Patent Publication Number: US-2023164023-A1

Title: Autonomous auditing and remediation of it asset records

Description:
BACKGROUND 
     Within enterprise environments, the process of auditing and tracking information technology (IT) assets for compliance in view of asset management policies is necessary to ensure that threats, vulnerabilities, or risks to business operations are mitigated. The process of auditing, detecting, and reporting non-compliant IT assets can take months to attain compliance, and the conventionally manual process also introduces a wide variety of errors. In typical scenarios, administrators must discover and learn about assets on the network, determine whether the assets are in compliance of relevant asset management policies, and remediate any noncompliant assets, if deemed necessary. 
     SUMMARY 
     Embodiments of the present disclosure are directed to tracking and auditing information technology (IT) assets (e.g., computing devices) in a networked computing environment for compliance in accordance with asset management policies. A set of programmed rules that corresponds to defined asset management policies is employed to determine whether an IT asset record associated with a particular IT asset in the networked computing environment is in compliance with the defined asset management policies. In some instances, the IT asset record can be determined non-compliant in view of the rules because one or more pieces of device location information or device service information is incorrect or missing. To this end, the present disclosure relates to both the training and employment of machine learning models to infer or otherwise determine missing pieces of device location information or device service information associated with a particular IT asset record. 
     A machine learning model can be trained and employed to determine device location information or device service information associated with the IT asset. In order to train the machine learning model, a master dataset of IT asset information is generated based on IT asset records collected from discrete data sources within the networked computing environment, each data source storing information relating to a corresponding number of IT assets logged by (e.g., in communication with or detected by) the data source. In order to facilitate machine learning model training, certain asset attributes (i.e., features) of the IT asset records are selected based on an analysis of the records that reveals respective value distributions and inter-dependencies, among other things. Moreover, to optimize the master dataset for training, some of the selected asset attributes, such as those associated with categorical values, can be label encoded such that categories in each asset attribute are assigned a unique numerical value. The master dataset and the selected asset attributes, including the label encoded asset attributes, are employed to train a decision tree-based machine learning model that, when given an IT asset record associated with an IT asset, is employable to infer or otherwise determine one or more pieces of device location information or device service information associated with the IT asset. Based on the determination of one or more pieces of device location information or device service information associated with the determined non-compliant IT asset, the IT asset record associated with the non-compliant IT asset can be manually or autonomously updated to place the IT asset and its record into compliance. 
     Advantageously, IT asset records associated with non-compliant IT assets in a networked computing environment can be detected, collected, and ultimately remediated, or in other words updated, in near real time or on-demand to comply with defined asset management policies. Unlike conventional methods that require the manual cataloguing, reconciling, normalizing, and auditing of IT asset records, the disclosed embodiments can detect IT assets in a networked computing environment, audit IT records associated with the IT assets, determine whether IT assets are compliant in accordance with defined asset management policies, generate inferences to remediate non-compliant IT asset records, and resolve such issues with minimal or no human intervention. A machine learning model that determines these inferences is trained with analytically-selected features, thereby ensuring a high level of accuracy in the automatic remediation of determined non-compliant IT asset records. 
     This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present invention is described in detail below with reference to the attached drawing figures, wherein: 
         FIG.  1    is an exemplary system for auditing and remediating information technology (IT) asset records in accordance with some embodiments of the present invention; 
         FIG.  2    is a block diagram of an asset accuracy intelligence system (AAIS) in accordance with some embodiments of the present invention; 
         FIG.  3    is a block diagram of an inference component in accordance with some embodiments of the present invention; 
         FIG.  4    is an illustration of various histograms and distribution charts for values associated with features extracted from a master dataset in accordance with some embodiments of the present invention; 
         FIG.  5    is an illustration of various plot matrices generated based on values associated with feature pairs extracted from a master dataset in accordance with some embodiments of the present invention; 
         FIG.  6    is an illustration of a user interface of an AAIS in accordance with some embodiments of the present invention; 
         FIG.  7    is an illustration of another user interface of an AAIS in accordance with some embodiments of the present invention; 
         FIG.  8    is an illustration of yet another user interface of an AAIS in accordance with some embodiments of the present invention; 
         FIG.  9    is a flow diagram showing a method for remediating IT asset records in accordance with some embodiments of the present invention; 
         FIG.  10    is a flow diagram showing a method for training an AAIS in accordance with some embodiments of the present invention; and 
         FIG.  11    is a block diagram of an exemplary computing environment suitable for use in implementing some embodiments of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The auditing, tracking, and remediation of non-compliant information technology (IT) assets in a networked computing environment is generally a very manual and tedious process, one that is prone to error and inconsistencies. In large enterprise environments, data sources associated with a variety of network services are typically available, each of which can provide complete or incomplete records of IT assets. While these data sources provide a general snapshot of IT assets within the enterprise, the data is generally inaccurate or incomplete, having no regard to compliance, which can have negative consequences on business operations or on service restoration times in production environments. 
     While conventional systems do exist to facilitate auditing and tracking of non-compliant IT assets, such systems generally are not fully compliant with asset management policies. It can still take months to audit, detect, and report non-compliant IT assets utilizing such systems. In typical settings, an asset owner may not know the non-compliance category or have a recommendation to remediate the non-compliance issue and as such, the rate of establishing overall compliance could take an unreasonable amount of time. Moreover, larger entities may include a variety of teams that are separately responsible for tracking and maintaining IT assets. When reconciling IT asset information from numerous parties, inconsistencies or incomplete asset information can cause further delay. 
     Accordingly, embodiments of the present disclosure are directed to automatically auditing, tracking and remediating IT asset records associated with IT assets in a networked computing environment. In some embodiments, a plurality of IT asset records is collected from a diverse set of data sources. Each data source can provide various pieces of information related to one or more IT assets associated with the data source. In various embodiments, the information includes different types of IT asset-related information (e.g., attributes or features) with corresponding values, such as IP address, MAC address, hostname, and the like. This collected information can be normalized and cleaned to generate a master dataset, which includes records for a plurality of IT assets. In some instances, values for certain IT asset attributes, such as those that are non-numerical, are tokenized. Moreover, in some instances, these tokenized values or other categorical values are assigned unique numerical values, or in other words label encoded, to facilitate the ability to derive insights therefrom. 
