AUTOMATED PRIVACY PROFILING FRAMEWORK FOR MACHINE LEARNING WORKSPACES

One example method includes automatically scanning, at a privacy data collector, received data to determine if the received data is related to an Artificial Intelligence (AI)/Machine Learning (ML) workspace that is used to build an ML model. For the received data that is determined to be related to the AI/ML workspace, parsing the data, by the privacy data collector, to determine if the data includes any Personal Identifiable Information (PII) or other sensitive information. For the data that includes PII data or other sensitive data, generating, by a ML classification model, a privacy classification for the data. For the classified data, performing, by a data masking component, a data masking operation on the PII data or other sensitive data to generate masked data.

FIELD OF THE INVENTION

Embodiments of the present invention generally relate to machine learning (ML) models. More particularly, at least some embodiments of the invention relate to systems, hardware, software, computer-readable media, and methods for the detection and removal of Personal Identifiable Information (PII) in workspaces that implement ML models.

BACKGROUND

The Machine Learning (ML) subfield of Artificial Intelligence (AI) provides automated methods for analysis of large sets of data that are too hard to program by hand. Various organizations like Amazon, Google, Microsoft, and VMWare have provided customers access to AI/ML Software Interfaces allowing them to easily embed their business use-cases into ML tasks on the vendors' platform. These ML as a service engine/tool lets data practitioners train classifiers, build machine learning models, serve them as APIs, etc., on public infrastructures and let others query results on their data. Some of these ML applications require private individuals Personal Identifiable Information (PII) data or credentials to data sources that would get access to restricted or highly restricted data, consequently exposing them to insider threat at these companies or outside threat to companies owning this data.

A Data Breach report published in 2022 indicated that the average data beach cost USD 4.35 million in comparison to USD 3.86 million on 2020. Various organizations are deploying a zero-trust approach as the companies which do not deploy zero trust incurred more than USD 1 million in cost. Breaches at organizations leveraging AI and automation tools cost USD 3.05 million less than at organizations without those tools. The report additionally indicates that compromised credentials, phishing, and cloud misconfiguration were the top attack vendors for enterprises and that Security AI alongside privacy had the biggest cost-mitigating effect. Extended detection and response technologies helped an average of 29 days in breach response time. A data breach/compromise incident occurs when there is a possibility of loss, theft, or disclosure of PII data, credentials, or highly confidential and sensitive information without the awareness of duty-related personnel or signing an NDA (Non-Disclosure Document).

To understand how machine learning works, it is important to understand how the data operates. Secure and Compliant Data of high quality is necessary for models to operate efficiently and generate the business value of data practitioners.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Embodiments of the present invention generally relate to machine learning (ML) models. More particularly, at least some embodiments of the invention relate to systems, hardware, software, computer-readable media, and methods for the detection and removal of Personal Identifiable Information (PII) in workspaces that implement ML models.

One example method includes automatically scanning, at a privacy data collector, received data to determine if the received data is related to an Artificial Intelligence (AI)/Machine Learning (ML) workspace that is used to build an ML model. For the received data that is determined to be related to the AI/ML workspace, parsing the data, by the privacy data collector, to determine if the data includes any Personal Identifiable Information (PII) or other sensitive information. For the data that includes PII data or other sensitive data, generating, by a ML classification model, a privacy classification for the data. For the classified data, performing, by a data masking component, a data masking operation on the PII data or other sensitive data to generate masked data.

In particular, one advantageous aspect of at least some embodiments disclosed herein provide an automated framework to classify and annotate data utilizing PCI and ISO, compliance, and security knowledge bases. Using Deep Neural Networks alongside reinforcement learning techniques, input data present in AI/ML workspaces like JupyterLab, Airflow, JupyterHub or other python-based workspaces, are classified to internal, public, or restricted data. Then, the embodiments leverage a platform to mask the data for all the existing documents and in case of any new events (file additions on the workspace staging directory), the process rinses and repeats. Ultimately, data practitioners do not have to be concerned about GDPR or PCI compliance requirements as the masked data is staged and written back to disk in an automated fashion, and they can continue to focus on optimizing their application code.

