Patent Publication Number: US-2016234240-A1

Title: Rules engine for converting system-related characteristics and events into cyber-security risk assessment values

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims the benefit of the filing date of U.S. Provisional Patent Application 62/113,075, filed Feb. 6, 2015, which is hereby incorporated by reference. 
    
    
     TECHNICAL FIELD 
     This disclosure relates generally to network security. More specifically, this disclosure relates to a rules engine for converting system-related characteristics and events into cyber-security risk assessment values. 
     BACKGROUND 
     Processing facilities are often managed using industrial process control and automation systems. Conventional control and automation systems routinely include a variety of networked devices, such as servers, workstations, switches, routers, firewalls, safety systems, proprietary real-time controllers, and industrial field devices. Often times, this equipment comes from a number of different vendors. In industrial environments, cyber-security is of increasing concern, and unaddressed security vulnerabilities in any of these components could be exploited by attackers to disrupt operations or cause unsafe conditions in an industrial facility. 
     SUMMARY 
     This disclosure provides a rules engine for converting system-related characteristics and events into cyber-security risk assessment values, including related systems and methods. A method includes receiving information identifying characteristics of multiple devices in a computing system and multiple events associated with the multiple devices. The method includes analyzing the information using multiple sets of rules. The method includes generating at least one risk assessment value based on the analyzing. The at least one risk assessment value identifies at least one cyber-security risk of the multiple devices. The method includes displaying the at least one risk assessment value in a user interface. 
     In some embodiments, the information is received from source data components that are associated with and collect data from the multiple devices. In some embodiments, the information is processed by a normalization component that formats the information to a common format according the type of the information. In some embodiments, the risk manager system also transmits cyber security risk information, corresponding to the analysis, to one or more target data components. In some embodiments, the risk manager system also converts cyber security risk information, corresponding to the analysis, into a format that can be processed by respective target data components. In some embodiments, the risk manager system also defines behaviors and applies the behaviors to the multiple sets of rules, the multiple sets of rules including at least one of time-based rules, cumulative rules, and impact rules. In some embodiments, the risk manager system also aggregates risk assessment values over a hierarchy of the multiple devices. 
     Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this disclosure, reference is now made to the following description, taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  illustrates an example industrial process control and automation system according to this disclosure; 
         FIG. 2  illustrates an example rule handling infrastructure for identifying security issues in industrial process control and automation systems or other systems according to this disclosure; and 
         FIG. 3  illustrates a flowchart of a process in accordance with disclosed embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     The figures, discussed below, and the various embodiments used to describe the principles of the present invention in this patent document are by way of illustration only and should not be construed in any way to limit the scope of the invention. Those skilled in the art will understand that the principles of the invention may be implemented in any type of suitably arranged device or system. 
     In the following discussion, “SIEM” refers to “Security Information and Event Management,” which denotes technology that provides real-time analysis of security alerts in a system. Also, “SCOM” refers to the System Center Operations Manager infrastructure monitoring software tool available from MICROSOFT CORPORATION. 
       FIG. 1  illustrates an example industrial process control and automation system  100  according to this disclosure. As shown in  FIG. 1 , the system  100  includes various components that facilitate production or processing of at least one product or other material. For instance, the system  100  is used here to facilitate control over components in one or multiple plants  101   a - 101   n . Each plant  101   a - 101   n  represents one or more processing facilities (or one or more portions thereof), such as one or more manufacturing facilities for producing at least one product or other material. In general, each plant  101   a - 101   n  may implement one or more processes and can individually or collectively be referred to as a process system. A process system generally represents any system or portion thereof configured to process one or more products or other materials in some manner. 
     In  FIG. 1 , the system  100  is implemented using the Purdue model of process control. In the Purdue model, “Level  0 ” may include one or more sensors  102   a  and one or more actuators  102   b . The sensors  102   a  and actuators  102   b  represent components in a process system that may perform any of a wide variety of functions. For example, the sensors  102   a  could measure a wide variety of characteristics in the process system, such as temperature, pressure, or flow rate. Also, the actuators  102   b  could alter a wide variety of characteristics in the process system. The sensors  102   a  and actuators  102   b  could represent any other or additional components in any suitable process system. Each of the sensors  102   a  includes any suitable structure for measuring one or more characteristics in a process system. Each of the actuators  102   b  includes any suitable structure for operating on or affecting one or more conditions in a process system. 
