Description:DESCRIPTION
Technical Field
[001] This disclosure relates generally to backup management and more particularly to a method and system for managing backup of sensitive documents.
Background
[002] Modern backup applications may offer diverse methods for safeguarding organizational data. Nonetheless, despite the regularity of these backups, there remains a considerable risk that sensitive data may become inaccessible in the event of system failures. Therefore, there is a critical need for an advanced mechanism which may ensure the sensitive data is highly available during system failure.
[003] The present invention is directed to overcome one or more limitations stated above or any other limitations associated with the known arts.
SUMMARY
[004] In one embodiment, a method for managing backup of sensitive documents is disclosed. In one example, the method may include monitoring, using an Artificial Intelligence (AI) model, a plurality of documents stored in a backup-enabled data source for data modification. The method may further include identifying, using the AI model, at least one data modification corresponding to one or more modified documents of the plurality of documents based on the monitoring. It should be noted that each of the at least one data modification corresponds to one of a new document addition to the plurality of documents, a document deletion from one of the plurality of documents, or a modification to data of one of the plurality of documents. The method may further include retrieving the one or more modified documents from the backup-enabled data source. The data of each of the plurality of documents may include content data and metadata. The method may further include extracting the metadata from each of the one or more modified documents using a parsing technique. It should be noted that the metadata may include a sensitivity label. For each of the one or more modified documents, the method may further include determining, using the AI model, a sensitivity classification from a set of sensitivity classifications based on the sensitivity label associated with the document. It should be noted that each of the set of sensitivity classifications corresponds to a predefined backup priority level. The method may further include triggering, using the AI model, a backup application to perform a backup of the document when the predefined backup priority level corresponding to the sensitivity classification of the document is above a predefined threshold priority level.
[005] In another embodiment, a system for managing backup of sensitive documents is disclosed. In one example, the system may include a processor and a computer-readable medium communicatively coupled to the processor. The computer-readable medium may store processor-executable instructions, which, on execution, may cause the processor to monitor, using an AI model, a plurality of documents stored in a backup-enabled data source for data modification. The processor-executable instructions, on execution, may further cause the processor to identify, using the AI model, at least one data modification corresponding to one or more modified documents of the plurality of documents based on the monitoring. It should be noted that each of the at least one data modification corresponds to one of a new document addition to the plurality of documents, a document deletion from one of the plurality of documents, or a modification to data of one of the plurality of documents. The processor-executable instructions, on execution, may further cause the processor to retrieve the one or more modified documents from the backup-enabled data source. The data of each of the plurality of documents may include content data and metadata. The processor-executable instructions, on execution, may further cause the processor to extract the metadata from each of the one or more modified documents using a parsing technique. It should be noted that the metadata may include a sensitivity label. For each of the one or more modified documents, the processor-executable instructions, on execution, may further cause the processor to determine, using the AI model, a sensitivity classification from a set of sensitivity classifications based on the sensitivity label associated with the document. It should be noted that each of the set of sensitivity classifications corresponds to a predefined backup priority level. The processor-executable instructions, on execution, may further cause the processor to trigger, using the AI model, a backup application to perform a backup of the document when the predefined backup priority level corresponding to the sensitivity classification of the document is above a predefined threshold priority level.
[006] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
BRIEF DESCRIPTION OF THE DRAWINGS
[007] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles.
[008] FIG. 1 is a block diagram of an exemplary system for managing backup of sensitive documents, in accordance with some embodiments of the present disclosure.
[009] FIG. 2 illustrates a functional block diagram of a system for managing backup of sensitive documents, in accordance with some embodiments of the present disclosure.
[010] FIG. 3 illustrates a flow diagram of an exemplary process for managing backup of sensitive documents, in accordance with some embodiments of the present disclosure.
[011] FIG. 4 illustrates a flow diagram of an exemplary process for performing backup of current version of stored documents, in accordance with some embodiments of the present disclosure.