A. Context for an Example Embodiment of the Invention

In general, private data is being used for a variety of machine learning (ML) applications. Search queries, browsing data, posts in social media, the videos that are watched, images that are viewed, and movie preferences are being collected and stored on a daily basis. This data collection happens via computers, devices on the edge, and in homes and offices. With the rise of these data collection forums, analytics has become one of the biggest drivers of business value. Analytics platforms rely heavily on ML to derive these actionable insights.

While ML algorithms leveraged on AI/ML workspace tools offer great insights, they completely depend on the data that is being fed during the learning and pre-processing stage. To build and deliver these machine learning capabilities, it is not unusual for organizations to hire, and groom dedicated machine learning engineers, cybersecurity engineers, product application security groups and legal teams to carefully analyze if the data that is being fed from external data sources into the platform is secure, compliant, and sanitized for consumption. By incorporating Privacy by Design (PbD) principles, ML system design teams can develop systems and processes that maintain and enhance the business value from data while maintaining the confidence and trust of the consumer.

Privacy by Design gives organizations and professionals the strategies and techniques to take a proactive approach to implementing privacy considerations. Its scope includes cybersecurity and privacy risk, privacy engineering, and privacy protection in any IT system design. It mandates that any system, process, or infrastructure that uses personal data consider privacy throughout its development lifecycle and identify possible risks to the rights and freedoms of the data subjects and minimize them before they can cause actual damage

There are three core components involved while incorporating PbD principles. They are:1) Minimize: Data processed should be restricted to the minimal amount necessary2) Hide: Privacy data and their interrelationships should be hidden from plain view3) Inform: Data subjects should be informed how their privacy information is processed, for what purpose, and by what means.

B. Example Problems that May be Addressed by an Embodiment of the Invention

Data scientists and architects need to decide on what is the best possible approach to fetch data, build models and run inference on top of the pre-built model. Data scientists must be an expert in Containers, Kubernetes, Data Security, Endpoints, Scaling, Persistent Volumes, GPUs DevOps, Programming in new languages, and tools, for example. Privacy and compliance for data introduced into AI/ML containers deployed on Kubernetes is assumed.

However, as data practitioners aspire to operate at scale to improve their model accuracy and have recurrent feedback loops back and forth various components in the data platform stack, the data breach surface area increases tremendously alongside data volume.FIG.1is illustrative of these problems, as it discloses an example architecture100that is used for deploying AI/ML applications as containers on top of Kubernetes.

With reference now toFIG.1, a schematic block diagram of the example architecture100for deploying AI/ML applications as containers on top of a container orchestration platform in accordance with embodiments disclosed herein is shown. For example, development, security, and operations (DevSecOps) module102can provide development, security, and operations elements. Techniques relating to continuous-integration (CI) and/or continuous delivery (CD) can be employed.

DevSecOps module102, an example of which can be Gitlab, can communicate with identity module104, an example of which can be Vault; data artifacts module106, which can be an image repository; and KOA identity platform108, which ultimately communicate with platform APIs110, orchestrator APIs114, and the other deployed APIs, which in this case are AI/ML APIs118.

It is noted that platform APIs110can communicate with artifact storage112, and orchestrator APIs114or AI/ML APIs118can communicate with AI/ML object store116. The various APIs (e.g., platform APIs110, orchestrator APIs114, and AI/ML APIs118) can be containerized and run on top of container orchestration platform120. In some embodiments, container orchestration platform120can provide containers-as-a-service (CaaS).

Typical sets of platform APIs110and orchestrator APIs114can be deployed on top of container orchestration platform120. Additionally, certain AI/ML APIs118can be deployed as well, examples of which can include: an application or API for persisting the data in a cluster of the container orchestration platform120(e.g., persistent volume claim (PVC)), deployment for hosting the application, service for accessing the application internally within the cluster, ingress for allowing external traffic to access the AI/ML AIPs118.

Platform APIs110and Orchestrator APIs114can be leveraged to deploy any AI/ML APIs118(e.g., AI/ML workspaces119) on top of container orchestration platform120clusters. These operations can also be leveraged by using open-source helm charts or docker images on top of the existing environment. Credentials required to securely access various components of the infrastructure can be stored in a Key-Value store such as identity module104(e.g., Vault). The images required for provisioning workspaces, images, pipelines, and so forth can be stored in a registry service like Harbor. Every interaction between components in shared APIs can also be secured using KOA identity platform108.

