Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A new report has found that nearly 40% of security leaders believe their organizations are least prepared for phishing and other social engineering attacks, Help Net Security reports. According to the report from VikingCloud, these concerns are driven by the increasing use of AI tools to assist in cyberattacks. “Generative or agentic AI-driven phishing attacks (51%) are leadership teams’ top concern when it comes to new cyberattack techniques,” the report says.

AI agents are quickly becoming “first-class citizens” in financial services, mimicking human behavior and holding privileged access that rivals employees. Yet unlike people, they don’t appear on your official org chart. The financial services sector already lives in a state of constant tension: the race to adopt new technologies for a competitive edge often faces off with the duty to preserve customer trust earned over decades of reliability, regulation, and security.

The eDiscovery landscape is undergoing a profound transformation, driven by the rapid evolution of artificial intelligence (AI). What was once a labor-intensive, manual process is now being revolutionized by technologies capable of analyzing vast volumes of data with speed, precision and insight. AI is not just a buzzword; it’s a catalyst for smarter, faster and more defensible legal workflows.

Healthcare has always been one of the toughest environments for maintaining privacy. Now add AI assistants, retrieval-augmented generation, and multimodal inputs like clinical images and voice notes. Sensitive information travels farther and faster than ever before, and the fallout from a single leak can be devastating, affecting clinical, legal, and reputational aspects. The question for 2025 is simple: how do we harness the advantages of AI without compromising private health data?

Large language model (LLM) security refers to the strategies and practices that protect the confidentiality, integrity, and availability of AI systems that use large language models. These models, such as OpenAI’s GPT series, are trained on vast datasets and can generate, translate, summarize, and analyze text. However, like any complex software component, LLMs present unique attack surfaces because they can be influenced by the data they process and the prompts they receive from users.

CYJAX has conducted research into the known use of AI on social media for fraudulent activities, uncovering how malicious actors exploit these technologies to deceive users and spread misinformation.