[012] FIG. 5 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
DETAILED DESCRIPTION
[013] Exemplary embodiments are described with reference to the accompanying drawings. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.
[014] Referring now to FIG. 1, an exemplary system 100 for managing backup of sensitive documents is illustrated, in accordance with some embodiments of the present disclosure. The system 100 may include a computing device 102. The computing device 102 may be, for example, but may not be limited to, server, desktop, laptop, notebook, netbook, tablet, smartphone, mobile phone, or any other computing device, in accordance with some embodiments of the present disclosure. The computing device 102 may retrieve one or more modified documents from the backup-enabled data storage based on identified data changes using an Artificial Intelligence (AI) model. Further, the computing device 102 may identify whether the one or more modified documents are confidential documents or not based on a sensitivity label. Upon identifying the confidential documents, the computing device 102 may trigger a backup application to perform backup of the one or more modified documents.
[015] As will be described in greater detail in conjunction with FIGS. 2 – 4, the computing device 102 may monitor, using an AI model, a plurality of documents stored in a backup-enabled data source for data modification. The computing device 102 may further identify, using the AI model, at least one data modification corresponding to one or more modified documents of the plurality of documents based on the monitoring. It should be noted that each of the at least one data modification corresponds to one of a new document addition to the plurality of documents, a document deletion from one of the plurality of documents, or a modification to data of one of the plurality of documents. The computing device 102 may further retrieve the one or more modified documents from the backup-enabled data source. The data of each of the plurality of documents may include content data and metadata. The computing device 102 may further extract the metadata from each of the one or more modified documents using a parsing technique. The metadata may include a sensitivity label. For each of the one or more modified documents, the computing device 102 may further determine, using the AI model, a sensitivity classification from a set of sensitivity classifications based on the sensitivity label associated with the document. Each of the set of sensitivity classifications corresponds to a predefined backup priority level. The computing device 102 may further trigger, using the AI model, a backup application to perform a backup of the document when the predefined backup priority level corresponding to the sensitivity classification of the document is above a predefined threshold priority level.
[016] In some embodiments, the computing device 102 may include one or more processors 104 and a memory 106. Further, the memory 106 may store instructions that, when executed by the one or more processors 104, may cause the one or more processors 104 to manage backup of sensitive documents, in accordance with aspects of the present disclosure. The memory 106 may also store various data (for example, a plurality of documents, one or more modified documents, backed-up one or more modified documents, a set of sensitivity classifications, predefined backup criteria, and the like) that may be captured, processed, and/or required by the system 100.
[017] The system 100 may further include a display 108. The system 100 may interact with a user interface 110 accessible via the display 108. The system 100 may also include one or more external devices 112. In some embodiments, the computing device 102 may interact with the one or more external devices 112 over a communication network 114 for sending or receiving various data. The communication network 114 may include, for example, but may not be limited to, a wireless fidelity (Wi-Fi) network, a light fidelity (Li-Fi) network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. The one or more external devices 112 may include, but may not be limited to, a remote server, a laptop, a netbook, a notebook, a smartphone, a mobile phone, a tablet, or any other computing device.
[018] Referring now to FIG. 2, a functional block diagram of a system 200 for managing backup of sensitive documents is illustrated, in accordance with some embodiments of the present disclosure. FIG. 2 is explained in conjunction with FIG. 1. The system 200 may be analogous to the system 100. The system 200 may implement the computing device 102. The system 200 may include, within the memory 106, a monitoring module 202, an identifying module 204, a validating module 206, a retrieving module 208, a metadata extracting module 210, a determining module 212, a backup module 214, an Artificial Intelligence (AI) model 216, and a backup storage database 218. The AI model 216, for example, may be, but may not be limited to, Natural Language Processing (NLP), Machine Learning (ML), Large Language Model (LLM) (e.g., Generative Pre-trained Transformer (GPT)-3, GPT-3.5, GPT-4, etc.), or the like.