In more detail, platform APIs110can handle requests sent by a data practitioner. These requests can be routed via the Platform API110in the enterprise infrastructure, which can classify the request type sent by the user and store associated metadata relied upon by other elements or modules.

With regard to orchestrator APIs114, based on the request received from Platform APIs110, Orchestrator APIs114can interact with a server of the container orchestration platform120cluster directly. Such can fulfill the specification of creating workspaces, accessing services or frameworks within the infrastructure, or launching jobs using CPUs or GPUs. A set of Orchestrator controllers can execute the containers needed to complete the pipeline specified by the user.

In summary, in the example architecture100for deploying AI/ML applications as containers on top of a container orchestration platform typical sets of Kubernetes APIs get deployed on top of clusters. Thus, there are four components deployed via AI/ML Platform and Orchestrator APIs:One for persisting the data in the Kubernetes cluster (Persistent Volume Claim/Persistent Volume [PVC])Deployment for hosting the applicationService for accessing the application internally within the clusterIngress for allowing external traffic to access the AI/ML application.

As captured inFIG.1, the container images are securely fetched from registry like Harbor and deployed via CI/CD solutions like Gitlab. The environment variables necessary to deploy these applications are fetched from Vault during runtime. In case there are any open-source packages that needs to be leveraged for running certain AI/ML workloads, they are fetched from the JFrog artifactory. The data currently stored in AI/ML workspaces are accessible across multiple clusters via network attached storage component (NFS). Some of the artifacts relevant to ML are stored and retrieved from ECS Object store.

As mentioned previously, the example architecture100for deploying AI/ML applications as containers on top of a container orchestration platform the data breach surface area increases tremendously alongside data volume. In order to prevent data breaches or to reduce the unwanted sharing of PII data, various systems have been developed. However, there are several problems with existing systems for preventing data breaches and the unwanted sharing of PII data.

One problem is that is difficult and resource intensive to perform manual data classification. It is not unusual for companies to hire dedicated SMEs to classify and understand if the data used for pre-processing, training, and building ML models is highly sensitive or restricted information. Another problem is categorical labeling. A data annotation process is tedious and requires meticulous inspection to whether input data sources in AI/ML workspaces has PII data and passwords for external data sources or not. It requires human intervention and is a time-consuming process. A further problem is policy and regulation changes. The need to choose the appropriate action plan for fixing data that does not comply with regulation rules is another demanding task for data scientists. A final problem is there is usually missing automation on what needs to be done on a periodic basis as data alongside compliance processes constantly evolve and expands on the AI/ML field. Expecting data practitioners to keep their data quality up to date with information governance rules and policies is a challenging process.

C. Detailed Description of an Example Embodiment of the Invention

The embodiments provided herein provide for a Preserving Privacy Engine (PPE) framework that provides users with an automated privacy compliance framework for any AI/ML data of csv, json, parquet, py or ipynb formats that are used in the example architecture100for deploying AI/ML applications as containers on top of a container orchestration platform.

The PPE of the embodiments disclosed herein integrated within the entire machine learning development lifecycle simplifies the identification of data that has enterprise-wide risk profile, secures it by automatically masking with consistency and delivers it to data scientists' environments before it becomes a serious threat to the organization. This framework enhances the compliance of popular AI/ML workspaces (e.g., AI/ML workspaces119) like Jupyter Notebooks, Jupyter hub or Airflow, by creating an awareness for users on the possibility of data breaches when restricted information is brought in and gives mechanisms to fix them before it breaches Service Level Agreements (SLAs).

FIG.2shows an embodiment of how the PPE of the embodiments disclosed herein is integrated into the architecture100for deploying AI/ML applications as containers on top of a container orchestration platform ofFIG.1. As illustrated,FIG.2shows a PPE202that is integrated between the platform APIs110and the AI/ML APIs118that are associated with the AI/ML workspaces119. Thus, the PPE202provide a framework for determining if any data input by users for use in the AI/ML workspaces119contains any PPI and if so, provides a mechanism to remove the PII so that the data scientists are able to continue to use the data in AI/ML workspaces119without the need to worry about potential data breaches of the PII.

