System and method for comprehensive data loss prevention and compliance management
A system and method to identify and prevent cybersecurity attacks on modern, highly-interconnected networks, to identify attacks before data loss occurs, using a combination of human level, device level, system level, and organizational level monitoring.
1. A system for comprehensive data loss prevention and compliance management, comprising:
a computing device comprising a processor and a memory;
a time series database module comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:
monitor a plurality of network events on a network; and
produce time-series data comprising a record of a network event and the time at which the event occurred;
an observation and state estimation module comprising a second plurality of programming instructions stored in the memory and operating on the processor, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to:
monitor a plurality of connected resources on the network; and
produce a cyber-physical graph representing the plurality of connected resources, wherein:
the connected resources comprise one or more of people, devices, systems, and organizations within the network;
the cyber-physical graph comprises nodes representing the connected resources, which each node having one or more properties containing descriptive information for the connected resource represented by that node; and
the cyber-physical graph comprises edges representing the logical relationships between the plurality of connected resources and the physical relationships between any connected resources comprising a hardware device;
an activity monitoring engine comprising a third plurality of programming instructions stored in the memory and operating on the processor, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to:
generate expected behavior data of the connected resources within the network by applying a behavioral model to each node of the cyber-physical graph and storing the expected behavior data for that node as the one or more properties of that node;
generate actual behavior data of the connected resources within the network from the time-series data and storing the expected behavior data for that node as the one or more properties of that node;
detect deviations of the actual behavior data from the expected behavior data by comparing the expected behavior data properties of each node with the actual behavior properties of that node; and
when deviations are detected, send information about the deviation to a risk analysis and scoring engine; and
a risk analysis and scoring engine comprising a fourth plurality of programming instructions stored in the memory and operating on the processor, wherein the fourth plurality of programming instructions, when operating on the processor, cause the computing device to:
receive deviation information from the activity monitoring engine;
analyze the severity of the threat posed by the deviation using at least one analysis algorithm;
generate a risk score based on a plurality of factors which indicate the severity of the threat; and
display the risk score in text and graphical form.
2. The system of claim 1 , wherein the risk analysis and scoring engine further generates an impact assessment score for each affected connected resource by determining an impact on the network using the cyber-physical graph.
3. The system of claim 2 , wherein the impact assessment score further comprises the calculation of the overall impact of a cyberattack, wherein the calculation is based at least in part on the impact assessment score for each connected resource affected by the cyberattack.
4. The system of claim 1 , wherein the detection of deviations is based in part on a comparison of relationships between the connected resources against known security vulnerabilities.
5. The system of claim 4 , wherein the risk score is based at least in part on the results of the comparison against known security vulnerabilities.
6. The system of claim 1 , wherein the observation and state estimation module is further configured to produce a visualization based at least in part on at least a portion of the time-series data, wherein the visualization illustrates changes to the data over time.
7. A method for comprehensive data loss prevention and compliance management, comprising the steps of:
monitoring a plurality of network events on a network;
producing time-series data comprising a record of a network event and the time at which the event occurred;
monitoring a plurality of connected resources on the network;
producing a cyber-physical graph representing the plurality of connected resources, wherein:
the connected resources comprise one or more of people, devices, systems, and organizations within the network;
the cyber-physical graph comprises nodes representing the connected resources, which each node having one or more properties containing descriptive information for the connected resource represented by that node; and
the cyber-physical graph comprises edges representing the logical relationships between the plurality of connected resources and the physical relationships between any connected resources comprising a hardware device;
generating expected behavior data of the connected resources within the network by applying a behavioral model to each node of the cyber-physical graph and storing the expected behavior data for that node as the one or more properties of that node;
generating actual behavior data of the connected resources within the network from the time-series data and storing the expected behavior data for that node as the one or more properties of that node;
detecting deviations of the actual behavior data from the expected behavior data by comparing the expected behavior data properties of each node with the actual behavior properties of that node; and
when deviations are detected:
analyzing the severity of the threat posed by the deviation using at least one analysis algorithm;
generating a risk score based on a plurality of factors which indicate the severity of the threat; and
displaying the risk score in text and graphical form.
8. The method of claim 7 , wherein the risk analysis and scoring engine further generates an impact assessment score for each affected connected resource by determining an impact on the network using the cyber-physical graph.
9. The method of claim 8 , wherein the impact assessment score further comprises the calculation of the overall impact of a cyberattack, wherein the calculation is based at least in part on the impact assessment score for each connected resource affected by the cyberattack.
10. The method of claim 7 , wherein the detection of deviations is based in part on a comparison of relationships between the connected resources against known security vulnerabilities.
11. The method of claim 10 , wherein the risk score is based at least in part on the results of the comparison against known security vulnerabilities.
12. The method of claim 7 , wherein the observation and state estimation module is further configured to produce a visualization based at least in part on at least a portion of the time-series data, wherein the visualization illustrates changes to the data over time.