[019] Initially, the monitoring module 202 may monitor, using the AI model 216, a plurality of documents stored in a backup-enabled data source for data modification. The plurality of documents may include any documents which contain information related to any domain. The domain, for example, may be, but may not be limited to, healthcare domain, entertainment domain, finance domain, e-commerce domain, educational domain, or the like. The plurality of documents, for example, may be, but may not be limited to, text documents (e.g., a Portable Document Format (PDF), a word document (DOC, or DOCX), HTML, or the like), images (e.g., Joint Photographic Expert Group (JPEG), Portable Network Graphics (PNG), etc.), videos, emails, spreadsheets (e.g., excel files, Comma Separated Values (CSV) files, etc.), or the like. The backup-enabled data storage, for example, may be, but may not be limited to, One Drive, Google® Drive, Dropbox, or the like.
[020] Further, the identifying module 204 may identify, using the AI model 216, at least one data modification corresponding to one or more modified documents of the plurality of documents based on the monitoring. It should be noted that each of the at least one modification corresponds to one of a new document addition to the plurality of documents, a document deletion from one of the plurality of documents, or a modification to data of one of the plurality of documents.
[021] Upon identifying the one or more modified documents, the validating module 206 may validate predefined backup criteria for each of the one or more modified documents. The predefined backup criteria are based on at least one of a predefined threshold modification in content data of a document (for example, changes above 100 characters, 150 characters, or the like) or a change in a modification timestamp of the document (for example, save operation (i.e., date and time when the document was last modified (or edited)).
[022] Upon successful validation of the predefined backup criteria, the retrieving module 208 may retrieve the one or more modified documents from the backup-enabled data source. It should be noted that the data of each of the plurality of documents may include the content data and metadata. Further, the retrieving module 208 may send the one or more modified documents to the metadata extracting module 210. Further, the metadata extracting module 210 may extract, using the AI model 216, the metadata from each of the one or more modified documents using a parsing technique. The metadata may include a sensitivity label. The sensitivity label may be a designation applied to the documents to indicate its level of sensitivity or required protection. The sensitivity label may enforce the protection settings (e.g., encryption, or the like) which may restrict access to the content data. The sensitivity label, for example, may include, but may not be limited to, visual marking (such as, header, footer, watermark, logo, digital signature, or the like), encryption, or the like.
[023] Further, for each of the one or more modified documents, the determining module 212 may determine, using the AI model 216, a sensitivity classification from a set of sensitivity classifications based on the sensitivity label associated with the document. Each of the set of sensitivity classifications corresponds to a predefined backup priority level. The set of sensitivity classifications, for example, may include, but may not be limited to, highly confidential, confidential, public, internal, or the like. By way of an example, the ‘highly confidential’ may correspond to ‘high priority level’. Similarly, the ‘confidential (or internal)’ may correspond to ‘medium priority level’, and the ‘public’ may correspond to ‘low priority level’. By way of an example, the AI model 216 may perform on demand backup for the highly confidential, confidential, and internal classified documents. On the other hand, the AI model 216 may perform backup for the public classified documents after completing the backup for all the above mentioned classified documents.
[024] Further, for each of the one or more modified documents, the backup module 214 may trigger, using the AI model 216, a backup application to perform a backup of the document when the predefined backup priority level corresponding to the sensitivity classification of the document is above a predefined threshold priority. The backup application, for example, may be, but may not be limited to, Backblaze, IDrive, Google® Cloud, Microsoft® Azure, Acronis, or the like. Further, the backup module 214 may perform the backup of each of the one or more modified documents using the backup application. Further, the backup module 214 may store each of the one or more modified documents in a backup storage database 218.
[025] Once the one or more modified documents are stored in the backup storage database 218, the monitoring module 202 may monitor in real time, each of the stored one or more modified documents for the data modification using the AI model 216. Further, the identifying module 204 may identify, using the AI model 216, the modification to the data of a stored document of the stored one or more modified documents based on a comparison of a stored version of the stored document with a current version of the stored document. Further, the validating module 206 may validate the sensitivity classification of the current version of the stored document based on the predefined threshold level. Upon successful validation, the backup module 214 may trigger the backup application to perform the backup of the current version of the stored document to the backup storage database 218.
