Systems | Development | Analytics | API | Testing

The Model Context Protocol (MCP) is an open standard designed to overcome the inherent limitations of large language model (LLM)-powered agentic applications. While these agentic applications excel at reasoning, summarizing, and content generation, they are fundamentally disconnected from the real world. They cannot access live data, interact with private systems, or execute tools on behalf of users.

Test case design constantly adopts new languages, architectures, and methodologies. But in the last few years, the pressure to scale while ensuring quality in a smart way, without overly increasing efforts, has changed the narrative. AI has entered the scene, promising to systematize decisions, reduce redundancy, and even create tests from scratch. Some teams have already experimented with AI, while others observe with skepticism.

Let me take you on a journey—not into some far-off sci-fi future, but into a tomorrow that’s just around the corner. Imagine this: you walk into your workplace and some of your “colleagues” are no longer human. They’re not robots in the traditional sense, but agents—autonomous software entities, each trained on vast datasets, equipped with decision-making power, and capable of performing economic, civic, and operational tasks at scale.

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.