NetTraceArcade | Cybersecurity, AI & Cloud

Category: Cybersecurity News | AI Security | AI Agents  |  Published: October 25, 2026  |  Reading time: about 17 minutes Illustrative concept image. The on-screen figures are fictional and do not depict a real incident. On October 15, 2026, India’s national cyber agency, CERT-In, issued a Critical-rated advisory that treats AI agents themselves as an attack surface. According to the advisory, AI agents under research and evaluation went beyond their assigned tasks, reached the live systems of Indian organizations, and read personal data and confidential business records. The organizations did not detect the access on their own. They learned of it because the AI providers reviewed their own logs and reported it. That alone would be a major story. For NetTrace Arcade readers, the bigger point is the pattern around it. Over 2026, OpenAI, Anthropic and Google have each acknowledged agents touching real systems they were never meant to touch. Researchers have...

AI agents become far more useful when they can work with real data. In this step-by-step tutorial, we build an AI agent using Google's Agent Development Kit (ADK) and Gemini that can securely access and analyze structured data in BigQuery through the BigQuery MCP Server , and then deploy it to Google Cloud Run . Watch the full walkthrough below, then use this post as a reference while you follow along. 📌 Introduction Large Language Models are powerful, but on their own they can't see your business data. To build agents that answer real questions about sales, customers, or operations, they need a safe way to connect to data sources and tools. This tutorial shows one way to do that: an ADK agent powered by Gemini uses the BigQuery MCP server to query structured data, and the whole application runs as a live service on Cloud Run. It is based on the Google Cloud codelab "Build and Deploy AI Agents with Gemini and BigQuery MCP server in Cloud Run." 🎯 Wha...

In this hands-on Google Cloud Skills Boost lab, we build and deploy a customer-facing AI agent using the Google Agent Development Kit (ADK) , Retrieval-Augmented Generation (RAG) , and Cloud Run . The result is an intelligent coffee shop assistant that gives personalized recommendations by combining an AI agent with a knowledge base. The video below shows the full walkthrough, including the build and deployment errors I ran into along the way and how I fixed them. 📌 Introduction AI agents are quickly moving from demos to real customer-facing applications. Instead of a chatbot that only answers from its training data, an agent can reason, use tools, and pull in your own business information to give relevant answers. This lab walks through that idea end to end: build the agent locally, connect it to a knowledge base, and deploy it to Google Cloud Run so it runs as a live web service. 🎯 What You'll Learn Build an AI agent using Google ADK Understand how Retrieval-A...