Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As the adoption of large language models (LLMs) continues to surge, ensuring their security has become a top priority for organizations leveraging AI-powered applications. The OWASP LLM Top 10 for 2025 serves as a critical guideline for understanding and mitigating vulnerabilities specific to LLMs. This framework, modeled after the OWASP Top 10 for web security, highlights the most pressing threats associated with LLM-based applications and provides best practices for securing AI-driven systems.

ComplyCore is an ongoing compliance program that helps organizations reduce the complexity of compliance while saving time and money. Alpharetta, GA – Cybriant, a leader in cybersecurity services, today announced a new compliance management system, ComplyCORE. ComplyCORE provides a concise compliance system which eases the achievement of compliance across multiple regulations.

The average data breach costs businesses around $4.5 million to overcome. A single breach could even cause your company to fail. The good news is there are steps you can take to safeguard your sensitive information. Knowing the security threats you face goes a long way toward keeping your data safe. So, which of the following is a configuration vulnerability? We’ve created a guide with the answers. Let’s explore the information you need to know.

In the evolving landscape of artificial intelligence, Large Language Models (LLMs) have become essential for natural language processing tasks. They power applications such as chatbots, machine translation, and content generation. One of the most impactful implementations of LLMs is in Retrieval-Augmented Generation (RAG), where the model retrieves relevant documents before generating responses.

Large language models (LLMs) have transformed various industries by enabling advanced natural language processing, understanding, and generation capabilities. From virtual assistants and chatbots to automated content creation and translation services, securing LLM applications is now integral to business operations and customer interactions. However, as adoption grows, so do security risks, necessitating robust LLM application security strategies to safeguard these powerful AI systems.

Top tips is a weekly column where we highlight what’s trending in the tech world and list ways to explore these trends. This week, we’re looking at how you can reduce the amount of data you share online and minimize your digital footprint. How would you like it if someone followed you throughout the day, noting every single action you perform, every place you visit, and every interaction you have? Think about it; isn’t this exactly what happens when you use the internet?

Let’s start with the fundamentals: What is Address Resolution Protocol (ARP)? ARP is a vital networking protocol that ensures effective communication between devices on a local-area network (LAN). Its primary function is to map IP addresses to MAC addresses, allowing devices to recognize and communicate with each other within the network. ARP bridges the gap between the logical layer (IP addresses) and the physical layer (MAC addresses).

Cyber risk is the potentially negative impact to an organization when information systems fail or are damaged, disrupted or destroyed by unauthorized use or by cyberattack. In the world of risk management, risk is commonly defined as threat times vulnerability times consequence. The objective of risk management is to mitigate vulnerabilities to threats and the potential consequences, thereby reducing risk to an acceptable level.

AI training data has become a major concern as DeepSeek reached the top spot on the App Store, sparking debates about data privacy and national security. In a timely CNBC interview amid the DeepSeek controversy, Feroot Security CEO Ivan Tsarynny highlighted the critical intersection of data collection, AI development, and security risks. He emphasized how the data collected through apps plays a crucial role in AI advancements, raising further concerns about digital privacy.

A third-party data breach is a security incident where an organization's sensitive data is compromised or stolen due to a vulnerability or cyber attack on one of its third party vendors. This type of breach happens outside the primary organization's own IT infrastructure but still impacts them, as the third-party vendor, contractor, or service provider has access to their data.