Finding deeply hidden and unexpected vulnerabilities early in the development process is key. However, time to invest in proactive tests is limited. Prioritizing speed over security is common. Our new AI-assistant CI Spark closes this gap and enables both speed and security. CI Spark makes use of LLMs to automatically identify attack surfaces and to suggest test code. Tests generated by CI Spark work like a unit test that automatically generates thousands of test cases.

Imagine an AI overlord sitting in a dark basement, plotting world domination through cybercrime. While the idea might seem like a sci-fi flick, it’s actually closer to reality than we think. AI has emerged as a game changer in a constantly evolving cyber landscape. AI algorithms can learn and adapt to security measures quickly, making them the ultimate cyber villains.

Phishing emails are fraudulent or malicious emails that are designed to deceive recipients and trick them into revealing sensitive information, such as login credentials, financial details, or personal data. Phishing email contents usually employ various social engineering techniques that are likely to manipulate recipients, leading to significant damage to personal or corporate information security.

Welcome to our cheat sheet covering the OWASP Top 10 for LLMs. If you haven’t heard of the OWASP Top 10 before, it’s probably most well known for its web application security edition. The OWASP Top 10 is a widely recognized and influential document published by OWASP focused on improving the security of software and web applications. OWASP has created other top 10 lists (Snyk has some too, as well as a hands-on learning path), most notably for web applications.

The widespread use of Artificial intelligence (AI) and machine learning (ML) introduce their own security challenges; an AI/ML security assessment can help. AI and ML provide many benefits to modern organizations; however, with their widespread use come significant security challenges. This article explores the vital role of AI/ML security assessments in unearthing potential vulnerabilities, from lax data protection measures to weak access controls and more.

AI is one of the hottest topics in tech right now. More than half of consumers have already tried generative AI tools like ChatGPT or DALL-E. According to a Gartner poll, 70% of executives say their business is investigating and exploring how they can use generative AI, while 19% are in pilot or production mode. Business use cases for AI range from enhancing the customer experience (38%), revenue growth (26%), and cost optimization (17%).

Can businesses stay compliant with security regulations while using generative AI? It’s an important question to consider as more businesses begin implementing this technology. What security risks are associated with generative AI? It's important to earn how businesses can navigate these risks to comply with cybersecurity regulations.

Just last week the UK’s NCSC issued a warning, stating that it sees alarming potential for so-called prompt injection attacks, driven by the large language models that power AI. The NSCS stated “Amongst the understandable excitement around LLMs, the global tech community still doesn‘t yet fully understand LLM’s capabilities, weaknesses, and (crucially) vulnerabilities.

AI has already revolutionized the way we work. ChatGPT, GitHub Copilot, and Zendesk AI are just a few of the tools that are taking over day-to-day tasks like generating customer support emails, de-bugging code, and much, much more. Yet despite all of these advancements, security teams are under more intense pressure than ever to mitigate rapidly evolving risks. Paired with a growing shortage of over 3.4 million cybersecurity workers, security teams are in need of a solution—and fast.