Systems, apparatus, articles of manufacture, and methods for data usage monitoring to identify and mitigate ethical divergence

Abstract

Methods, apparatus, systems, and articles of manufacture are disclosed for data usage monitoring to identify and mitigate ethical divergence. Disclosed example apparatus are to orchestrate resources in an edge environment based on ingested network traffic on an edge network, the ingested network traffic associated with a source node that is to source a target data stream and a target artificial intelligence (AI) application node that is to consume at least a portion of the target data stream. Disclosed example apparatus are also to execute a machine learning model based on the ingested network traffic to generate one or more outputs including at least one a first value representative of a data stream characteristic or a second value representative of an AI application node characteristic, determine the one or more outputs satisfy a threshold value, and generate an alert in response to the one or more outputs satisfying the threshold value.

1 . An apparatus to monitor data usage, comprising:
 interface circuitry to communicatively couple a network to processor circuitry; and   the processor circuitry including one or more of:
 at least one of a central processor unit, a graphics processor unit, or a digital signal processor, the at least one of the central processor unit, the graphics processor unit, or the digital signal processor having control circuitry to control data movement within the processor circuitry, arithmetic and logic circuitry to perform one or more first operations corresponding to instructions, and one or more registers to store a result of the one or more first operations, the instructions in the apparatus; 
 a Field Programmable Gate Array (FPGA), the FPGA including logic gate circuitry, a plurality of configurable interconnections, and storage circuitry, the logic gate circuitry and the plurality of the configurable interconnections to perform one or more second operations, the storage circuitry to store a result of the one or more second operations; or 
 Application Specific Integrated Circuitry (ASIC) including logic gate circuitry to perform one or more third operations; 
   the processor circuitry to perform at least one of the first operations, the second operations, or the third operations to instantiate:
 resource manager orchestration circuitry to orchestrate resources in an edge environment based on ingested network traffic on an edge network, at least some of the ingested network traffic associated with at least one source node that is to source a target data stream and at least one target artificial intelligence (AI) application node that is to consume at least a portion of the target data stream; and 
 machine learning circuitry to:
 execute a machine learning model based on the ingested network traffic to generate one or more outputs, the one or more outputs including at least one of a first value representative of a data stream characteristic or a second value representative of an AI application node characteristic; 
 determine the one or more outputs satisfy a threshold value; and 
 generate an alert in response to the outputs satisfying the threshold value. 
 
   
     
     
         2 . The apparatus of  claim 1 , wherein the interface circuitry is to:
 ingest at least a portion of the target data stream from the data source;   tag the at least portion of the target data stream with metadata; and   query an orchestrator to identify the machine learning model as associated with the metadata; and   the machine learning circuitry is to execute the machine learning model to determine the at least one of the first value representative of the data stream characteristic or the second value representative of the AI application node characteristic.   
     
     
         3 . The apparatus of  claim 1 , wherein the machine learning circuitry is to:
 determine at least one of a content type of the target data stream, a sensitive attribute of the target data stream, a security level of the target data stream, or a source location of the target data stream; and   execute the machine learning model to determine the first value of the data stream characteristic based on the at least one of the content type, the sensitive attribute, the security level, or the source location.   
     
     
         4 . The apparatus of  claim 3 , wherein the target data stream includes one or more target data points associated with the at least one of the content type, the sensitive attribute, the security level, or the source location, and the processor circuitry is to perform the at least one of the first operations, the second operations, or the third operations to instantiate:
 metadata manager circuitry to generate at least one target graph node representation of the target data stream based on at least one of the one or more target data points; and   the machine learning circuitry is to execute the machine learning model to compare the at least one target graph node representation to one or more baseline graph node representations, the one or more baseline graph node representations including respective one or more nominal data points from nominal data streams, the one or more nominal data points including at least one of a content type of nominal data stream, a sensitive attribute of the nominal data stream, a security level of the nominal data stream, or a source location of the nominal data stream.   
     
     
         5 . The apparatus of  claim 1 , wherein the machine learning circuitry is to:
 determine at least one of a service type attribute of the AI application node or a usage context of the target data stream for the AI application node; and   execute the machine learning model to determine the second value of the AI application node characteristic based on at least one of the service type attribute or the usage context.   
     
     
         6 . The apparatus of  claim 1 , wherein the machine learning circuitry is to train the machine learning model with at least one of nominal traffic or nominal node behavior, the nominal traffic indicative of one or more nominal data streams with one or more expected data points, the nominal node behavior indicative of one or more expected data consumption patterns by one or more nominal nodes. 
     
