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@article{194992,
        author = {Maddineni Sasidhar and M G JAGADEESH and Y SIVA NARAYANA},
        title = {AI-Powered DevSecOps Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6407-6419},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194992},
        abstract = {Getting software out the door quickly without cutting corners on security is something every development team wrestles with, and most DevSecOps setups today make it harder than it needs to be — scattered tools, steep costs, and security checks that show up too late to actually help. This paper describes an AI-Powered DevSecOps SaaS Platform built to pull those pieces together — CI/CD automation, security checks from the start, and smarter vulnerability ranking, all in one place. Rather than flagging everything equally, the platform uses AI to evaluate each issue in context and sort it by actual risk.
Running on Python/Go backends, a React frontend, and Kubernetes, the system is built to scale without requiring a dedicated security team to operate it. Startups and smaller companies get access to the same level of DevSecOps maturity that enterprises typically rely on expensive specialists to maintain.
Testing shows teams can fix issues faster, see their risk exposure more clearly, and spend less effort managing security operations overall. The framework shows it's possible to move quickly and stay secure without treating those two goals as opposites.},
        keywords = {Supply Chain Management, Artificial Intelligence, Machine Learning, Internet of Things, Predictive Analytics, Real-Time Monitoring, Reinforcement Learning, Data Fusion, Risk Mitigation, Operational Resilience.},
        month = {March},
        }