     In some embodiments, the master dataset is analyzed to identify asset attributes or features that are useful to build either of two machine learning models, one for inferring device location information and/or one for inferring device service (or owner) information of a given IT asset (i.e., computing device) based on its IT asset record. The values for each asset attribute in the master dataset can be analyzed to determine whether the values are equally distributed or are biased. In some embodiments, if a certain asset attribute is highly-biased, it is ignored to avoid measurement errors during model training. In some further embodiments, correlations between asset attributes are analyzed to determine the existence of potential dependencies there between. When training a model employable to infer device location information (i.e., a device location inference model)), the selected asset attributes can include, by way of example, IP address or asset location. Alternatively, when training a model employable to infer device service information (i.e., a device service inference model), the selected asset attributes can include, by way of example, device ID, hostname, device name, IP address, or device service ID. 
     Depending on the model being trained, the corresponding selected plurality of asset attributes can be employed to train a decision tree-based machine learning model employable to generate inferences relating to the device location or device service associated with a particular IT asset, as described herein. To this end, in some embodiments, recommendations to update the master dataset based on identified conflicts, errors, or missing values, are generated and presented to a user so that the user can manually direct the system to update the master dataset utilizing the generated inference(s). In some other embodiments, the master dataset is autonomously updated based on the inference(s) generated from any one of the decision tree-based machine learning models. In some further embodiments, an issue ticket associated with a recommendation (e.g., a generated inference for an IT asset record) is autonomously created and then closed (i.e., flagged as addressed or completed), autonomously or manually, based on an update made in accordance with the generated recommendation. 
     The system described herein automates the IT asset data collection and compliance audits for IT assets in a networked computing environment. The ability to automate these processes ensures accuracy in IT asset data collection and auditing of IT assets with minimal to no human intervention, also vastly improving overall efficiencies. Moreover, the described system not only identifies non-compliant IT assets, but generates inferences for missing or incorrect pieces of the IT asset data with a high rate of accuracy. The utilization of data analytics and natural language processing techniques in feature selection ensures that a machine model is trained to generate such inferences both accurately and efficiently. To this end, the ability to autonomously identify and remediate non-compliant IT asset records can save significant human and computing resources. 
     Turning now to  FIG.  1   , illustrated is a high level schematic depiction of an exemplary system  100  in which some embodiments of the present disclosure may be employed. It should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) can be used in addition to or instead of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by one or more entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. 
     The system in  FIG.  1    includes a plurality of information technology (IT) asset data sources  110  that are each coupled to and in communication with a network  115  (e.g., the Internet, intranet, corporate network). Each asset data source of the asset data sources  110  can include any variety of computing devices or databases that store IT asset records (i.e., information about IT assets). In some aspects, an asset data source tracks, detects, discovers, or observes IT assets on the network  115  and generates associated IT asset records (i.e., logs) for storage thereby. An IT asset record can include asset attributes (i.e., features) associated with an IT asset, such as IP address, unique identifier, subnet, device name, hostname, MAC address, domain name, device service name, device service identifier, and the like. An asset data source can include any combination of machine, host, service, or peripheral, among other things, that stores or logs IT asset records thereon. By way of non-limiting example, an asset data source can include systems and services such as service knowledge management systems (SKMS), domain name systems (DNS), virtualization solutions (e.g., VMware), network observation tools (e.g., Hubble), address resolution protocols (ARP), and the like. 
     The system  100  also has an asset accuracy intelligence system (AAIS)  120  that includes one or more computing devices, such as the computing device later described in accordance with  FIG.  9   . In accordance with some embodiments, the AAIS  120  audits IT assets based on associated IT asset records and a defined asset management policy, flags certain IT assets (or their associated records) as non-compliant in view of the defined asset management policy, and in doing so reduces the overall audit duration. Moreover, the AAIS  120  can utilize machine learning models to determine non-compliance, recommend appropriate remediation action to a user, or even autonomously perform remediation to the non-compliant assets. 
     Like the asset data sources  110 , the AAIS  120  can also be connected to and in communication with network  115 , and can communicate with the asset data sources  110  by retrieving and/or receiving IT asset records therefrom. The AAIS  120  can receive IT asset records and generate a master dataset utilizing the IT asset records for storage into the database  125 . For purposes of properly training the machine learning models, the AAIS  120  can clean the master dataset utilizing a variety of normalization and filtering techniques, as will be described. 
     The AAIS  120  can facilitate the selection of features that are useful for model training, as well. In some embodiments, the AAIS  120  modifies one or more portions of the IT asset records in the master dataset, to facilitate the extraction of insights therefrom to facilitate feature selection. For instance, the AAIS  120  can tokenize some values of one or more features, such as categorical features, to facilitate insight derivation. In some further embodiments, the AAIS  120  analyzes the values of each feature, before or after tokenization, to facilitate the determination of a feature&#39;s utility in training the machine learning model. For example, the AAIS  120  can analyze the distribution of various feature values to determine whether the feature&#39;s data is equally distributed or biased, such that highly-biased features are ignored to avoid measurement errors during training. In another example, the AAIS  120  can determine correlational strength between various selected features to determine how much one feature might be dependent on another feature. In some embodiments, the AAIS  120  modifies one or more portions of the IT asset records in the master dataset, to facilitate model training. For instance, the AAIS  120  can label encode some of the values in the master dataset to optimize efficiency and accuracy in model training, as will be described in further detail herein. 
     The AAIS  120  can generate either or both of two machine learning models (i.e., a device location inference model, a device service inference model), each of which target the same classification problem for different asset features, specifically device location information and device service information. In accordance with some embodiments, the device location information includes IT asset information relating to the physical geographical location of where the device or IT asset is located. IT asset features relating to device location information can include IP address, device location name, or data center name, among other things. The device service, on the other hand, can include IT asset information relating to the primary purpose or usage of the device or IT asset. IT asset features relating to device service information can include a unique device identifier (“device ID”), a host name, a device name, IP address, or a device service identifier (“device service ID”), among other things. One of the goals for the machine learning model is to infer or otherwise determine the correct value of these features, particularly for the IT assets where the values are missing in the associated IT asset record. To this end, in some embodiments, the AAIS  120  utilizes decision tree-based machine learning to accomplish the foregoing tasks. 
     Having trained the machine learning model (e.g., a device location inference model, a device service inference model), the AAIS  120  can provide a variety of techniques for remediating non-compliant IT asset records. In some embodiments, the AAIS  120  receives an IT asset record of an IT asset and processes the IT asset record through a device location inference model. In other words, the AAIS  120  can determine that one or more pieces of device location information is missing, and process the IT asset record through the device location inference model. The AAIS  120  can process the IT asset record through the device location inference model to determine, or in other words make an inference of, one or more pieces of device location information. The inferred data can include device location information that is missing from the IT asset record. It is also contemplated that the inferred data can included pieces of device location information that conflicts with device location information that is already present in the IT asset record. In some other embodiments, the AAIS  120  receives an IT asset record of an IT asset and processes the IT asset record through a device service inference model. In other words, the AAIS  120  can determine that one or more pieces of device service information is missing, and process the IT asset record through the device service inference model. The AAIS  120  can process the IT asset record through the device service inference model to determine, or in other words make an inference of, one or more pieces of device service information. Similar to the device location inference model, the inference data from the device service inference model can include device service information that is missing from the IT asset record. It is also contemplated that the inferred data can include pieces of device service information that conflicts with device service information that is already present in the IT asset record. 
