[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122726-en":3,"doc-seo-122726-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122726,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",6,"Technology","MACHINE LEARNING PLATFORM TO AUTOMATICALLY PERFORM PATCHING OF SERVERS - Technical Disclosure Commons - Defensive Publications Series","A technical disclosure focuses on cybersecurity and infrastructure management by presenting a machine learning platform that automatically performs server patching at internet scale. It targets the limitations of conventional manual patching, including low predictability, significant human error, fragmented workflows across administrators, and lack of centralized logging for failure analysis. The disclosure describes conventional patching life cycle steps such as vulnerability extraction and allocation, server selection, runbook-guided execution, completion tracking, and the operational handling required before and after patching.","Technical Disclosure Commons  \nDefensive Publications Series  \nJune 2023  \nMACHINE LEARNING PLATFORM TO AUTOMATICALLY PERFORM PATCHING OF SERVERS  \nHEMANTH THOTA VISA  \nSAURABH CHANDRA VISA  \nRISHU MEHROTRA VISA  \nVINAY KEERTHI VISA  \nFollow this and additional works at: [https://www.tdcommons.org/dpubs_series](https://www.tdcommons.org/dpubs_series)  \nRecommended Citation  \nTHOTA, HEMANTH; CHANDRA, SAURABH; MEHROTRA, RISHU; and KEERTHI, VINAY, \"MACHINE LEARNING PLATFORM TO AUTOMATICALLY PERFORM PATCHING OF SERVERS\", Technical Disclosure Commons,(June 05, 2023)  \n[https://www.tdcommons.org/dpubs_series/5938](https://www.tdcommons.org/dpubs_series/5938)  \nThis work is licensed under a Creative Commons Attribution 4.0 License.  \nThis Article is brought to you for free and open access by Technical Disclosure Commons. It has been accepted for inclusion in Defensive Publications Series by an authorized administrator of Technical Disclosure Commons.  \nTITLE: MACHINE LEARNING PLATFORM TO  \nAUTOMATICALLY PERFORM  \nPATCHING OF SERVERS  \nVISA  \nHEMANTH THOTA  \nSAURABH CHANDRA  \nRISHU MEHROTRA  \nVINAY KEERTHI  \nPublished by Technical Disclosure Commons, 2023 2  \nTECHNICAL FIELD  \nThe present disclosure generally relates to the field of cybersecurity and infrastructure management. More particularly, the present disclosure relates to machine learning platform to  \nautomatically perform patching of servers at internet scale.  \nBACKGROUND  \nServer patching is a process that updates a server’s software to fix errors, update versions, improve features and performances of the server, and the like. The fixing of errors involves handling security vulnerabilities and removing bugs that poses a risk to data and information of an organization. Server patching is a complex process that needs to be performed quickly  \nand accurately to minimize risks and maximize security.  \nConventionally, server administrators coordinate patching of the servers. The process of patching is manual and process heavy. The process of patching is spread across different machines and the administrators. This causes multiple leakages in quality and reliability of patching. As the process of patching is manual, predictability index in identifying and performing the patching is low (for example, ~70%) . Also, since the process of patching is manual, there are errors involved in the process of patching (for example, a human error rate of ~20% may be associated with the process of patching) . Further, there is a lack of a centralized log aggregation mechanism to analyze and understand failures and root causes. This  \ncauses long Mean Time To Repair (MTTRs) or failure resolution times of the servers.  \n[Figures 1A-1F] illustrate conventional patching process. The numbers presented in screenshots are illustrative only. They do not, in any form or shape, reflect the actual situation.  \nThe conventional patching process is extremely high-volume and manual.  \nFIG. 1A illustrates an overview of patching life cycle. Firstly, vulnerabilities in the servers are extracted and allocated to security team members. Then, the security team members select the servers to patch. The security team members perform the patching by following runbooks. Arunbook is a detailed guide for completing a commonly repeated task or procedure within an operation process of an organization. FIG. 1B illustrates data collection and work allocation process in the patching life cycle. In the work allocation process, a security lead may manually fetch the vulnerabilities (also addressed to as findings) from a data collection system. In the present description, Visa Vulnerability Management System (VVMS) portal is considered asthe data collection system. A person skilled in the art will appreciate that the data collection  \n[https://www.tdcommons.org/dpubs_series/5938](https://www.tdcommons.org/dpubs_series/5938) 3  \nsystem can comprise other known systems for collection of the vulnerabilities. FIG. 1C illustrates co","cbCaiiGxIariIK6C","https://ap.wps.com/l/cbCaiiGxIariIK6C","pdf",429932,1,22,"English","en",105,"# Technical Field\n## Background\n## Conventional Server Patching Workflow\n## High-Volume Manual Process and Its Limitations\n## Patching Life Cycle Overview\n## Server Selection, Runbooks, and Completion Tracking\n## Operational Steps During Patching","[{\"question\":\"What problem does the disclosure address?\",\"answer\":\"It addresses the complexity and risks of manual server patching, including security vulnerability handling, low predictability, and error-prone workflows across many machines and administrators.\"},{\"question\":\"What does the machine learning platform aim to do?\",\"answer\":\"It automatically perform patching of servers at internet scale to improve security responsiveness and reduce operational burden and failures.\"},{\"question\":\"What shortcomings are highlighted in the conventional patching process?\",\"answer\":\"The disclosure notes multiple sources of human error, low predictability (example around ~70%), higher failure resolution time (long MTTR), and insufficient centralized log aggregation for root-cause analysis.\"}]","MACHINE LEARNING PLATFORM TO AUTOMATICALLY PERFORM PATCHING OF SERVERS - 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