[026] It should be noted that all such aforementioned modules 202 – 218 may be represented as a single module or a combination of different modules. Further, as will be appreciated by those skilled in the art, each of the modules 202 – 218 may reside, in whole or in parts, on one device or multiple devices in communication with each other. In some embodiments, each of the modules 202 – 218 may be implemented as dedicated hardware circuit comprising custom application-specific integrated circuit (ASIC) or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. Each of the modules 202 – 218 may also be implemented in a programmable hardware device such as a field programmable gate array (FPGA), programmable array logic, programmable logic device, and so forth. Alternatively, each of the modules 202 – 218 may be implemented in software for execution by various types of processors (e.g., processor 104). An identified module of executable code may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, function, or other construct. Nevertheless, the executables of an identified module or component need not be physically located together but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose of the module. Indeed, a module of executable code could be a single instruction, or many instructions, and may even be distributed over several different code segments, among different applications, and across several memory devices.
[027] As will be appreciated by one skilled in the art, a variety of processes may be employed for managing backup of sensitive documents. For example, the exemplary system 100 and the associated computing device 102, may manage backup of sensitive documents, by the processes discussed herein. In particular, as will be appreciated by those of ordinary skill in the art, control logic and/or automated routines for performing the techniques and steps described herein may be implemented by the system 100 and the associated computing device 102 either by hardware, software, or combinations of hardware and software. For example, suitable code may be accessed and executed by the one or more processors on the system 100 to perform some or all of the techniques described herein. Similarly, application specific integrated circuits (ASICs) configured to perform some or all of the processes described herein may be included in the one or more processors on the system 100.
[028] Referring now to FIG. 3, an exemplary process 300 for managing backup of sensitive documents is illustrated via a flow chart, in accordance with some embodiments of the present disclosure. The process 300 may be implemented by the computing device 102 of the system 100. In some embodiments, the process 300 may include monitoring, by a monitoring module (such as the monitoring module 202) using an AI model (such as the AI model 216), a plurality of documents stored in a backup-enabled data source for data modification, at step 302. Further, the process 300 may include identifying, by an identifying module (such as the identifying module 204) using the AI model, at least one data modification corresponding to one or more modified documents of the plurality of documents based on the monitoring, at step 304. It should be noted that each of the at least one data modification corresponds to one of a new document addition to the plurality of documents, a document deletion from one of the plurality of documents, or a modification to data of one of the plurality of documents.
[029] Upon identifying the one or more modified documents, the process 300 may include validating, by a validating module (such as the validating module 206) predefined backup criteria for each of the one or more modified documents, at step 306. The predefined backup criteria are based on at least one of a predefined threshold modification in content data of a document or a change in a modification timestamp of the document. Upon successful validation of the predefined backup criteria, the process 300 may include retrieving, by a retrieving module (such as the retrieving module 208), the one or more modified documents from the backup-enabled data source, at step 308. The data of each of the plurality of documents may include the content data and metadata.
[030] Upon retrieving the one or more modified documents, the process 300 may include extracting, by a metadata extracting module (such as the metadata extracting module 210), the metadata from each of the one or more modified documents using a parsing technique, at step 310. It should be noted that the metadata may include a sensitivity label.
[031] Further, for each of the one or more modified documents, the process 300 may include determining, by a determining module (such as the determining module 212) using the AI model, a sensitivity classification from a set of sensitivity classifications based on the sensitivity label associated with the document, at step 312. Each of the set of sensitivity classifications corresponds to a predefined backup priority level. Further, the process 300 may include triggering, by a backup module (such as the backup module 214) using the AI model, a backup application to perform a backup of the document when the predefined backup priority level corresponding to the sensitivity classification of the document is above a predefined threshold priority level, at step 314.
