Systems | Development | Analytics | API | Testing

Chatbots are quickly becoming more sophisticated and integrated into business workflows, enhancing productivity and scalability. However, they also expand the attack surface for organizations. This new exploitation vector requires data engineers and security teams to incorporate various security guardrails when building their gen AI architecture. In this blog post, we discuss the risk of data leakage through AI chatbots.

In March 2024, the European Union passed the AI Act. This sweeping regulation reshapes how organizations deploy and manage AI systems. The law addresses AI risks that could affect both individuals and businesses, from hiring biases to critical infrastructure failures. Similar rules have started taking shape across the world, including several state-based regulations in the US. But the regulation is only the floor. We all share the responsibility of creating a safe, responsible AI future.

Katalon officially signed a Memorandum of Understanding (MOU) with RMIT University Vietnam. This collaboration aims to bridge academic learning with real-world software development and software testing expertise, equipping students with an adaptive mindset, future-ready skills and AI tools. Leaders and representatives from RMIT Vietnam and Katalon at the MoU signing ceremony.

The era of generative AI is upon us, and large language models (LLMs) like Claude are demonstrating incredible potential to revolutionize how businesses operate and interact with customers. However, to truly unlock this potential, AI needs secure and standardized access to the wealth of information and services locked within enterprise systems. This is where standards such as Model Context Protocol (MCP) come in, offering a powerful way to make enterprise resources AI-consumable.

In a new Forbes Insights report, 500 C-suite tech leaders share how they’re navigating data complexity, scaling AI, and prioritizing governance — and why modern integration is now mission-critical.

Enterprise adoption of large language models (LLMs) is surging. According to Gartner, more than 80% of enterprises will have deployed generative AI (GenAI) applications or used GenAI APIs by 2026, up from just 5% in 2023. That stark increase paints a telling picture: LLMs have evolved from a fringe technology to a cornerstone of business development and productivity. But as with any new technology, competition is fierce.