The PPE202framework ofFIG.2, in some embodiments, does at least the following five key functionalities seamlessly:1. Fetch a current set of documents of csv, json, parquet, py or ipynb formats from AI/ML workspaces119like Jupyter Notebooks or Airflows.2. Consolidate privacy compliance recommended PCI and ISO knowledge base data.3. Profile a list of documents and annotate the documents that are compliant vs non-compliant via neural networks with reinforcement learning techniques to improve classification accuracy.4. Mask the secure data via a masking engine such as Delphix to ensure that the data is protected and to prevent a data breach for data with sensitive information.5. Scan and profile any newly added documents into the AI/ML platform and synchronize it in an event-driven fashion.

C.2 Example Architecture According to One Embodiment of the Invention

With attention now toFIG.3, an example embodiment of a PPE300that corresponds to the PPE202and the use of the PPE300is illustrated. As illustrated, a data scientist302may access the architecture100for deploying AI/ML applications as containers on top of a container orchestration platform ofFIG.1to build an ML model using AI/ML workspaces119such as JupyterLab or a JupyterHub. In building the ML model, the data scientist302may use various data sources that are available to the AI/ML workspaces119. For example, the data scientist302may use IDE data304from his or her intelligent development environment (IDE), may use public ML data306that is publicly available over the internet or from a public repository or the like associated with the architecture100, or may use private ML data308from an internal or private repository or the like associated with the architecture100.

The IDE data304, the public ML data306, and/or the private ML data308is accessed by a Privacy Data Collector (PDC)310that is a component of the PPE300. In operation, the IDE data304, the public ML data306, and/or the private ML data308is continuously scanned by the PDC310to determine if the data includes any data related to AI/ML processes or other data science (DS) processes. The data is also scanned to see if it includes any PII data, passwords, sensitive data, or non-sensitive data. The data that is determined to be related to AI/ML processes, that includes PII data, passwords, or is considered sensitive data is included in a data file312.

In some embodiments, as will be explained in more detail to follow, the PDC310scans metadata of the IDE data304, the public ML data306, and/or the private ML data308to see if the metadata is related to AI/ML/DS processes or includes PII data, passwords, or is considered sensitive data. The PDC310also maintains a PII metadata log311that includes common types of data that are AI/ML/DS processes and that includes PII data, passwords, or sensitive data. The PII metadata log311is generated based on data received from the public sources such as the internet or public repositories. The PII metadata log311is used by the PDC310to determine which of the accessed IDE data304, the public ML data306, and/or the private ML data308should be included in the data file312.

The data file312may include different types of data that has different levels of privacy concerns. In addition, some of the data in the data file312may not actually have any privacy concerns as this data may have been selected by the privacy data collector in error. Accordingly, the PPE300includes a Privacy Classification Stage (PCS)314that is used to classify the data in the data file312and then to label the data. As illustrated, the PCS314includes a ML classification model316that is used to classify the data file312. In one embodiment, the ML classification model316is a multi-class Support Vector Machine (SVM). However, other ML classifiers can be used to implement the ML classification model316.

In operation, the ML classification model316accesses one or more privacy rules320that are stored in privacy knowledge base store318. The privacy rules320specify existing privacy rules that are in force in the location of the data scientist302when he or she is building the ML module using the AI/ML workspaces119. The privacy rules may be required by governmental agencies or by business organizations. Examples of the privacy rules320include, but are not limited to, the European General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), the Payment Card Industry Data Security Standard (PCI), and the International Organization for Standardization Data Security Standard (ISO). The privacy knowledge base store318is continuously updated with any new privacy rules or with changes to existing privacy rules. Thus, the data scientist302need not keep up on which privacy rules are currently in effect in the location he or she is working.

The ML classification model316also accesses historical data322from the privacy knowledge base store318. The historical data322includes historical examples of data files312that have been classified by the ML classification model316. The historical data322can be used along with the privacy rules320to train the ML classification model316.