[032] Further, the process 300 may include performing, by the backup module, the backup of each of the one or more modified documents using the backup application, at step 316. Further, the process 300 may include storing, by the backup module, each of the one or more modified documents in a backup storage database (such as the backup storage database 218), at step 318.
[033] By way of an example, consider a scenario where an AI agent (such as the AI model 216) may manage a backup of sensitive documents. In the current scenario, two documents (e.g., a first document and a second document) may be priorly stored in a drive (i.e., backup-enabled data source) of a computing device (such as the computing device 102). Further, a new document (e.g., a third document) may be stored (or added) in the drive. The new document may include confidential data (or sensitive data) along with a sensitivity label (e.g., watermark (i.e., highly confidential)). Simultaneously, the AI agent may continuously monitor the drive for the data changes. While monitoring, the AI agent may find (or identify) the new document (i.e., the third document) may be added in the drive. In some embodiments, the AI agent may find data changes (such as data modification in the first document) in the drive.
[034] Further, before initiating the backup of the new document, the AI agent may validate the new document based on the timestamp (i.e., the predefined backup criteria) of the new document. Upon successful validation, the AI agent may retrieve the new document from the drive. Further, the AI agent may parse the content of the new document to identify whether the new document is confidential or not based on the sensitivity label (i.e., the watermark) using a parsing technique. Upon identifying the new document is the highly confidential, the AI agent may extract the confidential data (or content) from the new document. Further, the AI agent may determine a priority level (e.g., high priority level) of the new document based on the sensitivity label.
[035] Further, the AI agent may trigger a backup application (e.g., a Backblaze) to perform a backup of the new document. In some embodiments, prior to triggering the backup application, the AI agent may send a request to a user to provide confirmation corresponding to the backup via a user interface. Upon receiving the confirmation from the user, the AI agent may trigger the backup application to perform the backup of the new document. Further, the AI agent may store the backed-up new document in a secondary storage database (i.e., the backup storage database 218) for future reference. Additionally, the AI agent may notify the user about the back up of the new document in the secondary storage database via the user interface.
[036] Referring now to FIG. 4, an exemplary process 400 for performing backup of current version of stored documents is illustrated via a flow chart, in accordance with some embodiments of the present disclosure. FIG. 4 is explained in conjunction with FIG. 3. In some embodiments, the process 400 may include monitoring in real time, by the monitoring module, each of the stored one or more modified documents for the data modification using the AI model, at step 402. Further, the process 400 may include identifying, by the identifying module using the AI model, the modification to the data of a stored document of the stored one or more modified documents based on a comparison of a stored version of the stored document with a current version of the stored document, at step 404.
[037] Further, the process 400 may include validating, by the validating module, the sensitivity classification of the current version of the stored document based on the predefined threshold priority level, at step 406. Upon successful validation, the process 400 may include triggering, by the backup module, the backup application to perform the backup of the current version of the stored document to the backup storage database, at step 408.
[038] In continuation with the above example, the AI agent may monitor in real time each of the three stored documents (i.e., the first stored document, the second stored document, and the third stored document) in the drive for the data changes. While monitoring, the AI agent may find data changes (e.g., addition of 150 new characters) in the current version of the first stored document based on a comparison of the stored version of the first stored document with the current version of the first stored document. Upon finding the data changes, the AI agent may validate the current version of the first stored document based on predefined priority level. Upon successful validation, the AI agent may trigger the backup application (i.e., Backblaze) to perform backup of the current version of the first stored document to the secondary storage database.
[039] As will be also appreciated, the above-described techniques may take the form of computer or controller implemented processes and apparatuses for practicing those processes. The disclosure can also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, solid state drives, CD-ROMs, hard drives, or any other computer-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer or controller, the computer becomes an apparatus for practicing the invention. The disclosure may also be embodied in the form of computer program code or signal, for example, whether stored in a storage medium, loaded into and/or executed by a computer or controller, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits.
