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.
The rapid integration of large language models (LLMs) into AI applications brings significant benefits but also introduces several supply chain risks. Developers and security experts using LLMs must understand AI supply chain risks and know how to mitigate them effectively.
Adding AI to technology has become the new trend and laptop processor manufacturers have jumped on this bandwagon by adding neural processing units (NPUs) to handle on-device AI tasks. Learn more about these processors with NPUs to get the best performance for your cost and needs when upgrading.
As AI becomes more integrated into applications, there is a greater need for more powerful and efficient chips for computers and laptops. The Intel Core Ultra is an advancement in chip technology that claims to increase on-device AI performance and accessibility for laptops and tablets. While this processor family may be a low-cost entry point for accelerating AI adoption, businesses that use or build on-device AI applications should consider these options carefully.
The release of LLMs with extended context length marks a significant advancement, enabling more comprehensive applications for these models. Developers and software engineers need to grasp the concept of context length and its impact on design before incorporating or developing applications with enhanced context LLMs to utilize this capability fully.
AI chatbots are useful tools to deploy on websites to assist customers. Benefits include boosting user experience, making websites more friendly, and reducing the cost of support staff. Despite all of the good, AI chatbots can do more harm than expected. Web development teams and UX designers must understand these dangers to create a successful AI chatbot deployment strategy.