FIG.4illustrates an embodiment of an example ML network400that is configured to use historical data to train a ML classification model. As illustrated, the ML network400, which can be implemented on a computing system that is the same as the computing system implementing the architecture100or implemented on a different computing system, includes a privacy knowledge base store402that may correspond to privacy knowledge base store318and that has stored thereon privacy rules403that may correspond to the privacy rules320and historical data404, which may correspond to the historical data322. The privacy rules403and the historical data404is processed by a feature extractor406configured to extract features from the privacy rules403and the historical data404. The features extracted by the feature extractor406may include the different classification labels based on the types of data and the relevant privacy rules. The extracted features are then used by a machine-learning module408to train a ML classification model410, which may correspond to the ML classification model316previously described.

Returning toFIG.3, the ML classification model316determines a privacy classification for the data file312and a corresponding label based on the privacy rules320and the type of data included in the data file312. For example, in one embodiment the ML classification model316may determine if the data file312should be given a privacy classification of “internal data”, a privacy classification of “restricted data”, a privacy classification of “highly restricted data”, or a privacy classification of “not restricted data”. Thus, data with little or no PII data may be a privacy classification of and labeled as not restricted. However, data with a high amount of PII data may be a privacy classification of and labeled as restricted or highly restricted. Data that is mostly data internal or private to an organization may be a privacy classification of and labeled as internal. Of course, other classifications and labels may be determined by the ML classification model316as operating circumstances warrant. The PCS314then generates privacy classified data324that represents the data of the data file312that has now been given a privacy classification by the ML classification model316.

The privacy classified data324is then accessed by a Data Masking Component (DMC)326of the PPE300. In operation, the DMC326determines if privacy classified data324has been properly classified. In addition, the DMC326performs a data masking operation on the privacy classified data324. The data masking operation modifies any PII, or other sensitive data included in the privacy classified data324in such as way that it is of little or no value to unauthorized users while still being usable by the data scientist302in the building of the ML model. Accordingly, any reasonable masking operation may be performed by the DMC326to mask the privacy classified data324. The masking operation generates masked data328. The masked data328is then returned to the AI/ML workspaces119so that the data scientist302can continue to build the ML models and orchestrate then as workflows using the other tools of the architecture100.

In some embodiments, any privacy classifications that are determined to be incorrect are added to the historical data322so as to be used in further training of the ML classification model316. In addition, correct privacy classifications are also added to the historical data322so as to be used in further training of the ML classification model316.

It will be appreciated that the operation of the PPE300is automatically done. Thus, the data scientist302need not worry about whether or not privacy rules are being violated during the building of the ML models. In addition, any time new IDE data304, the public ML data306, and/or the private ML data308is used or any existing IDE data304, the public ML data306, and/or the private ML data308is modified, the operation of the PPE300is automatically performed, resulting in the masking of any PII or sensitive data in the new or modified IDE data304, the public ML data306, and/or the private ML data308.

Briefly then, the example PPE300according to one embodiment of the invention may be implemented to comprise various components. These components may include the PDC310, the PCS314, and the DMC326. These components, which may each comprise a respective ML model to carry out their respective functions, are considered in turn below.

C.2.1 Aspects of an Example PDC

FIG.5Aillustrates an example embodiment of the PDC310. As shown inFIG.5A, the example embodiment of the PDC310receives the IDE data304from the intelligent development environment (IDE) of the data scientist302, the public ML data306from the internet or public repositories, and the private ML data308from the private repository such as a Gitlab repository and places the data in a notebook collector502. For example, the IDE data304is placed in a directory508, the public ML data306is placed in a directory504, and the private data is placed in a directly506of the netbook collector502.

In one embodiment, GitHub API, is a daily service that is run to retrieve all notebooks on repositories with topics relevant to AI/ML/DS. Gitlab webhook is another service which is executed to unveil anyone who recently started to work on AI/ML/DS projects and that has introduced sensitive or in-sensitive data into the platform. Though it retrieves a lot of unlikely candidates, this helps optimize the topic selection problem. Every repository is scarped during every “Push” that is made within the Gitlab repository. Given that not every user utilizes version control tools such as Gitlab or GitHub, IDEs on private cloud also is monitored for any file event on the path, the data scientist302is working on.

As shown at510, the PDC310accesses the pubic data sources such as the internet or public repositories to continuously generate and then maintain a PII metadata log512, which may correspond to the PII metadata log311, that includes a common PII metadata list514that is comprised of the top PII and other sensitive information found on notebook collectors of the internet and the public repositories having topics relevant to AI/ML/DS.

