LLM-augmented DevSecOps should land around 0.6–1.0% of total IT budget, with clear diminishing returns beyond ~1.5%. The biggest risk right now is tool sprawl and skills dilution, not lack of AI. The goal for IT executives should be to buy down risk and lead time, not to “AI everything” in their security infrastructure.
The October 29, 2025 MIT Iceberg Index headline finding is that visible AI adoption in tech accounts for only 2.2% of wage value, while “below the waterline” cognitive work across offices in industries like finance, and professional services pushes technical exposure to 11.7% in the US. For big organizations, this is less of a sci-fi speculation and more of a planning KPI. If 10–15% of your wage bill is doing skills that tools can already replicate, your real risk is being out-executed by peers that quietly turn that into lower operating costs and faster cycle times.
As businesses grow, it is common to experience application sprawl given that on average, businesses use 130 SaaS applications. Application sprawl leads to underutilized and redundant applications and unnecessary subscription costs. CIOs and IT leaders can solve these issues using application retirement, an application rationalization strategy, to optimize their application portfolio.
Organizations face complexities in managing the software development lifecycle (SDLC) as microservice architectures grow, especially with end-to-end (E2E) testing. This article explores Uber’s shift-left approach to E2E testing, which moved E2E testing earlier in the SDLC, reducing incidents by 71%. Security leaders and IT managers who aim to enhance software quality and operational efficiency should apply these practical insights to their enterprises.
Misconfigured cloud object storage, such as Amazon S3 and Azure Blob Storage, often leads to data breaches, exposing sensitive information. Proper configuration, including encryption, least privilege access, versioning, and network security, is essential. Cybersecurity professionals and solutions architects should read this article to ensure their storage configurations follow best practices, safeguarding sensitive data from unauthorized access.
Organizations moving to DevSecOps face challenges such as limited resources and the need for multifaceted expertise. Integrating Large Language Models (LLMs) into DevSecOps can enhance automation, reduce manual errors, and augment human capacity. Tech leaders and security experts should strategically leverage LLMs within their DevSecOps frameworks to enhance operational efficiency and drive innovation while ensuring robust security throughout the development process.
The gaming industry is lucrative and saturated with many game studios. A major challenge faced by game studios is development time. AI game engines improve on traditional game engines by automatically generating a game, decreasing development time, and enhancing realism. Decision-makers at game studios should pay attention to AI game engines and start planning for their use soon.
Organizations are embracing multi-cloud and hybrid-cloud strategies. Unfortunately, managing data across multiple clouds introduces challenges like complexity in data management, governance, security, and data integration. Modern data management approaches can help organizations manage these complexities to ensure seamless integration and improved data utilization. Business leaders and data professionals should read this article to discover strategies for enhancing data governance, integration, and value extraction.
The new year brings more challenges and opportunities for CIOs and IT executives. Knowing what they are and how to meet them is crucial for enterprises to excel in their respective markets. This four-part series identifies the four major trends IT leaders must navigate in 2025–the first is Artificial Intelligence (AI).
AI’s fast evolution has sparked innovation and creativity, but this has also made it difficult to regulate. Laws like the EU AI Act are in force and similar laws will be rolled out in other jurisdictions. Two bills from Colorado and California are examples of extensive responses to regulating AI in the US at the state level. AI service providers operating within the US must pay attention to these two bills and prepare themselves for future legislation from other states.