     At least one network  104  is coupled to the sensors  102   a  and actuators  102   b . The network  104  facilitates interaction with the sensors  102   a  and actuators  102   b . For example, the network  104  could transport measurement data from the sensors  102   a  and provide control signals to the actuators  102   b . The network  104  could represent any suitable network or combination of networks. As particular examples, the network  104  could represent an Ethernet network, an electrical signal network (such as a HART or FOUNDATION FIELDBUS network), a pneumatic control signal network, or any other or additional type(s) of network(s). 
     In the Purdue model, “Level  1 ” may include one or more controllers  106 , which are coupled to the network  104 . Among other things, each controller  106  may use the measurements from one or more sensors  102   a  to control the operation of one or more actuators  102   b . For example, a controller  106  could receive measurement data from one or more sensors  102   a  and use the measurement data to generate control signals for one or more actuators  102   b . Each controller  106  includes any suitable structure for interacting with one or more sensors  102   a  and controlling one or more actuators  102   b . Each controller  106  could, for example, represent a proportional-integral-derivative (PID) controller or a multivariable controller, such as a Robust Multivariable Predictive Control Technology (RMPCT) controller or other type of controller implementing model predictive control (MPC) or other advanced predictive control (APC). As a particular example, each controller  106  could represent a computing device running a real-time operating system. 
     Two networks  108  are coupled to the controllers  106 . The networks  108  facilitate interaction with the controllers  106 , such as by transporting data to and from the controllers  106 . The networks  108  could represent any suitable networks or combination of networks. As a particular example, the networks  108  could represent a redundant pair of Ethernet networks, such as a FAULT TOLERANT ETHERNET (FTE) network from HONEYWELL INTERNATIONAL INC. 
     At least one switch/firewall  110  couples the networks  108  to two networks  112 . The switch/firewall  110  may transport traffic from one network to another. The switch/firewall  110  may also block traffic on one network from reaching another network. The switch/firewall  110  includes any suitable structure for providing communication between networks, such as a HONEYWELL CONTROL FIREWALL (CF9) device. The networks  112  could represent any suitable networks, such as an FTE network. 
     In the Purdue model, “Level  2 ” may include one or more machine-level controllers  114  coupled to the networks  112 . The machine-level controllers  114  perform various functions to support the operation and control of the controllers  106 , sensors  102   a , and actuators  102   b , which could be associated with a particular piece of industrial equipment (such as a boiler or other machine). For example, the machine-level controllers  114  could log information collected or generated by the controllers  106 , such as measurement data from the sensors  102   a  or control signals for the actuators  102   b . The machine-level controllers  114  could also execute applications that control the operation of the controllers  106 , thereby controlling the operation of the actuators  102   b . In addition, the machine-level controllers  114  could provide secure access to the controllers  106 . Each of the machine-level controllers  114  includes any suitable structure for providing access to, control of, or operations related to a machine or other individual piece of equipment. Each of the machine-level controllers  114  could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. Although not shown, different machine-level controllers  114  could be used to control different pieces of equipment in a process system (where each piece of equipment is associated with one or more controllers  106 , sensors  102   a , and actuators  102   b ). 
     One or more operator stations  116  are coupled to the networks  112 . The operator stations  116  represent computing or communication devices providing user access to the machine-level controllers  114 , which could then provide user access to the controllers  106  (and possibly the sensors  102   a  and actuators  102   b ). As particular examples, the operator stations  116  could allow users to review the operational history of the sensors  102   a  and actuators  102   b  using information collected by the controllers  106  and/or the machine-level controllers  114 . The operator stations  116  could also allow the users to adjust the operation of the sensors  102   a , actuators  102   b , controllers  106 , or machine-level controllers  114 . In addition, the operator stations  116  could receive and display warnings, alerts, or other messages or displays generated by the controllers  106  or the machine-level controllers  114 . Each of the operator stations  116  includes any suitable structure for supporting user access and control of one or more components in the system  100 . Each of the operator stations  116  could, for example, represent a computing device running a MICROSOFT WINDOWS operating system. 
     At least one router/firewall  118  couples the networks  112  to two networks  120 . The router/firewall  118  includes any suitable structure for providing communication between networks, such as a secure router or combination router/firewall. The networks  120  could represent any suitable networks, such as an FTE network. 