FIG.5Billustrates an example embodiment of the common PII metadata list514. As illustrated, the common PII metadata list514lists metadata516that is included in the notebook collectors having topics relevant to AI/ML/DS and the number of times518that the metadata516is used. Thus,FIG.5Bshows full name metadata520is used 90k times as shown at522, social security number (SSN) metadata524is used 50k times as shown at526, and driver's license number metadata528is used 20k times as shown at530. The ellipses illustrate that there can be any number of additional PII metadata types532included on the common PII metadata list514and that this metadata can be used any number of time as illustrated by the ellipses534.

FIG.5Cillustrates some of the types of metadata that can be included in the common PII metadata list514as part of the additional PII metadata types532. For example, the additional PII metadata types532include a name540, account number information542, address information544, vehicle information546, birthdate548, medical and health information550, email address552, mobile phone number554, phone and fax number556, website information558, geographic information560, photographs with full face features562, social security information564, and biometric identifiers566. It will be appreciated that the PII metadata types shown inFIG.5Care by way of example only and thus any number of additional PII metadata types may also be included in the common PII metadata list514.

Returning toFIG.5A, the common PII metadata list514is used to is used by the PDC310to determine513which of the accessed IDE data304, the public ML data306, and/or the private ML data308includes the PII data found on the common PII metadata list514. In other words, the metadata of the IDE data304, the public ML data306, and/or the private ML data308is evaluated and must have at least one artifact that matches PII on the continuously maintained common PII metadata list514. This occurs whenever the notebook collector502is saved. The data in the notebook collector502matching the metadata of the common PII metadata list514is sent as data file312to a queue330.

C.2.2 Aspects of an Example PCS

FIG.6illustrates an example embodiment of the PCS314. As shown inFIG.6, the example embodiment of the PCS314retrieves the data file312for privacy classification from queue330, and splits602the input data frames. After splitting the input data frames, every cell within the AI/ML workspaces119is split604. After splitting every cell within the AI/ML workspaces119, all lines within the cells are parsed, and for every line of code, sensitive metadata and user methods are found606.

Scraping the objects and methods that are present in AI/ML workspaces119helps to classify if artifacts in the methods are Data-specific methods608or User-created methods610. Data-specific methods are methods which are used to profile the metadata of files in csv, json, parquet, py, pynb or related textual data formats. User-created methods, on the other hand, are used to understand if there is any sensitive data such as credentials or if there is any user cells which has some sensitive PII information. This can enrich prefixes, roots, and suffixes knowledge base for future classifications. Combining these two categories and feeding them into a Multi-Class Support Vector Machine (SVM) classifier612, which may correspond to the ML classification model316, helps to label the cell614and determine the privacy classification616. In one embodiment, the privacy classification is one of internal, restricted, or highly restricted information. The results of the classifier are sent to the queue330.

It is important to note that SVM classifier612does not support multi-classification natively. It supports binary classification and separates input data into two classes. For multiclass classification, a similar principle is applied where the data points are broken down into multiple binary classification problems. Directed Acyclic Graphs (DAG), Binary Tree (BT), One Against One (OAO) and One Against All (OAA) are some of the ways to solve multi-class classification problems for SVM. It is called One-to-Rest approach where the classifier uses “m” SVMs. Each SVM would predict membership in one of the “m” classes. In the One-to-One approach, the classifier uses m*(m−1)/2 SVMs.

In summary, every cell is enriched with metadata as PII information, credentials, and connectors. Ultimately these categorization techniques are used to label every cell present in the notebooks in an automated fashion within this framework, thus consequently determining the privacy category for the data. Based on its labels and previous knowledge, every cell is assigned to a class, thus helping in deciding the masking strategy of the DMC326.

C.2.3 Aspects of an Example DMC

FIG.6illustrates an example embodiment of the DMC326. As shown inFIG.6, the example embodiment of the DMC326receives the privacy classified data324from the queue330. A classification verification module702tests the privacy classified data324to determine if the classification and label provided by the PCS314is correct. If the classification is incorrect, this is stored in privacy knowledge base store704, which may correspond to the privacy knowledge base store318, as a mislabeled classification706. This helps to train the ML classification model316to prevent the mislabeling in the future. In the case where the classification verification module702comprises a reinforcement learning model or where the ML classification model316implements reinforcement learning, the mislabeled classification706may function as a negative reinforcement.