     In some embodiments, the AAIS  120  determines that one or more pieces of IT asset information (e.g., device location information or device service information), in an IT asset record, is missing or conflicting with inferred data, and thus responsively generates a notification that can be provided for display to a computing device  130  (e.g., a client device or a user device) coupled to the network  115 . Among other things, the AAIS  120  can provide an IT asset records management system that enables a user of the client device  130  to view the master dataset, remediate issues (e.g., flagged non-compliant assets) with the master dataset, and view other analytics information relating to the master dataset. For instance, the AAIS  120  can generate a web interface that is accessible by the client device  130 , so that a user of the client device  130  can be informed of identified issues (e.g., missing or conflicting IT asset information) and actively provide the AAIS  120  instructions to remediate the issues. The AAIS  120 , in some embodiments, receives instructions from the client device  130  to update the IT asset record with the inferred IT asset information, so that the updated IT asset record is stored in the master dataset. In some other embodiments, the AAIS  120  autonomously updates and stores the updated IT asset record with the inferred information, without any user interaction. 
     Lastly, the AAIS  120  can include or be coupled to an issue tracking component  140  via the network  115 . In some embodiments, the issue tracking component  140  includes an issue tracking or service management system, or more specifically a ticketing system, that provides users with the ability to view issues (e.g., non-compliant assets) identified by the AAIS  120 . In some instances, the issue tracking component  140  autonomously generates an issue ticket in response to the AAIS  120  determining that one or more pieces of IT asset information (e.g., device location information or device service information) is missing from an IT asset record, or that one or more pieces of such information already in an IT asset record conflicts with IT asset information inferred by the AAIS  120 . In this regard, the issue tracking component  140  can automatically generate an issue ticket and present to another computing device  150  (e.g., another client device or another user device), via a web interface, the generated issue ticket. The issue tracking component  140  can include controls in the web interface, that enables a user of the client device  150  to approve or deny a suggested or recommended update to the IT asset record. As described herein, the suggested or recommended update can included one or more pieces of IT asset information (e.g., device location information or device service information) inferred by the AAIS  120 . As such, the issue tracking component  140  can receive an approval via the web interface to update the IT asset record, and request the AAIS  120  to apply the update to the IT asset record for storage in the database  125 . In some other embodiments, the issue tracking component autonomously generates an issue ticket, automatically requests that the AAIS  120  apply the recommended update based on inferences produced thereby, and automatically closes the issue ticket so that a record of the applied update is stored in the ticketing system&#39;s records. 
     Referring now to  FIG.  2   ,  FIG.  2    illustrates a more detailed schematic depiction of an exemplary asset accuracy intelligence system (AAIS)  200  (such as the AAIS  120  of  FIG.  1   ) in which some embodiments of the present disclosure may be employed. As described in  FIG.  1   , it should be understood that this and other arrangements described herein are set forth only as examples. Other arrangements and elements (e.g., machines, interfaces, functions, orders, groupings of functions, etc.) can be used in addition to or instead of those shown, and some elements may be omitted altogether. Further, many of the elements described herein are functional entities that may be implemented as discrete or distributed components or in conjunction with other components, and in any suitable combination and location. Various functions described herein as being performed by one or more entities may be carried out by hardware, firmware, and/or software. For instance, various functions may be carried out by a processor executing instructions stored in memory. 
     The AAIS  200  of  FIG.  2    can include, at a high level, an ingestion component  210  that retrieves, extracts, receives, or otherwise obtains IT asset information from a variety of asset data sources  202 , such as the non-limiting example data sources (e.g., MAC &amp; IP Logs, Hubble, VM Data, Domain Entries, SKMS) depicted in  FIG.  2    and/or those discussed in accordance with asset data sources  110  of  FIG.  1   . The ingestion component  210  can, among other things, implement integrations with the asset data sources  202  or associated systems to fetch network infrastructure logs, or scan network configurations to discover and retrieve accurate asset information. The ingestion component  210  can also archive the discovered asset information into a data store, such as archive storage  212 . 
     In some embodiments, the AAIS  200  includes a stream data processing component  220  that can receive the discovered asset information (e.g., from ingestion component  210 ), extract the relevant asset information, and generate IT asset records in near real-time. The stream data processing component  220  can include a rule engine  222  in which an asset management policy is defined, or in other words, where corresponding rules for flagging an IT asset record for compliance or non-compliance in view of the asset management policy are defined. When an asset record is generated based on the received asset information, the asset record can be analyzed, or in other words audited, by the rule engine  222  for compliance or non-compliance in accordance with the defined asset management policy (i.e., a set of asset management rules). In some embodiments, the rule engine flags an IT asset record as compliant or non-compliant in view of the defined asset management policy, and stores the record and its compliance status (e.g., a flag or a corresponding feature value) in database  240 . 
     The AAIS  200  can also include an inference component  230  that can train any number of machine learning models (e.g., a device location inference model, a device service inference model) and employ the trained machine learning models to facilitate the inference of IT asset information, such as device location information (e.g., IP address, device location name, data center name) or device service information (e.g., device identifier, hostname, IP address, device service identifier) for a given IT asset record. In some embodiments, the inference component  230  is made accessible via one or more APIs. By way of example, if one or more pieces of device location information or device service information is missing for the asset record, then the rule engine can utilize the inference component  230  (e.g., send the IT asset record or at least a portion of the IT asset record to inference component  230 ) to determine whether the missing information can be inferred by the corresponding trained machine learning model (e.g., device location inference model for missing device location information, device service inference model for missing device service information). In some embodiments, the inference component  230  generates an inference of the missing IT asset information and sends it back to the rule engine  222  for subsequent storage as recommendation data in database  240 . It is also contemplated that in some other embodiments, all of the IT asset information is present in the asset record, and the rule engine utilizes inference component  230  to determine whether inferences can be made about the existing device location information or device service information within the IT asset record. In some instances, the inference component  230  generates an inference on one or more pieces of the IT asset information, and sends it back to the rule engine  222  for comparison with the existing IT asset information. To this end, the rule engine  222  can store, in database  240 , the audited asset data as well as recommendation data (e.g., any determined conflicting inference data). 
