Methods and apparatus to automate cyber defense decision process and response actions by operationalizing adversarial technique frameworks
In some embodiments, a method can include identifying detection coverage of a set of adversarial techniques based on telemetry data and a detection instance of an environment. The method can further include determining a subset of detection coverage that has a metric value below a metric value threshold and among the detection coverage for the set of adversarial techniques. The method may further include identifying at least one detection instance associated with the subset of detection coverage. The method can further include presenting, via a graphical user interface, a representation of at least one of the subset of detection coverage or the at least one detection instance associated with the subset of detection coverage. The method can further include updating the subset of detection coverage based on the telemetry data, the detection instance, or the at least one detection instance to improve the metric value.
1. A method, comprising:
storing threat information in an adversarial technique framework data structure using a framework and a data model of a set of known threats based on tools, tactics or procedures used by adversary groups, software, techniques, malware or campaigns;
electronically receiving a set of telemetry data from one or more networked components forming part of a network infrastructure, wherein the telemetry data is indicative of adversarial techniques, and wherein the networked components comprise one or more hardware or software components for monitoring a security state on a computer network;
automatically storing in a computerized data store one or more of security product configuration information, detection logic, and the telemetry data, wherein the security product configuration information comprises information about the networked components;
generating scores for the set of known threats;
determining one or more prioritized mitigation recommendations based on at least the scores for the threats in the set of known threats; and
presenting to a user via a graphical user interface the one or more prioritized mitigation recommendations, wherein determining the one or more mitigation recommendations further comprises:
storing a set of mitigations mapped for association with the set of known threats, detection logic, and telemetry data;
mapping the stored threat information for the set of known threats against the telemetry data to identify one or more security coverage gaps; and
determining the one or more mitigation recommendations based on the one or more identified security coverage gaps.
2. The method of claim 1 , wherein the telemetry data comprises one or more sets of log data and configuration data.
3. The method of claim 1 , wherein telemetry data comprises raw log data that can be used to detect adversarial techniques used by cyber adversaries.
4. The method of claim 1 , wherein a telemetry sensor of a networked component includes software stored in a memory and executed in a processor further comprising hardware to receive telemetry data from a set of data sources.
5. The method of claim 1 , further comprising identifying the telemetry data by automatically sensing the telemetry data in at least one of a data lake, an intrusion detection system (IDS), an intrusion prevention system (IPS), a network appliance, or an endpoint protection platform (EPP).
6. The method of claim 1 , further comprising:
training a machine learning model based on a set of historical telemetry data received from the networked components and a set of historical threat information;
receiving subsequent telemetry data from the networked components and subsequent threat information; and
executing the machine learning model based on the subsequent telemetry data and the subsequent threat information to predict an occurrence of a subsequent threat and a prioritized exposure score for the subsequent threat.
7. An apparatus, comprising:
a memory and a process of a compute device, the processor operatively coupled to the memory and configured for:
storing threat information in an adversarial technique framework data structure using a framework and a data model of a set of known threats based on tools, tactics or procedures used by adversary groups, software, techniques, malware or campaigns;
electronically receiving a set of telemetry data from one or more networked components forming part of a network infrastructure, wherein the telemetry data is indicative of adversarial techniques, and wherein the networked components comprise one or more hardware or software components for monitoring a security state on a computer network;
automatically storing in a computerized data store one or more of security product configuration information, detection logic, and the telemetry data, wherein the security product configuration information comprises information about the networked components;
generating scores for the set of known threats;
determining one or more prioritized mitigation recommendations based on at least the scores for the threats in the set of known threats; and
presenting to a user via a graphical user interface the one or more prioritized mitigation recommendations, wherein determining the one or more mitigation recommendations further comprises:
storing a set of mitigations mapped for association with the set of known threats, detection logic, and telemetry data;
mapping the stored threat information for the set of known threats against the telemetry data to identify one or more security coverage gaps; and
determining the one or more mitigation recommendations based on the one or more identified security coverage gaps.