     
         7 . The apparatus of  claim 1 , wherein the one or more outputs are one or more first outputs, and:
 the resource manager orchestration circuitry is to:
 instantiate a first super node in the edge environment; and 
 deploy a second instantiation of the machine learning model to the first super node; and 
   the machine learning circuitry is to:
 execute the second instantiation of the machine learning model based on a first plurality of data streams within network traffic ingested at the first super node to generate one or more second outputs, the one or more second outputs including values representative of data stream characteristics and values representative of AI application node characteristics; 
 share the one or more second outputs with a second super node in the edge environment; 
 obtain one or more third outputs from the second super node, the one or more third outputs generated from a third instantiation of the machine learning model executed at the second super node based on a second plurality of data streams within network traffic ingested at the second super node, the one or more third outputs including values representative of data stream characteristics and values representative of AI application node characteristics; and 
 train the machine learning model using at least one of the one or more second outputs or the one or more third outputs to build a consensus nominal data stream pattern. 
   
     
     
         8 . The apparatus of  claim 1 , wherein the machine learning model is a first machine learning model, the one or more outputs are one or more first outputs, the threshold value is a first threshold value, and
 the resource manager orchestration circuitry is to instantiate a deep data inspection node in the edge environment, the deep data inspection node to have access to the target data stream;   the interface circuitry to verify at least one feature in the target data stream corresponds to at least one of a target data stream characteristic or a target AI application node characteristic;   the processor circuitry is to perform the at least one of the first operations, the second operations, or the third operations to instantiate algorithm manager circuitry to select a second machine learning model trained on a feature set representative of at least one of at least one of the target data stream characteristic or the target AI application node characteristic;   the machine learning circuitry is to execute the second machine learning model over a period of time at the deep data inspection node based on the target data stream to generate one or more second outputs, the one or more second outputs including at least a third value representative of a deviation condition of the target data stream; and   the processor circuitry is to perform the at least one of the first operations, the second operations, or the third operations to instantiate deep data inspection circuitry to determine the one or more second outputs satisfy a second threshold value at least once over the period of time.   
     
     
         9 . The apparatus of  claim 8 , wherein:
 the resource manager orchestration circuitry is to deploy the trained second machine learning model across the deep data inspection node and one or more additional deep data inspection nodes in the edge environment; and   the machine learning circuitry is to train the second machine learning model with at least one nominal data stream pattern shared across the deep data inspection node and the one or more additional deep data inspection nodes.   
     
     
         10 . The apparatus of  claim 9 , wherein:
 the deep data inspection circuitry is to:
 at least one of determine the deviation condition of the target data stream occurs less than or equal to a frequency threshold, determine the deviation condition of the target data stream occurs less than or equal to a frequency threshold at the one or more additional deep data inspection nodes, or determine the deviation condition of the target data stream does not meet at least one constraint; and 
   the processor circuitry to perform the at least one of the first operations, the second operations, or the third operations to instantiate operation execution circuitry to cause at least one of a modification to the target data stream or a response to an attempt to consume the target data stream.   
     
     
         11 . The apparatus of  claim 1 , wherein the processor circuitry to perform the at least one of the first operations, the second operations, or the third operations to instantiate operation execution circuitry to, in response to the alert being generated, at least one of tag a portion of the target data stream with metadata, implement a blockchain for at least one data point in the target data stream, or prohibit consumption of the target data stream by the target AI application node. 
     
     
         12 . A non-transitory machine readable storage medium comprising instructions that, when executed, cause processor circuitry to at least:
 orchestrate resources in an edge environment based on ingested network traffic on an edge network, at least some of the ingested network traffic associated with at least one source node that is to source a target data stream and at least one target artificial intelligence (AI) application node that is to consume at least a portion of the target data stream;   execute a machine learning model based on the ingested network traffic to generate one or more outputs, the one or more outputs including at least one a first value representative of a data stream characteristic or a second value representative of an AI application node characteristic;   determine the one or more outputs satisfy a threshold value; and   generate an alert in response to the outputs satisfying the threshold value.   
     
     
         13 . The non-transitory machine readable storage medium  12 , wherein the instructions, when executed, further cause the processor circuitry to:
 ingest at least a portion of the target data stream from the data source;   tag the at least portion of the target data stream with metadata;   query an orchestrator to identify the machine learning model as associated with the metadata; and   execute the machine learning model to determine the at least one of the first value representative of the data stream characteristic or the second value representative of the AI application node characteristic.   
     
     
         14 . The non-transitory machine readable storage medium  12 , wherein the instructions, when executed, further cause the processor circuitry to:
 determine at least one of a content type of the target data stream, a sensitive attribute of the target data stream, a security level of the target data stream, or a source location of the target data stream; and   execute the machine learning model to determine the first value of the data stream characteristic based on the at least one of the content type, the sensitive attribute, the security level, or the source location.   
     
     
         15 . The non-transitory machine readable storage medium of  claim 12 , wherein the target data stream includes one or more target data points associated with at least one of a content type, a sensitive attribute, a security level, or a source location, and wherein the instructions, when executed, further cause the processor circuitry to:
 generate at least one target graph node representation of the target data stream based on at least one of the one or more target data points; and   execute the machine learning model to compare the at least one target graph node representation to one or more baseline graph node representations, the one or more baseline graph node representations including respective one or more nominal data points from nominal data streams, the one or more nominal data points including at least one of a content type of nominal data stream, a sensitive attribute of the nominal data stream, a security level of the nominal data stream, or a source location of the nominal data stream.   
     
