Secure Software Supply Chains: Managing Dependencies in an AI-Augmented Dev World
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I3P110Keywords:
Software Supply Chain Security, Dependency Management, AI-Augmented Development, DevSecOps, SBOM (Software Bill of Materials), Secure-by-Design, Vulnerability Scanning, Anomaly DetectionAbstract
Artificial intelligence (AI)-driven tools and open-source usage accelerated this process, making modern software development much more complex than it was a few years ago, and thereby changing the process of application creation and delivery. Although AI-augmented environments can also improve the productivity of the developers by automatically code-generating, providing intelligent suggestions, and accelerated iterations, they are also adding novel risks to the software supply chain. These involve the incorporation of untested dependencies, possible license clashes, poor provenance of the code, and a wider exploitable attack surface that is induced by dynamic or transitive components. The paper will discuss the problem and the solutions to the problem of managing software dependencies in AI-augmented development. It examines how the conventional supply chain weaknesses like dependency confusion, typosquatting, and compromised open-source packages are potentiated by AI-powered tooling. Supply chain insecurity leads to the outcomes indicated in case studies involving the SolarWinds Orion hack and the Log4Shell vulnerability. To counter it, we discuss a tiered and layered defence approach that refers to the emergence of sound practices: Software Bill of Materials (SBOMs), zero trust, secure-by-design devices and DevSecOps integration. The paper also discusses the possibility of AI as a risk factor as well as a tool of defence, their use as an anomaly detector, an autonomous dependency manager, a version-drift analyzer, and, with the help of AI, they can be used to audit code. Organizations can protect their software ecosystems and capitalize on the efficiencies of intelligent development processes by proactively governing themselves, automating, and implementing concepts of cross-functional collaboration
References
[1] Levine, S. (2020). AI-Augmented Software Engineering: Automated Code Generation and Optimization Using Large Language Models. International Journal of Emerging Trends in Computer Science and Information Technology, 1(4), 21-29.
[2] Okafor, C., Schorlemmer, T. R., Torres-Arias, S., & Davis, J. C. (2022, November). Sok: Analysis of software supply chain security by establishing secure design properties. In Proceedings of the 2022 ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defences (pp. 15-24).
[3] Nadgowda, S. (2022, November). Engram: The One Security Platform for Modern Software Supply Chain Risks. In Proceedings of the Eighth International Workshop on Container Technologies and Container Clouds (pp. 7-12).
[4] Handoyo, E., Jansen, S., & Brinkkemper, S. (2013, October). Software Ecosystem Modelling: The Value Chains. In Proceedings of the fifth international conference on management of emergent digital ecosystems (pp. 17-24).
[5] Jansen, S., & Cusumano, M. A. (2013). Defining software ecosystems: a survey of software platforms and business network governance. In Software ecosystems (pp. 13-28). Edward Elgar Publishing.
[6] Legenvre, H., Hameri, A. P., & Golini, R. (2022). Ecosystems and supply chains: How do they differ and relate? Digital Business, 2(2), 100029.
[7] Piergiorgio Ladisa, Henrik Plate, Matias Martinez, Olivier Barais, “Taxonomy of Attacks on Open Source Software Supply Chains,” arXiv, Apr. 8, 2022.
[8] Pham, P., Nguyen, V., & Nguyen, T. (2022, October). A review of AI-augmented end-to-end test automation tools. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (pp. 1-4).
[9] Levin, S. A. (1998). Ecosystems and the biosphere as complex adaptive systems. Ecosystems, 1(5), 431-436.
[10] Raatikainen, M., Motger, Q., Lüders, C. M., Franch, X., Myllyaho, L., Kettunen, E., ... & Männistö, T. (2022). Improved management of issue dependencies in issue trackers of large collaborative projects. IEEE Transactions on Software Engineering, 49(4), 2128-2148.
[11] Strode, D. E. (2016). A dependency taxonomy for agile software development projects. Information Systems Frontiers, 18(1), 23-46.
[12] Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International journal of production research, 57(7), 2179-2202.
[13] Putra, A. M., & Kabetta, H. (2022, October). Implementation of DevSecOps by integrating static and dynamic security testing in CI/CD pipelines. In 2022, the IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM) (pp. 1-6). IEEE.
[14] Nogueira, A. F., & Zenha-Rela, M. (2021). Monitoring a CI/CD workflow using process mining. SN Computer Science, 2(6), 448.
[15] Darem, A. A., Ghaleb, F. A., Al-Hashmi, A. A., Abawajy, J. H., Alanazi, S. M., & Al-Rezami, A. Y. (2021). An adaptive behavioral-based incremental batch learning malware variants detection model using concept drift detection and sequential deep learning. IEEE Access, 9, 97180-97196.
