In the early days of cybersecurity, implementing a Security Information and Event Management (SIEM) system was akin to constructing a house from scratch. The SIEM was a blank slate, and transforming raw data into actionable insights was a long and arduous journey. It began with the daunting task of ingesting data from various disparate sources and formats. From there, security teams had to craft detections — rules designed to identify malicious or suspicious activity.

In this age of digital supremacy, keeping our data safe and respecting privacy are super important. As more and more people and businesses use online platforms, it’s crucial to understand what types of data need that extra layer of protection, especially when it comes to PII vs PHI vs PCI. Understanding the distinctions between PII (Personally Identifiable Information), PHI (Protected Health Information), and PCI (Payment Card Information) is crucial.

Striking the right balance between innovation using Artificial Intelligence (AI) and Large Language Models (LLMs) and data protection is essential. In this blog, we’ll explore critical strategies for ensuring AI and LLM data security, highlighting some trade-offs.

As artificial intelligence (AI) continues to revolutionize various sectors, ensuring it is developed and deployed in alignment with ethical standards and fundamental rights is critical for businesses that use it. The European Union's Artificial Intelligence Act (AI Act), formally adopted on March 13, 2024, addresses this critical necessity by establishing a comprehensive and detailed legal framework for AI systems within the EU.

Snyk Code was the only code security tool shortlisted by developers as an AI tool they’ve been regularly using this past year or are looking forward to using next year in Stack Overflow’s recent 2024 AI Search and Developer Tools survey. This underlines Snyk’s dominance as the favorite AI security tool of both developers and security teams and confirms that Snyk Code is providing immense value to developers.

The momentous rise of AI continues, and more and more customers are demanding concrete results from these early implementations. The time has come for tech companies to prove what AI can do beyond adding conversational chat agents to website sidebars. Fortunately, it’s easy to see how cloud data protection has already benefited from advancements in AI and ML. Headline-grabbing large-language models are also making protecting data in the cloud easier to manage across organizations. ‍

Earlier this month, I was thrilled to join forces with the team at Dark Reading for a webinar on the future of AI in security operations. Titled CISO Perspectives: How to make AI an accelerator, not a blocker, the webinar allowed me to take a deep dive into the future role of AI in security with some of the most knowledgeable CISOs on the subject, Mandy Andress of Elastic and Matt Hillary of Drata.

Happy International Dog Day! This official holiday celebrates our furry friends and the joy they bring to our lives! Today is particularly special for all of us at Snyk because of our four-legged mascot, Patch the Doberman. But what exactly does a dog have to do with application security? Here at Snyk, we see the idea of a “guard dog” protecting someone’s home as similar to how AppSec solutions can protect today’s development practices.

Australian and New Zealand financial service institutions (FSIs) are facing pressure to innovate quickly while maintaining robust security and regulatory compliance. Many, like ANZ Bank and Commonwealth Bank, are exploring Generative AI to accelerate software development, but is it a silver bullet?

As language models (LLMs) in enterprise applications continue to grow, ensuring data privacy while maintaining response accuracy becomes crucial. One of the primary methods for protecting sensitive information is data masking. However, this process can lead to significant information loss, potentially rendering responses from LLMs less accurate. How can this loss be measured?