually created and reactive, relying on administrator judgment, and fail to proactively address vulnerabilities, especially in the rapidly changing landscape of IT and OT security.
Innovation Solution
A machine learning-based security policy development engine that generates recommended security policies using design artifacts and run-time data from industrial automation systems, allowing for proactive and data-driven security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security policies are manually created by network administrators, then the policies can be customized based on human judgment and experience, but the development process becomes reactive and fails to proactively identify new vulnerabilities
Solution Approach 1:
The system enables self-service by allowing the security policy development engine to automatically generate and update security policies without requiring manual intervention from network administrators. The engine autonomously analyzes design artifacts and runtime data to create proactive security policies that adapt to emerging vulnerabilities.
Solution Approach 2:
The patent replaces the mechanical process of manual policy creation with an automated machine learning-based engine. This substitution transforms the reactive manual process into a proactive automated system that continuously analyzes system data and generates security policies based on learned patterns and vulnerabilities.
2Adaptability or versatility
If security policies are manually developed, then administrator expertise can be applied, but the process is time-consuming and cannot keep pace with rapidly changing security threats
Solution Approach 1:
The security policy development engine operates continuously, constantly analyzing design artifacts and runtime data to generate and update security policies. This continuous operation ensures the system keeps pace with rapidly changing security threats without the interruptions inherent in manual development processes.
Solution Approach 2:
The system implements feedback loops where runtime data from the industrial automation system is continuously fed back to the security policy development engine. This feedback mechanism allows the engine to learn from actual system behavior and emerging threats, dynamically adjusting security policies to maintain adaptability.
3Reliability
If reactive security policy approaches are used, then existing vulnerabilities can be addressed, but new vulnerabilities remain undetected until they are exploited
Solution Approach 1:
The security policy development engine performs preliminary actions by proactively analyzing design artifacts and runtime data to identify potential vulnerabilities before they can be exploited. The system generates security policies that prevent future attacks rather than merely responding to discovered vulnerabilities.
Solution Approach 2:
The patent introduces an intermediary layer between the industrial automation system and security threats. The security policy development engine acts as a mediator that continuously monitors system data, identifies vulnerability patterns, and generates preventive security policies before threats can exploit weaknesses.
Data Source
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AI summary
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.