If the classification is correct, this is stored in privacy knowledge base store704as a proper classification708. This helps to train the ML classification model316to continue to properly classify in the future. In the case where the classification verification module702comprises a reinforcement learning model or where the ML classification model316implements reinforcement learning, the proper classification708may function as a positive reinforcement.

The example embodiment of the DMC326includes a data masking module710that performs data masking on the privacy classified data324. As previously described, the data masking modifies any PII, or other sensitive data included in the privacy classified data324in such a way that it is of little or no value to unauthorized users while still being usable by the data scientist302in the building of the ML model.

In one embodiment, the data masking module includes an API711that allows for the customization of the data masking mechanism and algorithm used to perform the data masking. The data masking module710then generates masked data712, which may correspond to the masked data328.

In one example embodiment, the DMC326may comprise the Delphix profiling service. The Delphix profiling service has an inventory of rules that is necessary to profile and identify sensitive data. It provided another validation on top of the SVM classifier and adds metadata rules to the existing Delphix profiler as necessary. As every pipeline stage comes with all labels assigned to its cells as inputs, it would be easier to map if the data attributes should be masked or not.

If any of these cells are mis-classified, the details are captured in the knowledge base so that it is less likely to be misplaced in the future. This negative reinforcement technique can be used to teach specific behaviors pertaining to privacy classifications and building so that false positives and negative parameters can be caught and discarded well in-advance. If the predictions of stages are accurately done, that is fed back to the knowledge base to comprehend the reasons for successfully running the pipeline. This is called a positive reinforcement technique. Every classification stage for the given metadata and credentials are also captured in knowledge base. These stages are run as containers before data scientists start to build and deploy their ML models to production. It is also important to highlight that the framework offers a mechanism to customize the algorithm used for masking via Masking APIs and iterate as necessary to keep them consistent with the privacy policies and rules.

D. Further Discussion

As apparent from this disclosure, example embodiments disclosed herein may possess various useful aspects and features. Some examples of these follow.

For example, an embodiment disclosed herein may implement a PPE framework that improves awareness about GDPR, ISO and PCI compliance process for all data csv, json, parquet, py, pynb or related textual data formats introduced into AI/ML workspaces or any other custom containers that is deployed on top of end-users' cloud native infrastructures.

An embodiment disclosed herein may introduce automation to keep the platform compliant even when new changes are introduced by an Information Governance committee. Guaranteeing end-to-end automation right from the discovery of restricted data in the platform, classifying and masking of sensitive data reinforces security. Chances of attack drop significantly as all applications are profiled via reinforcement techniques so that it becomes much better over time.

An embodiment disclosed herein implements a data classification stage that leverages a multi-class Support Vector Machine Classifier to predict if the metadata and its' corresponding data attributes have sensitive information like PII data or not. With the current automated labeling process, it guarantees security and governance for data practitioners who are usually focused on improving their application code.

A further embodiment disclosed herein implements an event-driven framework so that whenever new data is introduced into the platform, the profiling and masking capabilities kicks off automatically in a proactive fashion and updates the privacy knowledge base on an ad hoc basis.

E. Example Methods

Directing attention now toFIG.8, an example method800is disclosed. The method800will be described in relation to one or more of the figures previously described, although the method800is not limited to any particular embodiment.

The method800includes automatically scanning, at a privacy data collector, received data to determine if the received data is related to an Artificial Intelligence (AI)/Machine Learning (ML) workspace that is used to build an ML model (810). For example, as previously described the PDC310automatically scans the IDE data304received from the IDE of the data scientist302, the public ML data306received from the internet or public repositories, and the private ML data308received from the private repositories. The PDC310then determines if the revised data is related to the AI/ML workspaces119used to build an ML model.

The method800includes for the received data that is determined to be related to the AI/ML workspace, parsing the data, by the privacy data collector, to determine if the data includes any Personal Identifiable Information (PII) or other sensitive information (820). For example, as previously described the PDC310parses the IDE data304, the public ML data306, and the private ML data308to determine if the data includes PII or other sensitive information. In some embodiments, this is done by parsing metadata and comparing the metadata to the common PII metadata list514.

