Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

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.

It is first period in a busy school district. Teachers are opening their learning management systems to take attendance, preparing lesson slides, and answering a few messages from parents. Students are logging into Chromebooks after sneaking in a final Snap before leaving their phones in lockers. In the finance office, payments are being processed.

Organizations worldwide are increasingly developing or implementing AI-powered tools to streamline operations and scale efficiently. However, the benefits come with unpredictable risks unique to AI that need to be mitigated with the right safeguards. ‍ One of the biggest AI security challenges is the lack of formalized oversight. According to Vanta’s State of Trust Report, only 36% of organizations have AI-informed security policies in place or are in the process of building them.