     In the Purdue model, “Level  3 ” may include one or more unit-level controllers  122  coupled to the networks  120 . Each unit-level controller  122  is typically associated with a unit in a process system, which represents a collection of different machines operating together to implement at least part of a process. The unit-level controllers  122  perform various functions to support the operation and control of components in the lower levels. For example, the unit-level controllers  122  could log information collected or generated by the components in the lower levels, execute applications that control the components in the lower levels, and provide secure access to the components in the lower levels. Each of the unit-level controllers  122  includes any suitable structure for providing access to, control of, or operations related to one or more machines or other pieces of equipment in a process unit. Each of the unit-level controllers  122  could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. Although not shown, different unit-level controllers  122  could be used to control different units in a process system (where each unit is associated with one or more machine-level controllers  114 , controllers  106 , sensors  102   a , and actuators  102   b ). 
     Access to the unit-level controllers  122  may be provided by one or more operator stations  124 . Each of the operator stations  124  includes any suitable structure for supporting user access and control of one or more components in the system  100 . Each of the operator stations  124  could, for example, represent a computing device running a MICROSOFT WINDOWS operating system. 
     At least one router/firewall  126  couples the networks  120  to two networks  128 . The router/firewall  126  includes any suitable structure for providing communication between networks, such as a secure router or combination router/firewall. The networks  128  could represent any suitable networks, such as an FTE network. 
     In the Purdue model, “Level  4 ” may include one or more plant-level controllers  130  coupled to the networks  128 . Each plant-level controller  130  is typically associated with one of the plants  101   a - 101   n , which may include one or more process units that implement the same, similar, or different processes. The plant-level controllers  130  perform various functions to support the operation and control of components in the lower levels. As particular examples, the plant-level controller  130  could execute one or more manufacturing execution system (MES) applications, scheduling applications, or other or additional plant or process control applications. Each of the plant-level controllers  130  includes any suitable structure for providing access to, control of, or operations related to one or more process units in a process plant. Each of the plant-level controllers  130  could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. 
     Access to the plant-level controllers  130  may be provided by one or more operator stations  132 . Each of the operator stations  132  includes any suitable structure for supporting user access and control of one or more components in the system  100 . Each of the operator stations  132  could, for example, represent a computing device running a MICROSOFT WINDOWS operating system. 
     At least one router/firewall  134  couples the networks  128  to one or more networks  136 . The router/firewall  134  includes any suitable structure for providing communication between networks, such as a secure router or combination router/firewall. The network  136  could represent any suitable network, such as an enterprise-wide Ethernet or other network or all or a portion of a larger network (such as the Internet). 
     In the Purdue model, “Level  5 ” may include one or more enterprise-level controllers  138  coupled to the network  136 . Each enterprise-level controller  138  is typically able to perform planning operations for multiple plants  101   a - 101   n  and to control various aspects of the plants  101   a - 101   n . The enterprise-level controllers  138  can also perform various functions to support the operation and control of components in the plants  101   a - 101   n . As particular examples, the enterprise-level controller  138  could execute one or more order processing applications, enterprise resource planning (ERP) applications, advanced planning and scheduling (APS) applications, or any other or additional enterprise control applications. Each of the enterprise-level controllers  138  includes any suitable structure for providing access to, control of, or operations related to the control of one or more plants. Each of the enterprise-level controllers  138  could, for example, represent a server computing device running a MICROSOFT WINDOWS operating system. In this document, the term “enterprise” refers to an organization having one or more plants or other processing facilities to be managed. Note that if a single plant  101   a  is to be managed, the functionality of the enterprise-level controller  138  could be incorporated into the plant-level controller  130 . 
     Access to the enterprise-level controllers  138  may be provided by one or more operator stations  140 . Each of the operator stations  140  includes any suitable structure for supporting user access and control of one or more components in the system  100 . Each of the operator stations  140  could, for example, represent a computing device running a MICROSOFT WINDOWS operating system. 
     Various levels of the Purdue model can include other components, such as one or more databases. The database(s) associated with each level could store any suitable information associated with that level or one or more other levels of the system  100 . For example, a historian  141  can be coupled to the network  136 . The historian  141  could represent a component that stores various information about the system  100 . The historian  141  could, for instance, store information used during production scheduling and optimization. The historian  141  represents any suitable structure for storing and facilitating retrieval of information. Although shown as a single centralized component coupled to the network  136 , the historian  141  could be located elsewhere in the system  100 , or multiple historians could be distributed in different locations in the system  100 . 
