End-of-life management system
Network entities of a network system are managed in an end-of-life context. A network system is scanned to determine network entities such as hardware devices and/or software applications. A network entity can be identified as vulnerable based on end-of-life data. A risk score for the vulnerable network entity is computed based on the end-of-life data and optionally other factors, and a potentially mitigating action is determined based on the risk score.
1. A method, comprising:
receiving, by one or more processors, in response to querying, end-of-life information from the one or more data sources on a network system;
identifying, by the one or more processors at least one network entity on the network system as a vulnerable network entity based on the end-of-life information and projected support of the network entities;
computing, by the one or more processors a risk score for the vulnerable network entity based on the end-of-life information, and a measure of business stability of a vendor associated with the vulnerable network entity;
determining, by the one or more processors a potential mitigating action based on the risk score, wherein execution of the potential mitigating action triggers limiting network access to the vulnerable network entity; and
machine learning, by the one or more processors, a vendor tendency to predict when a network entity is likely to reach end-of-life based on vendor behavior towards other software applications or hardware provided by the vendor.
2. The method of claim 1 , further comprising continuously scanning the network system for the network entities.
3. The method of claim 1 , further comprising scanning the network system to identify the one or more network entities comprising at least one of a hardware or software entity.
4. The method of claim 1 , further comprising identifying at least one of the one or more network entities as the vulnerable network entity based on the end-of-life information pertaining to whether a vendor is maintaining or discontinuing support.
5. The method of claim 1 , further comprising identifying at least one of the one or more network entities as the vulnerable network entity based on a vendor contract.
6. The method of claim 1 , further comprising predicting end-of-life based on historical data regarding a network entity.
7. The method of claim 1 , further comprising performing cost benefit analysis with respect to two or more potential mitigating actions and selecting the action based on a result of the cost benefit analysis.
8. The method of claim 1 , further comprising determining a schedule for managing end-of-life contracts, wherein the schedule predicts potential mitigating actions.
9. The method of claim 1 , further comprising:
altering a vendor contract according to the vendor tendency and a prediction about the network entity.
10. The method of claim 1 , further comprising:
determining the network entity is secure due to the network entity not reaching end-of-life status; and
determining that mitigating actions are not required for the entity based on the network entity reaching end-of-life status.
11. A system comprising:
a processor coupled to a memory that stores instructions that, when executed by the processor, cause the processor to:
receive, in response to querying, end-of-life information from the one or more data sources on a network system;
identify at least one network entity on the network system as a vulnerable network entity based on the end-of-life information and projected support of the network entities;
compute a risk score for the vulnerable network entity based on the end-of-life information, and a measure of business stability of a vendor associated with the vulnerable network entity;
determine a potential mitigating action based on the risk score, wherein execution of the potential mitigating action triggers limiting network access to the vulnerable network entity; and
machine learn a vendor tendency to predict when a network entity is likely to reach end-of-life based on vendor behavior towards other software applications or hardware provided by the vendor.
12. The system of claim 11 , further comprising instructions configured to cause the processor to continuously scan the network system for the network entities.
13. The system of claim 11 , further comprising instructions configured to cause the processor to scan the network system to identify the one or more network entities comprising at least one of a hardware or software entity.
14. The system of claim 11 , further comprising instructions configured to cause the processor to identify at least one of the one or more network entities as the vulnerable network entity based on the end-of-life information pertaining to whether a vendor is maintaining or discontinuing support.
15. The system of claim 11 , further comprising instructions configured to cause the processor to identify at least one of the one or more network entities as the vulnerable network entity based on a vendor contract.
16. The system of claim 11 , further comprising instructions configured to cause the processor to predict end-of-life based on historical data regarding a network entity.
17. The system of claim 11 , further comprising instructions configured to cause the processor to perform cost benefit analysis with respect to two or more potential mitigating actions and selecting the action based on a result of the cost benefit analysis.
18. The system of claim 17 , further comprising instructions configured to cause the processor to:
determine the network entity is secure due to the network entity not reaching end-of-life status; and
determine that mitigating actions are not required for the entity based on the network entity reaching end-of-life status.
19. The system of claim 11 , further comprising instructions configured to cause the processor to determine a schedule for managing end-of-life contracts, wherein the schedule predicts potential mitigating actions.
20. A non-transitory computer readable medium comprising instructions that when executed by one or more processors cause the one or more processors to:
receive, in response to querying, end-of-life information from the one or more data sources on a network system;
identify at least one network entity on the network system as a vulnerable network entity based on the end-of-life information and projected support of the network entities;
compute a risk score for the vulnerable network entity based on the end-of-life information, and a measure of business stability of a vendor associated with the vulnerable network entity;
determine a potential mitigating action based on the risk score, wherein execution of the potential mitigating action triggers limiting network access to the vulnerable network entity; and
machine learn a vendor tendency to predict when a network entity is likely to reach end-of-life based on vendor behavior towards other software applications or hardware provided by the vendor.