     In some embodiments, the AAIS  200  implements a self-service API  232  that provides access to asset records stored in database  240 , and is made accessible via a web user interface  260  from a user device  265 . To this end, the user device  265  can display, via the web user interface  260 , information relating to assets that are flagged compliant and/or non-compliant by the rule engine. In some further embodiments, the user device  265  sends instructions, to the AAIS  200 , to accept or deny recommendation data (i.e., generated inferences) associated with an IT asset record. The AAIS  200  can thus provide direct access to determined non-compliant IT asset records, so that an administrator or other user can update IT asset records with recommendation data (i.e., inferred IT asset information) generated by the inference component  230 . 
     In some further embodiments, AAIS  200  of  FIG.  2    further includes an asset compliance auditing component  250  that monitors the database  240  for IT asset records having been flagged (e.g., by rule engine  222 ) as non-compliant. In this regard, the asset compliance auditing component  250  can continuously or periodically monitor the database  240  to determine whether an IT asset record needs remediation (i.e., flagged for non-compliance) or has been remediated, among other things. In some aspects, the asset compliance auditing component  250  can be in communication with a ticketing component  270 , similar to the issue tracking component  140  of  FIG.  1   , that can automatically generate an issue ticket based on a determination that an IT asset record associated with a non-compliance flag is stored in database  240 . As discussed in accordance with  FIG.  1   , the ticketing component  270  can present a user interface that is accessible by a user  280  via an associated computing device, so that the user  280  can be made aware of the generated ticket. The ticketing component  270  can receive instructions, from the user  280 , to accept recommendation data associated with a non-compliant IT asset record, and the asset compliance auditing component  250  can update the IT asset record with the recommendation data (i.e., the missing or conflicting inferred data) for storage in the database  240 . It is contemplated that the IT asset record can be flagged as compliant once the recommendation data is applied to the IT asset record and stored, and the ticketing component  270  can automatically close the issue ticket associated with the IT asset record in response to the application and subsequent storage of the recommendation data in association with the IT asset record. 
     Looking now to  FIG.  3   , a block diagram illustrates components of an inference component as was briefly described in accordance with inference component  230  in AAIS  200  of  FIG.  2   . In some embodiments, the inference component  300  is employed to train a decision-tree based machine learning model that generates inferences about any particular IT asset based on its associated IT asset record. In some embodiments, the inference component  300  can train a device location inference model for generating inferences about device location information regarding the IT asset. In some other embodiments, the inference component  300  can train a device service inference model for generating inferences about device service information regarding the IT asset. In addition to training the decision tree models, however, the inference component  300  can also be employed to generate the training data, clean and/or normalize the training data, identify useful features for the training data, and/or optimize the training data prior to model training. To this end, the inference component  300  can include a training data collection component  310 , a training data cleaning component  320 , a feature selecting component  330 , a training data optimizing component  340 , a model training component  350 , and an inference generating component  360 , as will be described herein. 
     In some embodiments, the inference component  300  includes a training data collection component  310  that receives a plurality of IT asset records from a variety of asset data sources, such as asset data sources  110  of  FIG.  1   . The IT asset records can be retrieved or obtained directly from the asset data sources, though it is contemplated that IT asset records can be received by the inference component  300  from any combination of components described in  FIG.  2   , such as the stream data processing component  220 , database  224 , ingestion component  210 , or archive storage  212 . In various embodiments, an IT asset record in the plurality of IT asset records includes a variety of information relating to a corresponding IT asset, such as IP address, MAC address, hostname, device name, service name, or any other asset attribute or feature described in the present disclosure. The training data collection component  310  can generate a master dataset based on the obtained plurality of IT asset records. 
     The inference component  300  can also include a training data cleaning component  320  that cleans the master dataset prior to the model training process. As missing values or outliers can appear in the master dataset, the training data cleaning component  320  can remove these records to prevent issues in model training. In some embodiments, the cleaning process includes any combination of steps, such as (1) dropping (or in other words deleting) data records that do not have attributes that could uniquely identify an asset; (2) dropping data records that don&#39;t have a date; (3) dropping data records whereby more than 50% of the asset attributes (i.e., columns) are empty; (4) for string-type features, replacing null values or otherwise empty values with an “UNKNOWN” string; and (5) for numerical-type features, replacing null values or otherwise empty values with an average value of the feature. 
     In some further embodiments, the inference component  300  includes a feature selecting component  330  that ensures that asset attributes or “features” that are selected for training are useful to build the model(s). It is contemplated that the addition of redundant variables can reduce model quality, and the selection of excess features can result in undesirable complexity of the model. To this end, in some embodiments, the feature selecting component  330  analyzes the distribution of feature values to determine whether the feature data is equally distributed or is biased to any particular value. The feature selecting component  330  can identify a feature having high bias to a particular value, and thus ignore (i.e., not select) the feature for purposes of training, to avoid training measurement errors.  FIG.  4    depicts example histograms or distribution charts for values associated with a variety of features extracted from a master dataset. Utilizing these histograms, the feature selecting component  330  can analyze how the values are distributed for a particular feature, and then make a decision about whether the feature should be selected for training. More specifically, looking at a first chart  410  labeled DEVICE_MODEL_ID, the feature selecting component  330  can determine that the first chart  410  presents multiple bars representative of different values, and that the data generally includes a well-distributed array of values for the DEVICE_MODEL_ID feature. To this end, the feature selecting component  330  can select the DEVICE_MODEL_ID feature as one to include in the model training process because a determination was made that the values associated with the feature are distributed above a diversity threshold. In some embodiments, a feature is determined distributed if the feature is associated with a variation of values having at least a threshold diversity. That is, a feature can be determined distributed (i.e., above a diversity threshold) if the feature has more than a threshold number or percentage of different values (e.g., X number of different values, Y % of variability). Referencing now another example, looking at a second chart  420  labeled DEVICE_ENV_ID, the feature selecting component  330  can determine that the second chart  420  presents only one bar that corresponds to a particular value, and that the data is highly biased to the particular value. To this end, the feature selecting component  330  would not select the DEVICE_MODEL_ID feature as one to include in the model training process. In some embodiments, a feature is determined not distributed, or in other words biased to a particular value, if the feature is associated with one or more values not having a threshold diversity. That is, a feature can be determined not distributed if less than a threshold number of values for the feature is different or that the feature has less than a threshold percentage of different values. 