     In particular embodiments, the various controllers and operator stations in  FIG. 1  may represent computing devices. For example, each of the controllers  106 ,  114 ,  122 ,  130 ,  138  could include one or more processing devices  142  and one or more memories  144  for storing instructions and data used, generated, or collected by the processing device(s)  142 . Each of the controllers  106 ,  114 ,  122 ,  130 ,  138  could also include at least one network interface  146 , such as one or more Ethernet interfaces or wireless transceivers. Also, each of the operator stations  116 ,  124 ,  132 ,  140  could include one or more processing devices  148  and one or more memories  150  for storing instructions and data used, generated, or collected by the processing device(s)  148 . Each of the operator stations  116 ,  124 ,  132 ,  140  could also include at least one network interface  152 , such as one or more Ethernet interfaces or wireless transceivers. 
     As noted above, cyber-security is of increasing concern with respect to industrial process control and automation systems. Unaddressed security vulnerabilities in any of the components in the system  100  could be exploited by attackers to disrupt operations or cause unsafe conditions in an industrial facility. However, in many instances, operators do not have a complete understanding or inventory of all equipment running at a particular industrial site. As a result, it is often difficult to quickly determine potential sources of risk to a control and automation system. 
     This disclosure recognizes a need for a solution that understands potential vulnerabilities in various systems, prioritizes the vulnerabilities based on risk to an overall system, and guides a user to mitigate the vulnerabilities. This is accomplished (among other ways) by using a “rule handling infrastructure,” which in the example in  FIG. 1  is implemented or supported using a risk manager  154 . The risk manager  154  includes any suitable structure that includes a rules engine for converting system-related characteristics and events into cyber-security risk assessment values. Here, the risk manager  154  includes one or more processing devices  156 ; one or more memories  158  for storing instructions and data used, generated, or collected by the processing device(s)  156 ; and at least one network interface  160 . Each processing device  156  could represent a microprocessor, microcontroller, digital signal process, field programmable gate array, application specific integrated circuit, or discrete logic. Each memory  158  could represent a volatile or non-volatile storage and retrieval device, such as a random access memory or Flash memory. Each network interface  160  could represent an Ethernet interface, wireless transceiver, or other device facilitating external communication. The functionality of the risk manager  154  could be implemented using any suitable hardware or a combination of hardware and software/firmware instructions. 
     Although  FIG. 1  illustrates one example of an industrial process control and automation system  100 , various changes may be made to  FIG. 1 . For example, a control and automation system could include any number of sensors, actuators, controllers, servers, operator stations, networks, risk managers, and other components. Also, the makeup and arrangement of the system  100  in  FIG. 1  is for illustration only. Components could be added, omitted, combined, or placed in any other suitable configuration according to particular needs. Further, particular functions have been described as being performed by particular components of the system  100 . This is for illustration only. In general, control and automation systems are highly configurable and can be configured in any suitable manner according to particular needs. In addition,  FIG. 1  illustrates an example environment in which the functions of the risk manager  154  can be used. This functionality can be used in any other suitable device or system. 
       FIG. 2  illustrates an example rule handling infrastructure  200  for identifying security issues in industrial process control and automation systems according to this disclosure. The infrastructure  200  could be supported or implemented using the risk manager  154  or other processing device configured to perform as disclosed herein. This infrastructure  200  analyzes collected risk data and dynamically creates risk values based on rules for various risk items. Risk values are associated with devices in a computing system in which the risks occurred or exist. 
     Multiple risk values can be aggregated up into a hierarchy of devices to help identify areas that are more at risk. In various embodiments, the infrastructure  200  is configured so that a user is able to add and remove security products (such as MCAFEE or SYMANTEC products) without having to modify the rule infrastructure. Rule sets, in various embodiments, can be generic so that the same rules for similar types of products (such as antivirus products) can apply to any product of that product type without having to modify the rules. 
     In the example shown in  FIG. 2 , the rule handling infrastructure  200  includes logical components including source data components  210 , target data components  220 , end point rule sets  230 , and a rule engine framework  240 . The rule handling infrastructure  200  also includes a user interface (GUI)  250  for displaying information and interacting with users as described herein. GUI  250  can display data as produced by the rule handling infrastructure  200  via a device or application directly interfacing with rule engine framework  240 , by a device or application that functions as or is connected to a target data component  220 , or otherwise. 
