Security Implications and Risk Management in Low-Code and RPA Deployments
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Keywords:
Security implications, Risk management, Low-Code Development, Robotic Process Automation (RPA), Digital transformation, Data privacy, Application vulnerabilitiesAbstract
As organizations increasingly embrace digital transformation through the adoption of Low-Code Development and Robotic Process Automation (RPA), the integration of these technologies raises critical considerations regarding security and risk management. This study conducts an in-depth exploration of the security implications associated with the deployment of Low-Code and RPA solutions, aiming to provide a comprehensive understanding of the potential risks and effective risk management strategies. The research investigates the unique security challenges posed by Low-Code and RPA deployments, considering factors such as data privacy, application vulnerabilities, and the potential impact on overall IT infrastructure.
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References
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Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.
Raparthi, M., Dodda, S. B., & Maruthi, S. (2020). Examining the use of Artificial Intelligence to Enhance Security Measures in Computer Hardware, including the Detection of Hardware-based Vulnerabilities and Attacks. European Economic Letters (EEL), 10(1).
Pargaonkar, S. (2020). Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering. Journal of Science & Technology, 1(1), 61-66.
Raparthi, M., Dodda, S. B., & Maruthi, S. (2021). AI-Enhanced Imaging Analytics for Precision Diagnostics in Cardiovascular Health. European Economic Letters (EEL), 11(1).
Vyas, B. (2021). Ensuring Data Quality and Consistency in AI Systems through Kafka-Based Data Governance. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(1), 59-62.
Rajendran, R. M. (2021). Scalability and Distributed Computing in NET for Large-Scale AI Workloads. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 10(2), 136-141.
Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.
Vyas, B. (2022). Optimizing Data Ingestion and Streaming for AI Workloads: A Kafka-Centric Approach. International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 1(1), 66-70.
Pargaonkar, S. (2021). Quality and Metrics in Software Quality Engineering. Journal of Science & Technology, 2(1), 62-69.
Vyas, B. Ethical Implications of Generative AI in Art and the Media. International Journal for Multidisciplinary Research (IJFMR), E-ISSN, 2582-2160.
Rajendran, R. M. (2022). Exploring the Impact of ML NET (http://ml. net/) on Healthcare Predictive Analytics and Patient Care. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 292-297.
Pargaonkar, S. (2021). The Crucial Role of Inspection in Software Quality Assurance. Journal of Science & Technology, 2(1), 70-77.
Pargaonkar, S. (2021). Unveiling the Future: Cybernetic Dynamics in Quality Assurance and Testing for Software Development. Journal of Science & Technology, 2(1), 78-84.
Pargaonkar, S. (2021). Unveiling the Challenges, A Comprehensive Review of Common Hurdles in Maintaining Software Quality. Journal of Science & Technology, 2(1), 85-94.
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License Terms
Ownership and Licensing:
Authors of this research paper submitted to the Journal of Science & Technology retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License.
License Permissions:
Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal of Science & Technology. This license allows for the broad dissemination and utilization of research papers.
Additional Distribution Arrangements:
Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in the Journal of Science & Technology.
Online Posting:
Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal of Science & Technology. Online sharing enhances the visibility and accessibility of the research papers.
Responsibility and Liability:
Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Journal of Science & Technology and The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.