     The feature selecting component  330  can analyze correlations between various feature values to determine whether any one feature is dependent on another feature.  FIG.  5    depicts example plot matrices  500  generated based on values associated with pairs of features extracted from a master dataset. Utilizing these plot matrices  500 , the feature selecting component  330  can analyze how values between two particular features are correlated with one another, and then make a decision about whether the feature should be selected for training. More specifically, looking at a first plot matrix  520  identified at the intersection of features hn 0   512  and dn 0   510 , the feature selecting component  330  can determine that the first plot matrix  520  presents a strong linear relationship between the two features, and that the data indicates that there is a threshold positive correlation between the values of hn 0   512  and dn 0   510 . To this end, the feature selecting component  330  can select hn 0   512  and dn 0   510  as features to include in the model training process because a determination was made that the values associated with the features are related to one another having a threshold positive correlation. In some embodiments, a feature is determined correlated to another feature if the linear relationship between the two features is determined to have a correlation coefficient that is above a positive threshold value (e.g., +0.2, +0.5, +0.7) or below a negative threshold value (e.g., −0.2, −0.5, −0.7). 
     In some further embodiments, the inference component  300  includes a training data optimizing component  340  that modifies values for one or more features of the master dataset prior to model training. IT asset information can include a variety of numerical and non-numerical values, although the majority of IT asset information is categorical, or in other words non-numerical. For instance, IP addresses (e.g., 192.168.1.1), subnets (e.g., 255.255.0.0), domain names (e.g., adobe.com), and the like, are features that include categorical values. It is generally understood that the majority of machine learning models prefer numerical values over categorical values as input during the training phase. It is also understood that numerical values tend to generate optimal results in model training when compared to categorical values. To this end, the training data optimizing component  340  can employ label encoding, such that for any particular feature, the training data optimizing component  340  can determine a total number of categories (n classes) and assign a unique numerical value (between 0 and n−1) to each of the determined categories. 
     In some further embodiments, the training data optimizing component  340  tokenizes values of certain categorical features to facilitate the derivation of insights, like those performed by feature selecting component  330 . In some aspects, values can be tokenized into portions (e.g., words) defined by common separators, such as spaces in a sentence. Alternatively, values can be tokenized into portions (e.g., numbers) defined by periods, decimals, or other symbols, such as decimal separators in an IP address. By way of non-limiting example, an IP address of an asset record comprises a categorical value, such as 192.168.001.001. The training data optimizing component  340  can, by way of example, tokenize the IP address so that the value is broken down into useful semantic units (e.g., 192, 168, 001, 001). With reference to  FIG.  5   , the ip 3   504  feature represents the first octet of IP addresses in the master dataset. The first token (ip 3   504 ) of the IP address can be useful, independent of the remainder of the IP address, to determine whether correlations exist between the first token and other tokens or other features in the master dataset. 
     The following is a list of some exemplary attributes portrayed in  FIG.  5    and is provided solely for purposes of understanding how feature selecting component  330  can derive insights from the various features of the master dataset, and how training data optimizing component  340  can modify the master dataset for purposes of insight derivation. In  FIG.  5   , the feature dn 1   502  represents a non-numerical identifier of a Device Name (i.e., a name associated with an IT asset), and can be utilized to identify a mapping to a particular party or team that owns the IT asset. The feature ip 3   504  represents the first octet of an IP address associated with an IT asset. Though only the first octet, any other tokenized portion (e.g., other octets) of the IP address can be identified as a separate feature and analyzed. The feature hn 1   506  can represent a non-numerical identifier of a hostname (e.g., a categorical hostname), and can be utilized to identify mappings to a party or team that owns the particular IT asset. The feature dn 0   510  can represent a numerical identifier of a Device Name (i.e., the name associated with the IT asset), and can be utilized to identify a group of devices created by a particular party or team. The feature hn 0   512  can represent a numerical identifier of a hostname, and can be utilized to identify a group of devices created by a particular party or team. 
     The inference component  300  can include a model training component  350  that can train a decision-tree based machine learning model. As described, in some embodiments, the machine learning model is employable to determine or otherwise infer a device location associated with an IT asset. As described herein, a device location of an IT asset can include one or more location-relevant features relating to the IT asset, such as IP address, device location name, or data center name, among other things. In some other embodiments, the machine learning model is employable to determine or otherwise infer a device service associated with the IT asset. The device service of an IT asset, on the other hand, can include one or more service-relevant features relating to a primary purpose or usage of the IT asset, such as a unique device identifier, a host name, a device name, IP address, or a device service identifier, among other things. In various embodiments, the goal for the machine learning model is to determine or infer a value for one or more of these aforementioned features, particularly for an IT asset where IT asset information (e.g., a value for a device location or device service-type feature) is missing in an associated IT asset record. 
     In accordance with some embodiments described herein, the model training component  350  employs decision-tree methodologies to train or otherwise generate the machine learning model. The model training component  350  can request from and/or receive, from feature selecting component  330 , a selected set of features for the machine learning model employable to determine or otherwise infer IT asset information (e.g., a value for a device location or device service-type feature) for an IT asset. The model training component  350  can also request from and/or receive, from training data optimizing component  340 , that at least a portion of the IT asset records in the master dataset is tokenized and/or label encoded for purposes of training the machine learning model. In some aspects, the portion of IT asset records can be a random selection of IT records from the master dataset. To this end, the model training component  350  can utilize a larger percentage (e.g., 70%) of the portion of IT asset records to train the machine learning model, preferably utilizing a decision-tree model. In some embodiments, a remaining, smaller percentage (e.g., 30%) of the portion of IT asset records is temporarily stored to test the first machine learning model after training. 
     In various embodiments, the inference component  300  includes an inference generating component  360  that employs a trained machine learning model to generate, based on information currently stored in an IT asset record associated with an IT asset, an inference about one or more pieces of IT asset information (e.g., device location information or device service information) associated with the IT asset. In some embodiments, the inference generating component  360  receives as input an IT asset record that is missing a piece of device location information relating to the IT asset and determines that the piece of device location is missing. In some embodiments, if the inference generating component  360  determines that the missing information is device location information, the inference generating component  360  can employ the device location inference model to generate, as a result based on the IT asset record provided thereto, one or more pieces of device location information that corresponds to the missing device location information. As is also described herein, the inference generating component  360  can receive as input an IT asset record that is includes a complete set of device location information relating to the IT asset. The inference generating component  360  can employ the device location inference model to generate, as a result based on the IT asset record provided thereto, one or more pieces of device location information that corresponds to, but conflicts with, the device location information already in the IT asset record. In some aspects, the generated or otherwise inferred piece(s) of device location information can be communicated to a rule engine, such as rule engine  222  of  FIG.  2   , a database such as database  240  of  FIG.  2   , a ticketing component such as ticketing component  270  of  FIG.  2   , or otherwise provided for display to a user device such as user device  265  of  FIG.  2   . It is contemplated that an inferred piece of device location information can be stored as a recommendation to be acted upon in association with the IT asset record, so that a user (e.g., user  280  of  FIG.  2   ) can act upon the recommendation and update the IT asset record with the stored device location information. 
