Determining local administrator rights-related dependency relationships using artificial intelligence techniques
Methods, apparatus, and processor-readable storage media for determining local administrator rights-related (LAR-related) dependency relationships using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to multiple actions performed across one or more software applications by one or more user devices; determining one or more action dependency relationships associated with at least one action performed by a given one of the user devices in connection with a given one of the software applications, by processing at least a portion of the obtained data using artificial intelligence techniques; modifying one or more LARs, granted for the given user device, to permit access to the given user device to perform one or more actions in connection with at least one of the software applications in accordance with the determined action dependency relationship(s); and performing one or more automated actions based on the modifying of the LAR(s).
1 . A computer-implemented method comprising:
obtaining data pertaining to multiple actions performed across one or more software applications by one or more user devices;
determining one or more action dependency relationships associated with actions performed by a given one of the one or more user devices in connection with a given one of the one or more software applications, by processing, using a processor-based machine learning system including a classifier and one or more decision tree models, at least a portion of the obtained data, the processing comprising:
processing, by the classifier, trained using large language model (LLM) embeddings, the at least a portion of the obtained data to classify the one or more software applications into one or more categories;
generating, at an output of the classifier, application category data related to the one or more categories;
processing, using the processor-based machine learning system, the application category data and user device activity data to create one or more chains of user device actions across the one or more classified software applications; and
processing, at an input of the one or more decision tree models, the one or more chains of user device actions to generate, at an output of the one or more decision tree models, at least one predicted next action for the given user device;
modifying one or more local administrator rights, granted for the given user device, to permit access to the given user device to perform the at least one predicted next action; and
performing one or more automated actions based at least in part on the modifying of the one or more local administrator rights;
wherein the method is performed by at least one processing device comprising a processor coupled to a memory.
2 . The computer-implemented method of claim 1 , wherein performing one or more automated actions comprises generating one or more workflows comprising instructions for carrying out, in association with the modifying of the one or more local administrator rights, the at least one predicted next action.
3 . The computer-implemented method of claim 1 , wherein modifying one or more local administrator rights granted for the given user device comprises granting one or more additional local administrator rights for the given user device to perform the at least one predicted next action.
4 . The computer-implemented method of claim 1 , wherein performing one or more automated actions comprises automatically training at least a portion of the classifier using feedback related to the modifying of the one or more local administrator rights.
5 . The computer-implemented method of claim 1 , wherein obtaining data pertaining to multiple actions comprises tracking actions performed across the one or more software applications installed on at least a portion of the one or more user devices by registering with one or more operating system channels associated with the at least a portion of the one or more user devices, and monitoring log data generated by the one or more software applications associated the one or more operating system channels.
6 . The computer-implemented method of claim 1 , wherein obtaining data pertaining to multiple actions comprises subscribing to at least one of one or more system events and one or more logging mechanisms provided by at least one operating system associated with at least a portion of the one or more user devices.
7 . The computer-implemented method of claim 1 , wherein obtaining data pertaining to multiple actions comprises processing messages sent in connection with at least a portion of the one or more software applications by implementing at least one plugin to one or more graphical user interface (GUI) software development kits (SDKs) of at least one operating system associated with at least a portion of the one or more user devices.
8 . The computer-implemented method of claim 1 , wherein obtaining data pertaining to multiple actions comprises processing historical log data associated with at least a portion of the one or more software applications by implementing at least one plugin to one or more log storage mechanisms used by the at least a portion of the one or more software applications installed on the one or more user devices.