[040] The disclosed methods and systems may be implemented on a conventional or a general-purpose computer system, such as a personal computer (PC) or server computer. Referring now to FIG. 5, an exemplary computing system 500 that may be employed to implement processing functionality for various embodiments (e.g., as a SIMD device, client device, server device, one or more processors, or the like) is illustrated. Those skilled in the relevant art will also recognize how to implement the invention using other computer systems or architectures. The computing system 500 may represent, for example, a user device such as a desktop, a laptop, a mobile phone, personal entertainment device, DVR, and so on, or any other type of special or general-purpose computing device as may be desirable or appropriate for a given application or environment. The computing system 500 may include one or more processors, such as a processor 502 that may be implemented using a general or special purpose processing engine such as, for example, a microprocessor, microcontroller or other control logic. In this example, the processor 502 is connected to a bus 504 or other communication medium. In some embodiments, the processor 502 may be an Artificial Intelligence (AI) processor, which may be implemented as a Tensor Processing Unit (TPU), or a graphical processor unit, or a custom programmable solution Field-Programmable Gate Array (FPGA).
[041] The computing system 500 may also include a memory 506 (main memory), for example, Random Access Memory (RAM) or other dynamic memory, for storing information and instructions to be executed by the processor 502. The memory 506 also may be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 502. The computing system 500 may likewise include a read only memory (“ROM”) or other static storage device coupled to bus 504 for storing static information and instructions for the processor 502.
[042] The computing system 500 may also include a storage devices 508, which may include, for example, a media drive 510 and a removable storage interface. The media drive 510 may include a drive or other mechanism to support fixed or removable storage media, such as a hard disk drive, a floppy disk drive, a magnetic tape drive, an SD card port, a USB port, a micro USB, an optical disk drive, a CD or DVD drive (R or RW), or other removable or fixed media drive. A storage media 512 may include, for example, a hard disk, magnetic tape, flash drive, or other fixed or removable medium that is read by and written to by the media drive 510. As these examples illustrate, the storage media 512 may include a computer-readable storage medium having stored therein particular computer software or data.
[043] In alternative embodiments, the storage devices 508 may include other similar instrumentalities for allowing computer programs or other instructions or data to be loaded into the computing system 500. Such instrumentalities may include, for example, a removable storage unit 514 and a storage unit interface 516, such as a program cartridge and cartridge interface, a removable memory (for example, a flash memory or other removable memory module) and memory slot, and other removable storage units and interfaces that allow software and data to be transferred from the removable storage unit 514 to the computing system 500.
[044] The computing system 500 may also include a communications interface 518. The communications interface 518 may be used to allow software and data to be transferred between the computing system 500 and external devices. Examples of the communications interface 518 may include a network interface (such as an Ethernet or other NIC card), a communications port (such as for example, a USB port, a micro USB port), Near field Communication (NFC), etc. Software and data transferred via the communications interface 518 are in the form of signals which may be electronic, electromagnetic, optical, or other signals capable of being received by the communications interface 518. These signals are provided to the communications interface 518 via a channel 520. The channel 520 may carry signals and may be implemented using a wireless medium, wire or cable, fiber optics, or other communications medium. Some examples of the channel 520 may include a phone line, a cellular phone link, an RF link, a Bluetooth link, a network interface, a local or wide area network, and other communications channels.
[045] The computing system 500 may further include Input/Output (I/O) devices 522. Examples may include, but are not limited to a display, keypad, microphone, audio speakers, vibrating motor, LED lights, etc. The I/O devices 522 may receive input from a user and also display an output of the computation performed by the processor 502. In this document, the terms “computer program product” and “computer-readable medium” may be used generally to refer to media such as, for example, the memory 506, the storage devices 508, the removable storage unit 514, or signal(s) on the channel 520. These and other forms of computer-readable media may be involved in providing one or more sequences of one or more instructions to the processor 502 for execution. Such instructions, generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), when executed, enable the computing system 500 to perform features or functions of embodiments of the present invention.