     The source data components  210  include individual input processing units (data source providers  212 ) for incoming data. The incoming data can include information identifying characteristics of multiple devices in a computing system (such as the system  100 ) and multiple events associated with the multiple devices, each designated in  FIG. 2  as a data source provider  212 . The source data components  210  could, for example, be associated with and collect data from the various computing and networking components shown in  FIG. 1 . In different implementations, there may or may not be a theoretical limit to the number of data source providers that can be supported. There can, of course, be physical limits based on the hardware memory or processor speed that could limit the total number of data source providers. The incoming data can come from any source that a rule engine is able to access, including any of the devices in the computing system. This could include (but is not limited to) data from the following:
         Security software (antivirus, whitelisting, etc.)   Data acquisition subsystems (SIEMs, SCOM, etc.)   Devices (routers, switches, etc.)   Computers (events, performance, etc.)   Data sources (including, but not limited to, databases and files)   Applications that inject data (such as for testing, simulation, etc.)   Web applications (Web API)       

     The data source providers  212  can be specific as to the device, software, or other input source from which they are getting data. Each can include custom code that knows how to get the data from an input source. The data can be passed to and processed by a normalization component  214  that takes the incoming data and formats it to a common format related to the type of data. For example, data from different antivirus software products can be grouped into similar data items, and the values can be formatted to common values (antivirus installed, antivirus enabled, etc.). This data is made available to the rule engine framework  240  and used by the end point rule sets  230 . 
     The target data components  220  can be associated with and provide information generated by the rule engine framework  240  to various devices or systems. For example, the target data components  220  can be used to interact with mobile or fixed computing devices of personnel responsible for managing security in the system  100 . Target data components  220  can include data source adapters  222  that convert the information generated by the rule engine framework  240 , such as cyber security risk information, into a format that can be processed by the respective target data components  220 . In general, inputs to rule engine framework  240  are from data source providers  212  and outputs from rule engine framework  240  are provided to target data components  220 . 
     The end point rule sets  230  define different rules to be applied to data from the source data components  210 . The rules in the end point rule sets  230  are used to analyze characteristics of different devices and different events that occur involving the devices (such as the various devices in  FIG. 1 ). The rules can also generate values indicative of security vulnerabilities or other problems with the source data components  210 . For example, the rules can be used to generate at least one risk assessment value identifying at least one cyber-security risk of the devices. 
     In various embodiments, the end point rule sets  230  get configuration data associated to the rules via values that are defined by the user. This allows a specific site implementation to modify the rules to fit their site needs if they desire. For example, a site might have different clusters or zones of devices where the devices in that zone are not critical to the plant operations or other functions. In this case, certain types of risks that would normally be ranked with a high value could be modified so that the values are not that high. This would prevent zones of little importance from overshadowing the other zones that might be more important. The end point rules sets  230  can include weighting factors or other user-definable configuration data as part of specific rules that are applied to increase or decrease the risk assessment value associated with any specific device or cyber-security risk. 
     The rule engine framework  240  is a primary component for the rule handling infrastructure  200 . It handles start-up tasks for the rule engine, which could include:
         Loading the end point rule sets  230 ;   Loading the source data components  210 ;   Loading the target data components  220 ;   Getting configuration items for each rule; and   Initializing data models.       

     A data model for devices can include a hierarchy tree that groups data based on how the data was configured when the system was set up. This allows for grouping risk items and assigning impact risks on other items within the hierarchy tree. Once the rule engine is initialized, it can start threads, such as to handle the processing of each independent source data component  210 . The rule engine framework  240  also contains a common data adapter interop component, which identifies the internal data formats that are passed to individual components in the rule engine. This includes data internal to the rule engine framework  240  and data passed between data source providers, data source adapters, and end point rule sets. 
     The rule engine framework  240  also contains individual features for defining behaviors  242  on rules defined in the rule sets. This can include, but is not limited to, behaviors to support time-based rules, cumulative rules, and impact rules. Time-based behaviors allow for defining rules that have some special processing based on the passage of time. Cumulative-based behaviors allow for defining rules that have special processing on data based on how many times data of the rule is processed. Impact rules allow for defining a rule that impacts the risk on other devices in the hierarchy tree of the device that the rule is processing. 