[16] “Supply Chain Attacks: 6 Steps to Protect Your Software Supply Chain,” press release, GOV.UK, Nov. 5, 2021.
[17] Rotter, J. P., Airike, P. E., & Mark-Herbert, C. (2014). Exploring political corporate social responsibility in global supply chains. Journal of Business Ethics, 125(4), 581-599.
[18] Dalmarco, G., & Barros, A. C. (2018). Adoption of Industry 4.0 technologies in supply chains. In Innovation and Supply Chain Management: relationship, collaboration and strategies (pp. 303-319). Cham: Springer International Publishing.
[19] Williams, Z., Ponder, N., & Autry, C. W. (2009). Supply Chain Security Culture: Measurement, Development and Validation. The International Journal of Logistics Management, 20(2), 243-260.
[20] Sobb, T., Turnbull, B., & Moustafa, N. (2020). Supply chain 4.0: A survey of cyber security challenges, solutions and future directions. Electronics, 9(11), 1864.
[21] Al-Farsi, S., Rathore, M. M., & Bakiras, S. (2021). Security of Blockchain-Based Supply Chain Management Systems: Challenges and Opportunities. Applied Sciences, 11(12), 5585.
[22] Pappula, K. K., & Rusum, G. P. (2020). Custom CAD Plugin Architecture for Enforcing Industry-Specific Design Standards. International Journal of AI, BigData, Computational and Management Studies, 1(4), 19-28. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V1I4P103
[23] Rahul, N. (2020). Optimizing Claims Reserves and Payments with AI: Predictive Models for Financial Accuracy. International Journal of Emerging Trends in Computer Science and Information Technology, 1(3), 46-55. https://doi.org/10.63282/3050-9246.IJETCSIT-V1I3P106
[24] Enjam, G. R., & Tekale, K. M. (2020). Transitioning from Monolith to Microservices in Policy Administration. International Journal of Emerging Research in Engineering and Technology, 1(3), 45-52. https://doi.org/10.63282/3050-922X.IJERETV1I3P106
[25] Pappula, K. K., & Anasuri, S. (2021). API Composition at Scale: GraphQL Federation vs. REST Aggregation. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 54-64. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P107
[26] Pedda Muntala, P. S. R., & Jangam, S. K. (2021). Real-time Decision-Making in Fusion ERP Using Streaming Data and AI. International Journal of Emerging Research in Engineering and Technology, 2(2), 55-63. https://doi.org/10.63282/3050-922X.IJERET-V2I2P108
[27] Rahul, N. (2021). AI-Enhanced API Integrations: Advancing Guidewire Ecosystems with Real-Time Data. International Journal of Emerging Research in Engineering and Technology, 2(1), 57-66. https://doi.org/10.63282/3050-922X.IJERET-V2I1P107
[28] Enjam, G. R., & Chandragowda, S. C. (2021). RESTful API Design for Modular Insurance Platforms. International Journal of Emerging Research in Engineering and Technology, 2(3), 71-78. https://doi.org/10.63282/3050-922X.IJERET-V2I3P108
[29] Pappula, K. K. (2022). Containerized Zero-Downtime Deployments in Full-Stack Systems. International Journal of AI, BigData, Computational and Management Studies, 3(4), 60-69. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I4P107
[30] Jangam, S. K., Karri, N., & Pedda Muntala, P. S. R. (2022). Advanced API Security Techniques and Service Management. International Journal of Emerging Research in Engineering and Technology, 3(4), 63-74. https://doi.org/10.63282/3050-922X.IJERET-V3I4P108
[31] Anasuri, S. (2022). Zero-Trust Architectures for Multi-Cloud Environments. International Journal of Emerging Trends in Computer Science and Information Technology, 3(4), 64-76. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I4P107
[32] Pedda Muntala, P. S. R., & Karri, N. (2022). Using Oracle Fusion Analytics Warehouse (FAW) and ML to Improve KPI Visibility and Business Outcomes. International Journal of AI, BigData, Computational and Management Studies, 3(1), 79-88. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I1P109
[33] Rahul, N. (2022). Optimizing Rating Engines through AI and Machine Learning: Revolutionizing Pricing Precision. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 93-101. https://doi.org/10.63282/3050-9262.IJAIDSML-V3I3P110
[34] Enjam, G. R. (2022). Secure Data Masking Strategies for Cloud-Native Insurance Systems. International Journal of Emerging Trends in Computer Science and Information Technology, 3(2), 87-94. https://doi.org/10.63282/3050-9246.IJETCSIT-V3I2P109