[046] In an embodiment where the elements are implemented using software, the software may be stored in a computer-readable medium and loaded into the computing system 500 using, for example, the removable storage unit 514, the media drive 510 or the communications interface 518. The control logic (in this example, software instructions or computer program code), when executed by the processor 502, causes the processor 502 to perform the functions of the invention as described herein.
[047] Various embodiments provide method and system for managing backup of sensitive documents. The disclosed method and system may monitor, using an AI model, a plurality of documents stored in a backup-enabled data source for data modification. Further, the disclosed method and system may identify, using the AI model, at least one data modification corresponding to one or more modified documents of the plurality of documents based on the monitoring. Each of the at least one data modification corresponds to one of a new document addition to the plurality of documents, a document deletion from one of the plurality of documents, or a modification to data of one of the plurality of documents. Further, the disclosed method and system may retrieve the one or more modified documents from the backup-enabled data source. The data of each of the plurality of documents may include content data and metadata. Further, the disclosed method and system may extract the metadata from each of the one or more modified documents using a parsing technique. The metadata may include a sensitivity label. Moreover, for each of the one or more modified documents, the disclosed method and system may determine, using the AI model, a sensitivity classification from a set of sensitivity classifications based on the sensitivity label associated with the document. Each of the set of sensitivity classifications corresponds to a predefined backup priority level. Thereafter, for each of the one or more modified documents, the disclosed method and system may trigger, using the AI model, a backup application to perform a backup of the document when the predefined backup priority level corresponding to the sensitivity classification of the document is above a predefined threshold priority level.
[048] Thus, the disclosed method and system try to overcome the technical problem of managing backup of sensitive documents. The disclosed method and system may perform backup of one or more modified documents based on a sensitivity level and sensitivity classification using an AI model. The disclosed method and system may not follow any backup policies for the backup of the one or more modified documents. The disclosed method and system may store the one or more modified documents in a backup storage database which may reduce the risk of losing sensitive data in the event of system failure. The disclosed method and system may also perform backup of one or more stored modified documents based on data changes in real time. The disclosed method and system may be efficient and effective.
[049] In light of the above mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
[050] It will be appreciated that, for clarity purposes, the above description has described embodiments of the invention with reference to different functional units and processors. However, it will be apparent that any suitable distribution of functionality between different functional units, processors or domains may be used without detracting from the invention. For example, functionality illustrated to be performed by separate processors or controllers may be performed by the same processor or controller. Hence, references to specific functional units are only to be seen as references to suitable means for providing the described functionality, rather than indicative of a strict logical or physical structure or organization.
[051] Although the present invention has been described in connection with some embodiments, it is not intended to be limited to the specific form set forth herein. Rather, the scope of the present invention is limited only by the claims. Additionally, although a feature may appear to be described in connection with particular embodiments, one skilled in the art would recognize that various features of the described embodiments may be combined in accordance with the invention.
[052] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[053] It is intended that the disclosure and examples be considered as exemplary only. , Claims:CLAIMS
I/We Claim:
1. A method (300) for managing backup of sensitive documents, the method (300) comprising:
monitoring (302), by a computing device (102) using an Artificial Intelligence (AI) model (216), a plurality of documents stored in a backup-enabled data source for data modification;
identifying (304), by the computing device (102) using the AI model (216), at least one data modification corresponding to one or more modified documents of the plurality of documents based on the monitoring, wherein each of the at least one data modification corresponds to one of a new document addition to the plurality of documents, a document deletion from one of the plurality of documents, or a modification to data of one of the plurality of documents;
retrieving (308), by the computing device (102), the one or more modified documents from the backup-enabled data source, wherein the data of each of the plurality of documents comprises content data and metadata;
extracting (310), by the computing device (102), the metadata from each of the one or more modified documents using a parsing technique, wherein the metadata comprise a sensitivity label; and
for each of the one or more modified documents,
determining (312), by the computing device (102) using the AI model (216), a sensitivity classification from a set of sensitivity classifications based on the sensitivity label associated with the document, wherein each of the set of sensitivity classifications corresponds to a predefined backup priority level; and
triggering (314), by the computing device (102) using the AI model (216), a backup application to perform a backup of the document when the predefined backup priority level corresponding to the sensitivity classification of the document is above a predefined threshold priority level.