     The rule engine framework  240  supports the ability to aggregate the risk items from risk areas, PCs, zones, and sites into one or more aggregate sets  244 . Based on the rule set calculations, it can assign the highest risk found at a particular level and, for example, make it available to display, for example in GUI  250 . For example, a zone aggregate record could display the highest risk item calculated among the PCs and devices found within the zone. The calculation of aggregates and aggregate sets  244  can be common among all rules so it is part of the rule engine framework  240  to make the end point rule sets  230  simpler and light weight (less complicated). 
     Rules engine framework includes an execution engine  246 , that can be implemented using one or more processors or controllers, that executes the various processes as described herein. These can be executed under the control of executable instructions stored in a machine-readable medium. 
     Among other things, this infrastructure  200  can include a number of unique features. For example, in various embodiments, source data and target data components  210 - 220  can be added and removed as needed without requiring any changes to the rule engine framework  240  or the end point rule sets  230 . In various embodiments, end point rule sets  230  can be added or removed without requiring any changes to the rule engine framework  240 . In various embodiments, the rule engine framework  240  defines behaviors that can be applied to rule sets  230  that provide handling time-based rules, cumulative rules, impact rules, etc. 
     In some embodiments, end point rule sets  230  can be generic, and adding a new source data provider need not require the end point rule set  230  to be modified if a rule set already exists for that data source type (such as antivirus). In various embodiments, the rule engine framework  240  provides features to calculate aggregate risk assessment values, which can be aggregated from the bottom level (such as PC or device level, etc.) all the way up (zone, site, etc.). In various embodiments, data is broken up into individual items and identified as risk items. The risk items have individual risk factors applied to them, thereby allowing some risk items to be more critical than others. 
     In some embodiments, the rule engine also calculates risks (in addition to merely collecting data). In various embodiments, risk calculations can be based on the ISO 27005 risk management standard (ISO/IEC 27005:2011) or other standard. 
     Although  FIG. 2  illustrates one example of a rule handling infrastructure  200  for identifying security issues in industrial process control and automation systems or other systems, various changes may be made to  FIG. 2 . For example, the functional division of the components  210 - 250  and the functional divisions within each component  210 - 250  are for illustration only. Various components or sub-components could be combined, further subdivided, rearranged, or omitted and additional components or sub-components could be added according to particular needs. 
       FIG. 3  illustrates a flowchart of a process  300  in accordance with disclosed embodiments, that can be performed, for example, by risk manager  154 , rule handling infrastructure  200 , or other device configured to perform as described, referred to generically as the “risk manager system” below. 
     The risk manager system receives information identifying characteristics of multiple devices in a computing system and multiple events associated with the multiple devices ( 305 ). In some embodiments, the information is received from source data components that are associated with and collect data from the multiple devices. In some embodiments, the information is processed by a normalization component that formats the information to a common format according the type of the information. 
     The risk manager system analyzes the information using multiple sets of rules ( 310 ). In some embodiments, the risk manager system also transmits cyber security risk information, corresponding to the analysis, to one or more target data components. In some embodiments, the risk manager system also converts cyber security risk information, corresponding to the analysis, into a format that can be processed by respective target data components. In some embodiments, the risk manager system also defines behaviors and applies the behaviors to the multiple sets of rules, the multiple sets of rules including at least one of time-based rules, cumulative rules, and impact rules. 
     The risk manager system generates at least one risk assessment value based on the analyzing, the at least one risk assessment value identifying at least one cyber-security risk of the multiple devices ( 315 ). In some embodiments, the risk manager system also aggregates risk assessment values over a hierarchy of the multiple devices. 
     The risk manager system stores and displays the at least one risk assessment value to a user ( 320 ). 