The method800includes for the data that includes PII data or other sensitive data, generating, by a ML classification model, a privacy classification for the data (830). For example, as previously described the ML classification model316of the PCS314provides a privacy classification for the data to generate the privacy classified data324.

The method800includes for the data having the privacy classification, performing, by a data masking component, a data masking operation on the PII data or other sensitive data to thereby generate masked data (840). For example, as previously described the DMC326performs a data masking operation on the privacy classified data324to generate the masked data328. The masked data328can then be used in the AI/ML workspaces119to build the ML model.

F. Further Example Embodiments

Embodiment 1. A method, comprising: automatically scanning, at a privacy data collector, received data to determine if the received data is related to an Artificial Intelligence (AI)/Machine Learning (ML) workspace that is used to build an ML model; for the received data that is determined to be related to the AI/ML workspace, parsing the data, by the privacy data collector, to determine if the data includes any Personal Identifiable Information (PII) or other sensitive information; for the data that includes PII data or other sensitive data, generating, by a ML classification model, a privacy classification for the data; and for the data having the privacy classification, performing, by a data masking component, a data masking operation on the PII data or other sensitive data to thereby generate masked data.

Embodiment 2. The method as recited in any preceding embodiment, further comprising: providing the masked data to the AI/MML workspace to be used in building the ML model.

Embodiment 3. The method as recited in any preceding embodiment, wherein the ML classification model is a multi-class Support Vector Machine (SVM).

Embodiment 4. The method as recited in any preceding embodiment, wherein determining if the data includes any Personal Identifiable Information (PII) or other sensitive information comprises: scanning metadata of the received data that is determined to be related to the AI/ML workspace to determine PII data or other sensitive data included in the metadata; and comparing the metadata of the received data with a continuously updated common PII metadata list that is associated with the privacy data collector.

Embodiment 5. The method as recited in any preceding embodiment, wherein the PII data includes one or more of a name, account number information, address information, vehicle information, birthdate, medical and health information, email address, mobile phone number, phone and fax number, website information, geographic information, photographs with full face features, social security information, and biometric identifiers.

Embodiment 6. The method as recited in any preceding embodiment, wherein the ML classification model is trained using one or more of privacy rules and historical classification data.

Embodiment 7. The method as recited in any preceding embodiment, wherein the ML classification model uses one or more privacy rules when generating the privacy classification for the data.

Embodiment 8. The method as recited in any preceding embodiment, wherein one or more privacy rules comprise one or more of European General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), Payment Card Industry Data Security Standard (PCI), or International Organization for Standardization Data Security Standard (ISO).

Embodiment 9. The method as recited in any preceding embodiment, wherein the received data is received from one or more of a public repository, a private repository, or an intelligent development environment (IDE) that are associated with the AI/ML workspace.

Embodiment 10. The method as recited in any preceding embodiment, further comprising: verifying, by the data masking component, that the generated privacy classification for the data is correct.

Embodiment 11. A system, comprising hardware and/or software, operable to perform any of the operations, methods, or processes, or any portion of any of these, disclosed herein.

G. Example Computing Devices and Associated Media

As used herein, the term ‘module’ or ‘component’ may refer to software objects or routines that are executed on the computing system. The different components, modules, engines, and services described herein may be implemented as objects or processes that execute on the computing system, for example, as separate threads. While the system and methods described herein may be implemented in software, implementations in hardware or a combination of software and hardware are also possible and contemplated. In the present disclosure, a ‘computing entity’ may be any computing system as previously defined herein, or any module or combination of modules running on a computing system.

In the example ofFIG.9, the physical computing device900includes a memory902which may include one, some, or all, of random access memory (RAM), non-volatile memory (NVM)904such as NVRAM for example, read-only memory (ROM), and persistent memory, one or more hardware processors906, non-transitory storage media908, UI device910, and data storage912. One or more of the memory components902of the physical computing device900may take the form of solid state device (SSD) storage. As well, one or more applications914may be provided that comprise instructions executable by one or more hardware processors906to perform any of the operations, or portions thereof, disclosed herein.