This study investigates the impact of integrating DevSecOps and Generative Artificial Intelligence (GAI) on software delivery performance within technology firms. Utilizing a qualitative research methodology, the research involved semi-structured interviews with industry practitioners and analysis of case studies from organizations that have successfully implemented these methodologies. The findings reveal significant enhancements in research and development (R&D) efficiency, improved source code management, and heightened software quality and security. The integration of GAI facilitated automation of coding tasks and predictive analytics, while DevSecOps ensured that security measures were embedded throughout the development lifecycle. Despite the promising results, the study identifies gaps related to the generalizability of the findings due to the limited sample size and the qualitative nature of the research. This paper contributes valuable insights into the practical implementation of DevSecOps and GAI, highlighting their potential to transform software delivery processes in technology firms. Future research directions include quantitative assessments of the impact on specific business outcomes and comparative studies across different industries.

Alert fatigue is a common issue faced by software teams using the DevSecOps paradigm. The overwhelming number of warnings and alerts generated by security and code scanning tools, particularly in smaller teams where resources are limited, leads to desensitization and diminished responsiveness to security warnings, potentially exposing systems to vulnerabilities. This paper explores the potential of LLMs in generating actionable security reports that emphasize the financial impact and consequences of detected security issues, such as credential leaks, if they remain unaddressed. A survey conducted among developers indicates that LLM-generated reports significantly enhance the likelihood of immediate action on security issues by providing clear, comprehensive, and motivating insights. Integrating these reports into DevSecOps workflows can mitigate attention saturation and alert fatigue, ensuring that critical security warnings are addressed effectively.

DevOps has emerged as one of the most rapidly evolving software development paradigms. With the growing concerns surrounding security in software systems, the DevSecOps paradigm has gained prominence, urging practitioners to incorporate security practices seamlessly into the DevOps workflow. However, integrating security into the DevOps workflow can impact agility and impede delivery speed. Recently, the advancement of artificial intelligence (AI) has revolutionized automation in various software domains, including software security. AI-driven security approaches, particularly those leveraging machine learning or deep learning, hold promise in automating security workflows. They reduce manual efforts, which can be integrated into DevOps to ensure uninterrupted delivery speed and align with the DevSecOps paradigm simultaneously. This paper seeks to contribute to the critical intersection of AI and DevSecOps by presenting a comprehensive landscape of AI-driven security techniques applicable to DevOps and identifying avenues for enhancing security, trust, and efficiency in software development processes. We analyzed 99 research papers spanning from 2017 to 2023. Specifically, we address two key research questions (RQs). In RQ1, we identified 12 security tasks associated with the DevOps process and reviewed existing AI-driven security approaches. In RQ2, we discovered 15 challenges encountered by existing AI-driven security approaches and derived future research opportunities. Drawing insights from our findings, we discussed the state-of-the-art AI-driven security approaches, highlighted challenges in existing research, and proposed avenues for future opportunities.

The rapid adoption and scrutiny around ChatGPT, the latest artificial intelligence model, highlights the exploding potential of AI. It also underscores a stark delta between AI's current capabilities and human willingness to embrace them. This untapped potential is perhaps most apparent in the public sector where AI is both championed and feared - offering exciting federal use cases but lacking policy guidance and maturity at scale. So how can agencies better leverage AI? Results of a recent SAIC survey uncover a need to improve overall AI readiness to enable the technology's integration and benefit from it. The independent survey of federal government executives revealed that while AI is on respondents' radar, fewer than one in five are "very" likely to adopt AI in the next year.

Iterative Health is pioneering the use of artificial intelligence-based precision medicine in gastroenterology, with the aim of helping to optimize clinical trials investigating the treatment of IBD. We use advanced machine learning and computer vision to interpret endoscopic videos along with other types of data, helping clinicians better assess patients with potential GI problems. Ultimately, the company aims to establish more meaningful endpoints to serve as better predictors of both therapeutic response and disease outcomes. Iterative Health is seeking a Principal Infrastructure Engineer(DevSecOps) to develop Cloud Infrastructure Security strategies for data, applications and machine learning model development as well as to implement security measurements and monitoring and support security compliances. At Iterative Health, we're actively working towards creating an environment that is representative of the diversity of patients our technology serves.

When discussing AI in software development, we often talk about machine learning. But is this the same thing? Can machine learning replace DevOps? And can AI completely replace DevOps? This article will explore the differences between machine learning and AI and how to integrate both in your organization.

Software development teams are adapting AI & ML models into their apps and platforms to lessen DevOps lags. AI-driven DevOps will be the way of the future and flow with the tide. Software development tool vendors are speeding up the pace of integrating AI and machine learning models into their apps while seeking ways to lessen the delays in DevOps teams. Artificial intelligence will replace people as the essential tool for computing & analysis, revolutionizing how teams create, distribute, deploy, and manage applications since humans are not suited to handle the enormous volumes of data and computing required in daily operations. But first, let's grasp how AI and DevOps are related before we explore how ai ml will impact DevOps.

Everyone in business today "feels the need for speed". But probably none more so than application developers, who have found themselves dragged out from behind their cubicle walls and thrust into the spotlight of digital transformation. The most successful developers now work closely with the business side using methodologies like Agile and DevOps, which is also in the name of speed to bring products to light sooner. Yet they must do so with the business goals always in focus. Developers are expected to think about the customer experience, create apps in the cloud, enable them for mobile, AI, IoT, edge -- and now to help secure those apps.

DevSecOps (a collective term used for development, security, and operations), is the integration of security that goes on through the multiple phases of the lifecycle of software development. Operations on DevSecOps begin from initial design through deployment, testing, integration, and software delivery. If we look at it, DevSecOps represents an essential evolution in the security approach for development organizations. We term'DevSecOps' as an evolution because it revolutionizes the way operations have changed. Previously, 'security' was'tacked on' to the final product, tested by separate quality assurance (QA) and security team at the end of the development cycle. Now that we know what DevSecOps is let's look at the different pillars of the process!

Wind River today revealed a waterfall of new features available designed to automate and accelerate DevSecOps and other "pipelines" across the lifecycle of intelligent systems. The latest release of their platform is focused on transformational automation technologies, including a customizable automation engine, digital feedback loop, enhanced security, and analytics with machine learning capabilities. The announcement also included industry-proven technologies from ecosystem partners to the Wind River Studio Marketplace, which makes solutions available that are developed and delivered on the Wind River Studio "cloud-native platform for the development, deployment, operations, and servicing of mission-critical intelligent systems from devices to cloud." The company claims the platform "enables dramatic improvements in productivity, agility, and time-to-market, with seamless technology integration that includes far edge cloud compute, data analytics, security, 5G, and AI/ML." "The next generation of cloud-connected intelligent systems require the right software infrastructure to securely capture and process real-time machine data with digital feedback from a multitude of embedded systems, enabling advanced automated and autonomous scenarios," said Kevin Dallas, president, and CEO, Wind River.