Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems
In today's data-driven environment, protecting machine learning ecosystems has taken on critical importance. Organisations are relying more and more on AI and ML models to guide important decisions and operations, which have led to an increase in system vulnerabilities. The critical need for te...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/21/e3sconf_icecs2024_02033.pdf |
| _version_ | 1797299452841558016 |
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| author | Dhabliya Dharmesh Rizvi Nuzhat Dhablia Anishkumar Sridhar A. Phani Kale Sunil D. Padhi Dipanjali |
| author_facet | Dhabliya Dharmesh Rizvi Nuzhat Dhablia Anishkumar Sridhar A. Phani Kale Sunil D. Padhi Dipanjali |
| author_sort | Dhabliya Dharmesh |
| collection | DOAJ |
| description | In today's data-driven environment, protecting machine learning ecosystems has taken on critical importance. Organisations are relying more and more on AI and ML models to guide important decisions and operations, which have led to an increase in system vulnerabilities. The critical need for techniques to create resilient machine learning (ML) systems that can withstand changing threats is discussed in this study.Data protection is an important component of securing ML environments. Every part of the process, from data preprocessing through model deployment, needs to be secured. In order to reduce potential vulnerabilities, this incorporates code review procedures, safe DevOps practises, and container security.System resilience is vitally dependent on on-going monitoring and anomaly detection. Organisations can respond quickly to security problems by detecting deviations from normal behaviour early on and adjusting their defences as necessary.A strong incident response plan is essential. To protecting machine learning ecosystems necessitates a comprehensive strategy that includes monitoring, incident response, model security, pipeline security, and data protection. By implementing these tactics, businesses may create robust machine learning (ML) systems that can endure the changing threat landscape, protect their data, and guarantee the validity of their AI-driven decision-making processes. |
| first_indexed | 2024-03-07T22:50:54Z |
| format | Article |
| id | doaj.art-d74642d091284454abf45e79a45765e8 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-07T22:50:54Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-d74642d091284454abf45e79a45765e82024-02-23T10:21:00ZengEDP SciencesE3S Web of Conferences2267-12422024-01-014910203310.1051/e3sconf/202449102033e3sconf_icecs2024_02033Securing Machine Learning Ecosystems: Strategies for Building Resilient SystemsDhabliya Dharmesh0Rizvi Nuzhat1Dhablia Anishkumar2Sridhar A. Phani3Kale Sunil D.4Padhi Dipanjali5Professor, Department of Information Technology, Vishwakarma Institute of Information TechnologyDirector, Symbiosis Law School, Nagpur Campus, Symbiosis International (Deemed University)Engineering Manager, Altimetrik India Pvt LtdAssociate Professor, Dept of CSE, Aditya Engineering CollegeDepartment of Artificial Intelligence & Data Science, Vishwakarma Institute of Information TechnologyDhole Patil college of EngineeringIn today's data-driven environment, protecting machine learning ecosystems has taken on critical importance. Organisations are relying more and more on AI and ML models to guide important decisions and operations, which have led to an increase in system vulnerabilities. The critical need for techniques to create resilient machine learning (ML) systems that can withstand changing threats is discussed in this study.Data protection is an important component of securing ML environments. Every part of the process, from data preprocessing through model deployment, needs to be secured. In order to reduce potential vulnerabilities, this incorporates code review procedures, safe DevOps practises, and container security.System resilience is vitally dependent on on-going monitoring and anomaly detection. Organisations can respond quickly to security problems by detecting deviations from normal behaviour early on and adjusting their defences as necessary.A strong incident response plan is essential. To protecting machine learning ecosystems necessitates a comprehensive strategy that includes monitoring, incident response, model security, pipeline security, and data protection. By implementing these tactics, businesses may create robust machine learning (ML) systems that can endure the changing threat landscape, protect their data, and guarantee the validity of their AI-driven decision-making processes.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/21/e3sconf_icecs2024_02033.pdfmachine learningdecision makingresilient systemsecurity model |
| spellingShingle | Dhabliya Dharmesh Rizvi Nuzhat Dhablia Anishkumar Sridhar A. Phani Kale Sunil D. Padhi Dipanjali Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems E3S Web of Conferences machine learning decision making resilient system security model |
| title | Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems |
| title_full | Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems |
| title_fullStr | Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems |
| title_full_unstemmed | Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems |
| title_short | Securing Machine Learning Ecosystems: Strategies for Building Resilient Systems |
| title_sort | securing machine learning ecosystems strategies for building resilient systems |
| topic | machine learning decision making resilient system security model |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/21/e3sconf_icecs2024_02033.pdf |
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