     Similarly, in some other embodiments, the inference generating component  360  receives as input an IT asset record that is missing a piece of device service information relating to the IT asset and determines that the piece of device service information is missing. The inference generating component  360  can employ the device service inference model to generate, as a result based on the IT asset record provided thereto, one or more pieces of device service information that corresponds to the missing device service information. As described herein, in some other embodiments, the inference generating component  360  receives as input an IT asset record that includes a complete set of device service information relating to the IT asset. The inference generating component  360  can employ the trained device service inference model to generate, as a result based on the IT asset record provided thereto, one or more pieces of device service information that corresponds to, but conflicts with, the device service information in the IT asset record. In some aspects, the generated or otherwise inferred piece(s) of device service information can be communicated to a rule engine such as rule engine  222  of  FIG.  2   , a database such as database  240  of  FIG.  2   , a ticketing component such as ticketing component  270  of  FIG.  2   , or otherwise provided for display to a user device such as user device  265  of  FIG.  2   . It is contemplated that an inferred piece of device service information can be stored as a recommendation to be acted upon in association with the IT asset record, so that a user (e.g., user  280  of  FIG.  2   ) can act upon the recommendation and update the IT asset record with the stored device service information. 
     Looking now to  FIG.  6   ,  FIG.  6    depicts an exemplary user interface  600  that can be accessed by a user device, such as computing device  130  or computing device  150 , so that a user can act upon recommendations generated by an asset accuracy intelligence system (AAIS) as described in accordance with  FIGS.  1 - 3   . In accordance with some embodiments described herein, the AAIS generates for display, among other things, a list of determined compliant or non-compliant IT assets, filtered views of the IT assets, interactive analytics relating to asset compliance rates, reports including remediation actions for non-compliant assets, recommendation accuracy of the trained machine learning models, and interactive interfaces to facilitate the rejection or acceptance of recommended remediation (i.e., updates) to be performed on determined non-compliant IT asset records. The exemplary user interface  600  depicts, among other things, an asset management section having a detailed view of IT assets determined non-compliant by the AAIS. In the illustrated example, the user interface  600  includes one or more filters  610  that enable a user of the user interface  600  to filter out IT assets based on a variety of attributes, such as owner, compliance status (e.g., compliant or non-compliant), and ticket number or status, among other things. Also depicted in the user interface  600  is an IT asset record  620  identified as non-compliant, specifically one that appears to be missing device service information. In accordance with the presented example, IP address “4.1.1.9” is identified as an un-inventoried (i.e., non-compliant) IT asset record because the IT asset record does not have a device associated with it. In other words, a rule engine (e.g., rule engine  222 ) of the system described herein may have defined therein a rule that requires each detected IP address on the networked computing environment to have a complete IT asset record, including all necessary IT asset information. Here, it appears that while an IP address has been identified, the IT asset record associated with the IP address is missing device service information. Because the IT asset record  620  is determined non-compliant, an action  630  to remedy the issue is presented alongside the IT asset record  620 . Here, the device service inference model was employed such that the action  630  suggests that the IP address “4.1.1.9” should be associated with a specific device. Below the non-compliant IT asset record  620  is a recommendation section  640  that presents a suggested remediation action to be performed. Specifically, the recommendation section  640  suggests that “Device ABCD” should be the device associated with IP address “4.1.1.9.” As depicted, the suggested remediation action (i.e., the inferred data) can also be displayed within the IT asset record  620  with an indicator (e.g., exclamation point or other icon) to distinguish the inferred data from the actual IT asset record data. Also depicted in the user interface  600  is a set of interactive action buttons or graphical user interface (“GUI”) elements that enables a user to reject  650  the recommended remediation action (i.e., to ignore the inferred data) or accept (update  660 ) the recommended remediation action (i.e., to update the IT asset record with the inferred data). 
     Looking now to  FIG.  7   ,  FIG.  7    depicts another exemplary user interface  700  that can be provided for display by the AAIS after a user interacts with the reject  650  GUI element of  FIG.  6   . That is, if a user decides to decline the recommended remediation action generated by AAIS and provides an input that corresponds to the reject  650  GUI element, then an updated user interface  700  can be generated and provided for display. In some embodiments, and as presented in exemplary user interface  700 , a predefined set of rejection reasons  710  is presented for selection by the user. That is, the user is provided with a set of selectable rationales as to why the recommended action is being rejected. In this regard, one of the rejection reasons  710  can be selected and the user can confirm his or her selection by interacting with the reject confirmation button  720 . In some aspects, the selected rejection reason can be stored and utilized as feedback to the AAIS system. 
     Looking now to  FIG.  8   ,  FIG.  8    depicts yet another exemplary user interface  800  provided for display by the AAIS after a user interacts with the update  660  GUI element of  FIG.  6   . That is, if a user decides to accept the recommended remediation action generated by AAIS and provides an input that corresponds to the update  660  GUI element, then an updated user interface  800  can be generated and provided for display. In some embodiments, and as presented in exemplary user interface  800 , the IT asset record  810  associated with IP address “4.1.1.9” is presented with the inferred location service information  820  (e.g., “Device ABCD”, “Host ABCD”, “Role A”) so that a user can confirm their desire to update the IT asset record with the inferred location service information. In some aspects, the user interface  800  can present an additional request to confirm the user&#39;s desire to update the record, such as the presented confirmation box  830 . The user interface  800  can also present an update confirmation button  840 , which can be enabled after confirmation box  830  is interacted with, so that the user can confirm his or her desire to store the inferred location service information into the IT asset record  810 . 
     Turning now to  FIG.  9   , a flow diagram is provided that illustrates a method for remediating IT asset records. Looking at block  910 , a computing system, such as an Asset Accuracy Intelligence System (AAIS) described in accordance with  FIGS.  1 - 3   , can receive an IT asset record associated with a particular IT asset. The IT asset record can include at least a first piece of IT asset information associated with the particular IT asset. In some aspects, the IT asset record can include a piece of device location information. As described herein, device location information of an IT asset can include one or more pieces of location-relevant information relating to the IT asset, such as IP address, device location name, or data center name, among other things. In some other aspects, the IT asset record can include a piece of device service information. The device service information of an IT asset, on the other hand, can include one or more pieces of service-relevant information relating to a primary purpose or usage of the IT asset, such as a unique device identifier, a host name, a device name, IP address, or a device service identifier, among other things. 
