Recently, Sysdig published a blog post about the ways businesses can harden their LLM-based AI applications using the OWASP Top 10 for Large Language Models. So why are we writing about MITRE ATLAS, and how is this any different from the OWASP Top 10 for LLMs?

According to CIS, just in the first half of 2024, malware-based threats rose by 30% from 2023. A similar 30% year-over-year increase was also found in cyber attacks in 2024 in a report by Check Point Research. With such alarming statistics, it is evident that the need for threat monitoring has become more critical than ever before. In this blog post, we'll explore what threat monitoring entails, why it's essential, and how you can implement best practices to safeguard your business.

Artificial intelligence (AI) is increasingly embedded in modern organisations—from standalone generative AI apps to AI copilots in popular SaaS platforms, and the integration of self-hosted or public large language models (LLMs) in private applications tailored to specific business needs. The recently introduced EU AI Act sets out to regulate AI development and usage, ensuring that AI systems are safe, ethical, and respect fundamental rights.

As in many companies around the world, Bitsight leadership believes that adoption and innovation through the use of artificial intelligence (AI) capabilities is crucial to the future of our company. From the top down, our employees are continually on the hunt for ways to leverage AI to improve business outcomes and customer productivity.

Masking large volumes of data isn’t just a bigger version of small-scale masking—it’s exponentially more complex. High-volume data masking introduces unique engineering challenges that demand careful balancing of performance, integration, accuracy, and infrastructure costs. In this blog, we’ll dive into the critical factors you must consider when choosing the right tool for large-scale data masking, helping you confidently navigate these complexities.

AI-powered solutions are becoming increasingly prevalent in enterprise environments, leading to a new trend in procurement: the need for comprehensive AI governance frameworks. This shift reflects a growing awareness of AI-specific risks and the need for more granular controls. As the founder of Nightfall AI, a startup building AI models for data security, I've navigated hundreds of procurement cycles and observed a rise in AI-specific requirements.

Large Language Models (LLMs) have become central to many AI-driven applications. These models, such as OpenAI’s GPT and Google’s Bard, process massive amounts of data to generate human-like responses. Their ability to handle natural language has revolutionized industries from customer service to healthcare. However, as their use expands, so do concerns about LLM security. LLM security is critical because these models handle sensitive data, making them tempting targets for cybercriminals.

Eric Brüggemann, the new CEO of Code Intelligence, stepped into the role in September. We asked him about his vision of the role and future of AI and fuzz testing in securing automotive software.