     Note that the risk manager  154  and/or the rule handling infrastructure  200  shown here could use or operate in conjunction with any combination or all of various features described in the following previously-filed and concurrently-filed patent applications (all of which are hereby incorporated by reference): 
     U.S. patent application Ser. No. 14/482,888 entitled “DYNAMIC QUANTIFICATION OF CYBER-SECURITY RISKS IN A CONTROL SYSTEM”; 
     U.S. Provisional Patent Application No. 62/036,920 entitled “ANALYZING CYBER-SECURITY RISKS IN AN INDUSTRIAL CONTROL ENVIRONMENT”; 
     U.S. Provisional Patent Application No. 62/113,221 entitled “NOTIFICATION SUBSYSTEM FOR GENERATING CONSOLIDATED, FILTERED, AND RELEVANT SECURITY RISK-BASED NOTIFICATIONS” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0048937-0115) filed concurrently herewith; 
     U.S. Provisional Patent Application No. 62/113,100 entitled “TECHNIQUE FOR USING INFRASTRUCTURE MONITORING SOFTWARE TO COLLECT CYBER-SECURITY RISK DATA” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0048943-0115) filed concurrently herewith; 
     U.S. Provisional Patent Application No. 62/113,186 entitled “INFRASTRUCTURE MONITORING TOOL FOR COLLECTING INDUSTRIAL PROCESS CONTROL AND AUTOMATION SYSTEM RISK DATA” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0048945-0115) filed concurrently herewith; 
     U.S. Provisional Patent Application No. 62/113,165 entitled “PATCH MONITORING AND ANALYSIS” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0048973-0115) filed concurrently herewith; 
     U.S. Provisional Patent Application No. 62/113,152 entitled “APPARATUS AND METHOD FOR AUTOMATIC HANDLING OF CYBER-SECURITY RISK EVENTS” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0049067-0115) filed concurrently herewith; 
     U.S. Provisional Patent Application No. 62/114,928 entitled “APPARATUS AND METHOD FOR DYNAMIC CUSTOMIZATION OF CYBER-SECURITY RISK ITEM RULES” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0049099-0115) filed concurrently herewith; 
     U.S. Provisional Patent Application No. 62/114,865 entitled “APPARATUS AND METHOD FOR PROVIDING POSSIBLE CAUSES, RECOMMENDED ACTIONS, AND POTENTIAL IMPACTS RELATED TO IDENTIFIED CYBER-SECURITY RISK ITEMS” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0049103-0115) filed concurrently herewith; 
     U.S. Provisional Patent Application No. 62/114,937 entitled “APPARATUS AND METHOD FOR TYING CYBER-SECURITY RISK ANALYSIS TO COMMON RISK METHODOLOGIES AND RISK LEVELS” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0049104-0115) filed concurrently herewith; and 
     U.S. Provisional Patent Application No. 62/116,245 entitled “RISK MANAGEMENT IN AN AIR-GAPPED ENVIRONMENT” and corresponding non-provisional U.S. patent application Ser. No. ______ of like title (Docket No. H0049081-0115) filed concurrently herewith. 
     In some embodiments, various functions described in this patent document are implemented or supported by a computer program that is formed from computer readable program code and that is embodied in a computer readable medium. The phrase “computer readable program code” includes any type of computer code, including source code, object code, and executable code. The phrase “computer readable medium” includes any type of medium capable of being accessed by a computer, such as read only memory (ROM), random access memory (RAM), a hard disk drive, a compact disc (CD), a digital video disc (DVD), or any other type of memory. A “non-transitory” computer readable medium excludes wired, wireless, optical, or other communication links that transport transitory electrical or other signals. A non-transitory computer readable medium includes media where data can be permanently stored and media where data can be stored and later overwritten, such as a rewritable optical disc or an erasable memory device. 
     It may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms “application” and “program” refer to one or more computer programs, software components, sets of instructions, procedures, functions, objects, classes, instances, related data, or a portion thereof adapted for implementation in a suitable computer code (including source code, object code, or executable code). The term “communicate,” as well as derivatives thereof, encompasses both direct and indirect communication. The terms “include” and “comprise,” as well as derivatives thereof, mean inclusion without limitation. The term “or” is inclusive, meaning and/or. The phrase “associated with,” as well as derivatives thereof, may mean to include, be included within, interconnect with, contain, be contained within, connect to or with, couple to or with, be communicable with, cooperate with, interleave, juxtapose, be proximate to, be bound to or with, have, have a property of, have a relationship to or with, or the like. The phrase “at least one of,” when used with a list of items, means that different combinations of one or more of the listed items may be used, and only one item in the list may be needed. For example, “at least one of: A, B, and C” includes any of the following combinations: A, B, C, A and B, A and C, B and C, and A and B and C. 
     While this disclosure has described certain embodiments and generally associated methods, alterations and permutations of these embodiments and methods will be apparent to those skilled in the art. Accordingly, the above description of example embodiments does not define or constrain this disclosure. Other changes, substitutions, and alterations are also possible without departing from the spirit and scope of this disclosure, as defined by the following claims.