     At block  920 , the AAIS can determine that the IT asset record requires remediation based on the associated IT asset record and a defined set of asset management rules. More specifically, the AAIS can have a set of asset management rules that correspond to an asset management policy of an entity (e.g., an owner or overseeing body of the networked computing environment). In embodiments, an IT asset record received by the AAIS may need to be analyzed in view of the set of asset management rules to determine whether the IT asset associated with the IT asset record is complaint or non-compliant in accordance with the defined set of asset management rules. In some cases, the set of asset management rules can require that all device location information of a particular IT asset be present in its associated IT asset record. That is, the set of asset management rules can define a requirement that all features, such as IP address, device location name, and data center name, be defined or stored in an IT asset record associated with a particular IT asset. As such, if any one particular piece of device location information is not defined in the IT asset record, the AAIS can flag the IT asset as non-compliant and thus requiring remediation. 
     Similarly, in some other cases, the set of asset management rules can require that all device service information of a particular IT asset be present in its associated IT asset record. That is, the set of asset management rules can define a requirement that all features, such as a unique device identifier, a host name, a device name, IP address, or a device service identifier, be defined or stored in an IT asset record associated with a particular IT asset. As such, if any one particular piece of device service information is not defined in the IT asset record, the AAIS can flag the IT asset as non-compliant and thus requiring remediation. In accordance with some embodiments, the AAIS generates a ticket associated with the IT asset, indicating that the IT asset and its associated IT asset record are non-compliant. 
     At block  930 , the AAIS can utilize a machine learning model to determine at least a second piece of IT asset information associated with the particular IT asset. Because the IT asset was determined non-compliant, the second piece of IT asset information being sought can correspond to one or more pieces of device location or service information that is missing from the IT asset record. As is described in accordance with some embodiments herein, the AAIS can train, utilizing an inference component as described in accordance with inference component  300  of  FIG.  3   , one or more machine learning models utilizing IT asset records collected from a plurality of asset data sources, such as asset data sources  110  of  FIG.  1   . In some embodiments, a device location inference model can be trained and employed to determine or otherwise infer device location information associated with the IT asset. In some other embodiments, a device service inference model can be trained and employed to determine or otherwise infer device service information associated with the IT asset. The goal for either machine learning model is to determine or infer a value for one or more of these features, particularly for an IT asset where a value for a device location-type or device service-type feature is missing in an associated IT asset record. To this end, the AAIS can process, utilizing a trained machine learning model (i.e., device location inference model, device service inference model), the IT asset record including at least the first piece of IT asset information. In response, the trained machine learning model (i.e., device location inference model, device service inference model) can generate one or more inferences that correspond to at least a second piece of IT asset information associated with the particular asset. That is, if the first piece of IT asset information is a piece of device location information, the device location inference model can be employed to generate the second piece of IT asset information that can include another piece of device location information that is missing from the IP asset record. Similarly, if the first piece of IT asset information is a piece of device service information, the device service inference model can be employed to generate the second piece of IT asset information that can include another piece of device service information that is missing from the IT asset record. In accordance with some embodiments, the AAIS can modify the ticket associated with the IT asset, so that the ticket can reflect a recommended update (i.e., the second piece of IT asset information) to remediate the IT asset record. 
     At block  940 , the AAIS can update the IT asset record based on the determined second piece of IT asset information. In other words, the piece of IT asset information determined missing from the IT asset record and causing the IT asset record to be non-compliant can be stored in the IT asset record. In some embodiments, the AAIS presents the IT asset record and the second piece of IT asset information to a user, so that the user can employ a user device, such as user device  130  of  FIG.  1   , to provide instructions to the AAIS to accept the inference and update the IT asset record. In some other embodiments, the AAIS autonomously accepts the inference and updates the IT asset record to store the second piece of IT asset information therein. In some other embodiments, the AAIS autonomously accepts the inference to update the IT asset record and closes the ticket associated with the IT asset, so that the closed ticket can reflect the fact that the recommended update was applied to the IT asset record. 
     Turning now to  FIG.  10   , a flow diagram is provided that illustrates another method for remediating IT asset records. As was described in accordance with block  930  of  FIG.  9   , the AAIS can utilize a machine learning model to determine, based on at least a first piece of IT asset information associated with a particular IT asset, at least a second piece of IT asset information associated with the particular IT asset. Because the IT asset was determined non-compliant by the AAIS in block  920  of  FIG.  9   , the second piece of IT asset information being sought can correspond to one or more pieces of IT asset information (e.g., device location information or device service information) that is missing from the IT asset record. As is described in accordance with some embodiments herein, the AAIS trains, utilizing an inference component as described in accordance with inference component  300  of  FIG.  3   , a machine learning model utilizing IT asset records collected from a plurality of asset data sources, such as asset data sources  110  of  FIG.  1   . 
     The trained machine learning model can include a device location inference model employable to determine or otherwise infer device location information associated with the IT asset or device service inference model employable to determine or otherwise infer device service information associated with the IT asset. As described herein, the goal for the machine learning model is to determine or infer a value for one or more of features, particularly for an IT asset where a value for a device location or device service-type feature is missing in the associated IT asset record. To this end, the AAIS can process, utilizing a trained machine learning model, the IT asset record including at least the first piece of IT asset information. In response, the trained machine learning model can generate one or more inferences that correspond to at least a second piece of IT asset information associated with the particular asset. That is, if the first piece of IT asset information is a piece of device location information, the device location inference model can be employed to generate a second piece of IT asset information that includes another piece of device location information that is missing from the IP asset record. Likewise, if the first piece of IT asset information is a piece of device service information, the device service inference model can be employed to generate a second piece of IT asset information that includes another piece of device service information that is missing from the IT asset record. 
     In some embodiments, the AAIS, or more specifically an inference component like inference component  300  of  FIG.  3   , receives a plurality of IT asset records (e.g., training data) collected from a variety of asset data sources, such as asset data sources  110  of  FIG.  1   . As described in accordance with training data cleaning component  320  of  FIG.  3   , the AAIS can normalize or otherwise clean the plurality of IT asset records utilizing a variety of techniques, such as deleting data records that do not have attributes that could uniquely identify an asset, dropping data records that don&#39;t have a date, dropping data records whereby more than 50% of the asset attributes (i.e., columns) are empty, replacing null values or otherwise empty values with an “UNKNOWN” string for string-type values, or replacing null values or otherwise empty values with an average value of the feature for numerical-type values. 