2. The method (300) as claimed in claim 1, comprising performing (316) the backup of each of the one or more modified documents using the backup application, wherein performing the backup comprises storing each of the one or more modified documents in a backup storage database.

3. The method (300) as claimed in claim 2, comprising:
monitoring (402) in real time, each of the stored one or more modified documents for the data modification using the AI model (216);
identifying (404), using the AI model (216), the modification to the data of a stored document of the stored one or more modified documents based on a comparison of a stored version of the stored document with a current version of the stored document;
validating (406) the sensitivity classification of the current version of the stored document based on the predefined threshold priority level; and
triggering (408) the backup application to perform the backup of the current version of the stored document to the backup storage database upon successful validation.

4. The method (300) as claimed in claim 1, comprising:
prior to retrieving the one or more modified documents, validating (306) predefined backup criteria for each of the one or more modified documents; and
retrieving the one or more modified documents upon successful validation of the predefined backup criteria.

5. The method (300) as claimed in claim 4, wherein the predefined backup criteria are based on at least one of a predefined threshold modification in the content data of a document or a change in a modification timestamp of the document.

6. A system (100) for managing backup of sensitive documents, the system (100) comprising:
a processor (104); and
a memory (106) communicatively coupled to the processor (104), wherein the memory (106) stores processor executable instructions, which, on execution, causes the processor (104) to:
monitor (302), using an Artificial Intelligence (AI) model (216), a plurality of documents stored in a backup-enabled data source for data modification;
identify (304), using the AI model (216), at least one data modification corresponding to one or more modified documents of the plurality of documents based on the monitoring, wherein each of the at least one data modification corresponds to one of a new document addition to the plurality of documents, a document deletion from one of the plurality of documents, or a modification to data of one of the plurality of documents;
retrieve (308) the one or more modified documents from the backup-enabled data source, wherein the data of each of the plurality of documents comprises content data and metadata;
extract (310) the metadata from each of the one or more modified documents using a parsing technique, wherein the metadata comprise a sensitivity label; and
for each of the one or more modified documents,
determine (312), using the AI model (216), a sensitivity classification from a set of sensitivity classifications based on the sensitivity label associated with the document, wherein each of the set of sensitivity classifications corresponds to a predefined backup priority level; and
trigger (314), using the AI model (216), a backup application to perform a backup of the document when the predefined backup priority level corresponding to the sensitivity classification of the document is above a predefined threshold priority level.

7. The system (100) as claimed in claim 6, wherein the processor executable instructions cause the processor (104) to perform (316) the backup of each of the one or more modified documents using the backup application, wherein performing the backup comprises storing each of the one or more modified documents in a backup storage database.

8. The system (100) as claimed in claim 7, wherein the processor executable instructions cause the processor (104) to:
monitor (402) in real time, each of the stored one or more modified documents for the data modification using the AI model (216);
identify (404), using the AI model (216), the modification to the data of a stored document of the stored one or more modified documents based on a comparison of a stored version of the stored document with a current version of the stored document;
validate (406) the sensitivity classification of the current version of the stored document based on the predefined threshold priority level; and
trigger (408) the backup application to perform the backup of the current version of the stored document to the backup storage database upon successful validation.

9. The system (100) as claimed in claim 6, wherein the processor executable instructions cause the processor (104) to:
prior to retrieving the one or more modified documents, validate (306) predefined backup criteria for each of the one or more modified documents; and
retrieve the one or more modified documents upon successful validation of the predefined backup criteria.

10. The system (100) as claimed in claim 9, wherein the predefined backup criteria are based on at least one of a predefined threshold modification in the content data of a document or a change in a modification timestamp of the document.