The article argues that 2026 marks a shift toward autonomous, agentic AI systems capable of planning and acting, not just answering questions. These agents are often integrated with large language models (LLMs) or retrieval-augmented generation (RAG) systems. Unlike reactive chatbots, they can execute actions such as mass-sending emails, manipulating databases, and interacting with internal platforms or external apps. This raises the cybersecurity threat surface and introduces new risk vectors that are harder to guard against with traditional defenses. The piece suggests the security landscape is at a turning point due to the autonomy and complexity of these agents. Overall, it calls for a rethinking of security practices to address these increasingly capable AI agents.

• Highlight the shift to autonomous agents with planning and reasoning capabilities
• Note the broad action surface, including emails, databases, and app interactions
• Warn that this autonomy creates novel cybersecurity risks and defense challenges
• Emphasize the need for updated governance and security practices

The Indian Institute of Technology Kharagpur announced the launch of four online executive courses in artificial intelligence, machine learning, and technology leadership. The programs are delivered online as executive education offerings. The Indian Express reported the launch. The courses are designed to upskill mid-career professionals seeking leadership in technology and AI-focused projects. The initiative expands access to advanced AI education and supports workforce development.

• Announce four online executive courses in AI, ML, and tech leadership
• Target mid-career professionals
• Deliver via online executive education format
• Cite coverage by The Indian Express
• Aims to upskill professionals for tech leadership

The IEEE Signal Processing Society is inviting submissions for a forthcoming special issue of the IEEE Journal of Selected Topics in Signal Processing (JSTSP) focusing on Autonomous and Evolutive Optimization in Networked AI. The topic is described as a transformative paradigm for Signal Processing and AI communities. The approach combines traditional knowledge-based adaptive signal processing techniques and data-centric deep neural network models. The concept centers on systems that dynamically acquire high-quality data in the continuous inferences of networked AI models. The call highlights the interdisciplinary nature of the field and its potential to reshape both research and application in networked AI.

• Announce JSTSP special issue on autonomous optimization
• Emphasize integration of traditional adaptive signal processing with deep learning
• Highlight dynamic data acquisition during AI inferences
• Call for submissions to authors across disciplines