8. The apparatus of claim 7 , wherein the telemetry data comprises one or more sets of log data and configuration data.
9. The apparatus of claim 7 , wherein telemetry data comprises raw log data that can be used to detect adversarial techniques used by cyber adversaries.
10. The apparatus of claim 7 , wherein a telemetry sensor of a networked component includes software stored in a memory and executed in a processor further comprising hardware to receive telemetry data from a set of data sources.
11. The apparatus of claim 7 , further comprising:
identifying telemetry data, at least a portion of the telemetry data indicative of a set of adversarial techniques; and
determining a detection instance queryable against the telemetry data to detect the set of adversarial techniques.
12. The apparatus of claim 7 , further comprising identifying the telemetry data by automatically sensing the telemetry data in at least one of a data lake, an intrusion detection system (IDS), an intrusion prevention system (IPS), a network appliance, or an endpoint protection platform (EPP).
13. The apparatus of claim 7 , further comprising:
training a machine learning model based on a set of historical telemetry data received from the networked components and a set of historical threat information;
receiving subsequent telemetry data from the networked components and subsequent threat information; and
executing the machine learning model based on the subsequent telemetry data and the subsequent threat information to predict an occurrence of a subsequent threat and a prioritized exposure score for the subsequent threat.
14. A non-transitory processor-readable medium storing code representing instructions to be executed by a processor, the code comprising code to cause the processor to perform:
storing threat information in an adversarial technique framework data structure using a framework and a data model of a set of known threats based on tools, tactics or procedures used by adversary groups, software, techniques, malware or campaigns;
electronically receiving a set of telemetry data from one or more networked components forming part of a network infrastructure, wherein the telemetry data is indicative of adversarial techniques, and wherein the networked components comprise one or more hardware or software components for monitoring a security state on a computer network;
automatically storing in a computerized data store one or more of security product configuration information, detection logic, and the telemetry data, wherein the security product configuration information comprises information about the networked components;
generating scores for the set of known threats;
determining one or more prioritized mitigation recommendations based on at least the scores for the threats in the set of known threats; and
presenting to a user via a graphical user interface the one or more prioritized mitigation recommendations, wherein determining the one or more mitigation recommendations further comprises:
storing a set of mitigations mapped for association with the set of known threats, detection logic, and telemetry data;
mapping the stored threat information for the set of known threats against the telemetry data to identify one or more security coverage gaps; and
determining the one or more mitigation recommendations based on the one or more identified security coverage gaps.
15. The non-transitory processor-readable memory of claim 14 , wherein the telemetry data comprises one or more sets of log data and configuration data.
16. The non-transitory processor-readable memory of claim 14 , wherein telemetry data comprises raw log data that can be used to detect adversarial techniques used by cyber adversaries.
17. The non-transitory processor-readable memory of claim 14 , wherein a telemetry sensor of a networked component includes software stored in a memory and executed in a processor further comprising hardware to receive telemetry data from a set of data sources.
18. The non-transitory processor-readable memory of claim 14 , further comprising:
identifying telemetry data, at least a portion of the telemetry data indicative of a set of adversarial techniques; and
determining a detection instance queryable against the telemetry data to detect the set of adversarial techniques.
19. The non-transitory processor-readable memory of claim 14 , further comprising identifying the telemetry data by automatically sensing the telemetry data in at least one of a data lake, an intrusion detection system (IDS), an intrusion prevention system (IPS), a network appliance, or an endpoint protection platform (EPP).
20. The non-transitory processor-readable memory of claim 14 , further comprising:
training a machine learning model based on a set of historical telemetry data received from the networked components and a set of historical threat information;
receiving subsequent telemetry data from the networked components and subsequent threat information; and
executing the machine learning model based on the subsequent telemetry data and the subsequent threat information to predict an occurrence of a subsequent threat and a prioritized exposure score for the subsequent threat.