     The AAIS can further alter or otherwise modify the cleaned plurality of IT asset records to optimize either feature selection or model training, as described in accordance with training data optimizing component  340  of  FIG.  3   . By way of example, the AAIS can tokenize one or more features in the cleaned plurality of IT asset records, particularly if the values for such features are categorical (i.e., non-numerical). In some aspects, categorical values such as IP addresses, subnets, domain names, and the like, can be tokenized so that portions thereof (e.g., octets of an IP address) can be separated into independent features. Moreover, in some other aspects, categorical values can be label encoded, such that for any particular feature, the AAIS can determine a total number of categories (n classes) and assign a unique numerical value (between 0 and n−1) to each of the determined categories. 
     After the cleaned plurality of IT asset records is optimized, the AAIS can derive insights from the records to select optimal features for model training. As described in accordance with feature selecting component  330  of  FIG.  3   , in some embodiments, the AAIS analyzes the distribution of values for any feature of the IT asset records to determine whether the data for the feature is equally distributed or is biased to any particular value. In cases where the data is determined biased to a particular value for a feature, the feature can be ignored and not selected for model training. On the other hand, where data is determined sufficiently distributed (i.e., having at least a threshold number of different values or a threshold percentage of different values), the AAIS can select the feature for purposes of model training. In some other embodiments, the AAIS analyzes correlations between various feature values to determine whether any one feature is dependent on another feature. More specifically, the AAIS can determine whether two features have a positive or negative correlation there between, and select the features if the correlations meet a threshold correlation value, as described in accordance with feature selecting component  330 . 
     Looking at block  1010  of  FIG.  10   , the AAIS can receive the cleaned plurality of IP asset records and the set of features selected from the cleaned plurality of IP asset records. In some cases, the set of features is selected based on correlational relationships identified between features of the IP asset records. In other cases, the set of features is selected based on diversity of values in the various features of the IP asset records. In yet some other cases, the set of features is selected based on a combination of identified correlational relationships and diversity. 
     At block  1020 , the AAIS can train a decision tree-based machine learning model, as similarly described in accordance with model training component  350  of  FIG.  3   , training the model with the cleaned plurality of IP asset records and the selected set of features. The trained machine learning model can be employed to determine, based at least in part on a first piece of IT asset information in an IT asset record associated with a particular IT asset, at least a second piece of IT asset information for the IT asset record associated with the particular IT asset. As similarly described in relation to blocks  910 - 940  of  FIG.  9   , the trained machine learning model can facilitate the autonomous remediation of IT asset records in accordance with the various embodiments described herein. 
     Having described embodiments of the present disclosure, an exemplary operating environment in which embodiments of the present disclosure may be implemented is described below in order to provide a general context for various aspects of the present disclosure. Referring initially to  FIG.  11    in particular, an exemplary operating environment for implementing embodiments of the present disclosure is shown and designated generally as computing device  1100 . Computing device  1100  is but one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the disclosed embodiments. Neither should the computing device  1100  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated. 
     The embodiments described herein may be described in the general context of computer code or machine-useable instructions, including computer-executable instructions such as program modules, being executed by a computer or other machine, such as a personal data assistant or other handheld device. Generally, program modules including routines, programs, objects, components, data structures, etc., refer to code that perform particular tasks or implement particular abstract data types. The embodiments described herein may be practiced in a variety of system configurations, including hand-held devices, consumer electronics, general-purpose computers, more specialty computing devices, etc. The embodiments described herein may also be practiced in distributed computing environments where tasks are performed by remote-processing devices that are linked through a communications network. 
     With reference to  FIG.  11   , computing device  1100  includes a bus  1110  that directly or indirectly couples the following devices: memory  1112 , one or more processors  1114 , one or more presentation components  1116 , input/output (I/O) ports  1118 , input/output components  1120 , and an illustrative power supply  1122 . Bus  1110  represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of  FIG.  11    are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, processors have memory. The inventor recognizes that such is the nature of the art, and reiterates that the diagram of  FIG.  11    is merely illustrative of an exemplary computing device that can be used in connection with one or more embodiments of the present disclosure. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “hand-held device,” etc., as all are contemplated within the scope of  FIG.  11    and reference to “computing device.” 
     Computing device  1100  typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by computing device  1100  and includes both volatile and nonvolatile media, and removable and non-removable media. By way of example, and not limitation, computer-readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computing device  1100 . Computer storage media does not comprise signals per se. Communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media. 
     Memory  1112  includes computer-storage media in the form of volatile and/or nonvolatile memory. The memory may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard drives, optical-disc drives, etc. Computing device  1100  includes one or more processors that read data from various entities such as memory  1112  or I/O components  1120 . Presentation component(s)  1116  present data indications to a user or other device. Exemplary presentation components include a display device, speaker, printing component, vibrating component, etc. 
     I/O ports  1118  allow computing device  1100  to be logically coupled to other devices including I/O components  1120 , some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc. Also contemplated are Internet of Things (“IOT”) devices or components, any of which can include one or more sensors or actuators, among other things. The I/O components  1120  may provide a natural user interface (NUI) that processes air gestures, voice, or other physiological inputs generated by a user. In some instances, inputs may be transmitted to an appropriate network element for further processing. An NUI may implement any combination of speech recognition, stylus recognition, facial recognition, biometric recognition, gesture recognition both on screen and adjacent to the screen, air gestures, head and eye tracking, and touch recognition (as described in more detail below) associated with a display of the computing device  1100 . The computing device  1100  may be equipped with depth cameras, such as stereoscopic camera systems, infrared camera systems, RGB camera systems, touchscreen technology, and combinations of these, for gesture detection and recognition. Additionally, the computing device  1100  may be equipped with accelerometers or gyroscopes that enable detection of motion. The output of the accelerometers or gyroscopes may be provided to the display of the computing device  1100  to render immersive augmented reality or virtual reality. 
     As can be understood, embodiments of the present disclosure provide for, among other things, autonomously remediating IT asset records in accordance with a defined asset management policy. The present disclosure has been described in relation to particular embodiments, which are intended in all respects to be illustrative rather than restrictive. Alternative embodiments will become apparent to those of ordinary skill in the art to which the present disclosure pertains without departing from its scope. 
     From the foregoing, it will be seen that embodiments described herein are well adapted to attain all the ends and objects set forth above, together with other advantages which are obvious and inherent to the system and method. It will be understood that certain features and subcombinations are of utility and may be employed without reference to other features and subcombinations. This is contemplated by and is within the scope of the claims.