     
         16 . The non-transitory machine readable storage medium of  claim 12 , wherein the instructions, when executed, further cause the processor circuitry to:
 determine at least one of a service type attribute of the AI application node or a usage context of the target data stream for the AI application node; and   execute the machine learning model to determine the second value of the AI application node characteristic based on at least one of the service type attribute or the usage context.   
     
     
         17 . The non-transitory machine readable storage medium of  claim 12 , wherein the instructions, when executed, further cause the processor circuitry to:
 select at least one policy for the ingested network traffic used to initiate the machine learning model; and   train the machine learning model with at least one of nominal traffic or nominal node behavior, the nominal traffic indicative of one or more nominal data streams with one or more expected data points, the nominal node behavior indicative of one or more expected data consumption patterns by one or more nominal nodes.   
     
     
         18 . The non-transitory machine readable storage medium of  claim 12 , wherein the instructions, when executed, further cause the processor circuitry to:
 instantiate a first super node in the edge environment; and   deploy a second instantiation of the machine learning model to the first super node;   execute the second instantiation of the machine learning model based on a first plurality of data streams within network traffic ingested at the first super node to generate one or more second outputs, the one or more second outputs including values representative of data stream characteristics and values representative of AI application node characteristics;   share the one or more second outputs with a second super node in the edge environment;   obtain one or more third outputs from the second super node, the one or more third outputs generated from a third instantiation of the machine learning model executed at the second super node based on a second plurality of data streams within network traffic ingested at the second super node, the one or more third outputs including values representative of data stream characteristics and values representative of AI application node characteristics; and   train the machine learning model using at least one of the one or more second outputs or the one or more third outputs to build a consensus nominal data stream pattern.   
     
     
         19 . The non-transitory machine readable storage medium of  claim 12 , wherein the machine learning model is a first machine learning model, wherein the outputs are first outputs, wherein the threshold value is a first threshold value, and wherein the instructions, when executed, further cause the processor circuitry to:
 instantiate a deep data inspection node in the edge environment, the deep data inspection node to have access to the target data stream;   verify at least one feature in the target data stream corresponds to at least one of a target data stream characteristic or a target AI application node characteristic;   select a second machine learning model trained on a feature set representative of at least one of at least one of the target data stream characteristic or the target AI application node characteristic;   execute the second machine learning model over a period of time at the deep data inspection node based on the target data stream to generate one or more second outputs, the one or more second outputs including at least a third value representative of a deviation condition of the target data stream; and   determine the one or more second outputs satisfy a second threshold value at least once over the period of time.   
     
     
         20 . The non-transitory machine readable storage medium of  claim 19 , wherein the instructions, when executed, further cause the processor circuitry to:
 deploy the trained second machine learning model across the deep data inspection node and one or more additional deep data inspection nodes in the edge environment; and   train the second machine learning model with at least one nominal data stream pattern shared across the deep data inspection node and the one or more additional deep data inspection nodes.   
     
     
         21 . The non-transitory machine readable storage medium of  claim 20 , wherein the instructions, when executed, further cause the processor circuitry to:
 at least one of determine the deviation condition of the target data stream occurs less than or equal to a frequency threshold, determine the deviation condition of the target data stream occurs less than or equal to a frequency threshold at the one or more additional deep data inspection nodes, or determine the deviation condition of the target data stream does not meet at least one constraint; and   cause at least one of a modification to the target data stream or a response to an attempt to consume the target data stream.   
     
     
         22 . The non-transitory machine readable storage medium of  claim 12 , wherein the instructions, when executed, further cause the processor circuitry to:
 in response to the alert being generated, at least one of tag a portion of the target data stream with metadata, implement a blockchain for at least one data point in the target data stream, or prohibit consumption of the target data stream by the target AI application node.   
     
     
         23 . A method, comprising:
 orchestrating resources in an edge environment based on ingested network traffic on an edge network, at least some of the ingested network traffic associated with at least one source node that is to source a target data stream and at least one target artificial intelligence (AI) application node that is to consume at least a portion of the target data stream; and   executing a machine learning model based on the ingested network traffic to generate one or more outputs, the one or more outputs including at least one a first value representative of a data stream characteristic or a second value representative of an AI application node characteristic;   determining the one or more outputs satisfy a threshold value; and   generating an alert in response to the outputs satisfying the threshold value.   
     
     
         24 . The method of  claim 23 , including:
 determining at least one of a content type of the target data stream, a sensitive attribute of the target data stream, a security level of the target data stream, or a source location of the target data stream; and   executing the machine learning model to determine the first value of the data stream characteristic based on the at least one of the content type, the sensitive attribute, the security level, or the source location.   
     
     
         25 . The method of  claim 23 , including:
 determining at least one of a service type attribute of the AI application node or a usage context of the target data stream for the AI application node; and   executing the machine learning model to determine the second value of the AI application node characteristic based on at least one of the service type attribute or the usage context.   
     
     
         26 - 33 . (canceled)