9 . A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:
to obtain data pertaining to multiple actions performed across one or more software applications by one or more user devices;
to determine one or more action dependency relationships associated with actions performed by a given one of the one or more user devices in connection with a given one of the one or more software applications, by processing, using a processor-based machine learning system including a classifier and one or more decision tree models, at least a portion of the obtained data, the processing comprising:
processing, by the classifier, trained using large language model (LLM) embeddings, the at least a portion of the obtained data to classify the one or more software applications into one or more categories;
generating, at an output of the classifier, application category data related to the one or more categories;
processing, using the processor-based machine learning system, the application category data and user device activity data to create one or more chains of user device actions across the one or more classified software applications; and
processing, at an input of the one or more decision tree models, the one or more chains of user device actions to generate, at an output of the one or more decision tree models, at least one predicted next action for the given user device;
to modify one or more local administrator rights, granted for the given user device, to permit access to the given user device to perform the at least one predicted next action; and
to perform one or more automated actions based at least in part on the modifying of the one or more local administrator rights.
10 . The non-transitory processor-readable storage medium of claim 9 , wherein performing one or more automated actions comprises generating one or more workflows comprising instructions for carrying out, in association with the modifying of the one or more local administrator rights, the at least one predicted next action.
11 . The non-transitory processor-readable storage medium of claim 9 , wherein modifying one or more local administrator rights granted for the given user device comprises granting one or more additional local administrator rights for the given user device to perform the at least one predicted next action.
12 . The non-transitory processor-readable storage medium of claim 9 , wherein performing one or more automated actions comprises automatically training at least a portion of the classifier using feedback related to the modifying of the one or more local administrator rights.
13 . An apparatus comprising:
at least one processing device comprising a processor coupled to a memory;
the at least one processing device being configured:
to obtain data pertaining to multiple actions performed across one or more software applications by one or more user devices;
to determine one or more action dependency relationships associated with actions performed by a given one of the one or more user devices in connection with a given one of the one or more software applications, by processing, using a processor-based machine learning system including a classifier and one or more decision tree models, at least a portion of the obtained data, the processing comprising:
processing, by the classifier, trained using large language model (LLM) embeddings, the at least a portion of the obtained data to classify the one or more software applications into one or more categories;
generating, at an output of the classifier, application category data related to the one or more categories;
processing, using the processor-based machine learning system, the application category data and user device activity data to create one or more chains of user device actions across the one or more classified software applications; and
processing, at an input of the one or more decision tree models, the one or more chains of user device actions to generate, at an output of the one or more decision tree models, at least one predicted next action for the given user device;
to modify one or more local administrator rights, granted for the given user device, to permit access to the given user device to perform the at least one predicted next action; and
to perform one or more automated actions based at least in part on the modifying of the one or more local administrator rights.
14 . The apparatus of claim 13 , wherein performing one or more automated actions comprises generating one or more workflows comprising instructions for carrying out, in association with the modifying of the one or more local administrator rights, the at least one predicted next action.
15 . The apparatus of claim 13 , wherein modifying one or more local administrator rights granted for the given user device comprises granting one or more additional local administrator rights for the given user device to perform the at least one predicted next action.
16 . The apparatus of claim 13 , wherein performing one or more automated actions comprises automatically training at least a portion of the classifier using feedback related to the modifying of the one or more local administrator rights.
17 . The apparatus of claim 13 , wherein obtaining data pertaining to multiple actions comprises tracking actions performed across the one or more software applications installed on at least a portion of the one or more user devices by registering with one or more operating system channels associated with the at least a portion of the one or more user devices, and monitoring log data generated by the one or more software applications associated the one or more operating system channels.
18 . The apparatus of claim 13 , wherein obtaining data pertaining to multiple actions comprises subscribing to at least one of one or more system events and one or more logging mechanisms provided by at least one operating system associated with at least a portion of the one or more user devices.
19 . The apparatus of claim 13 , wherein obtaining data pertaining to multiple actions comprises processing messages sent in connection with at least a portion of the one or more software applications by implementing at least one plugin to one or more graphical user interface (GUI) software development kits (SDKs) of at least one operating system associated with at least a portion of the one or more user devices.
20 . The apparatus of claim 13 , wherein obtaining data pertaining to multiple actions comprises processing historical log data associated with at least a portion of the one or more software applications by implementing at least one plugin to one or more log storage mechanisms used by the at least a portion of the one or more software applications installed on the one or more user devices.