Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

There’s been a lot of excitement around generative AI technology over the past few years, especially in software development. Developers of all levels are turning to AI tools, such as GitHub Copilot, Amazon CodeWhisperer, and OpenAI’s ChatGPT, to support their coding efforts. In fact, GitHub found that 92% of developers use AI coding tools. However, many businesses are realizing that they need to be more cautious when using AI in software development.

Explore Our Latest Insights on Artificial Intelligence (AI). Learn More. It is easy to assume that large language models (LLMs) and generative AI (GenAI) security products are a mysterious box of magic. While, in general, interactions with these models are abstract; you make an API call to a remote endpoint and receive a response without much exposure to the security controls around the model, there are security risks of AI to consider when using them.

The Evolution of Email Threats Email has long been a favored vector for cyber attacks. From the early days of simple phishing scams to the more advanced spear-phishing campaigns, email threats have consistently evolved. However, the integration of AI has brought about a paradigm shift in both the complexity and frequency of these attacks. AI-Powered Phishing Phishing attacks have traditionally relied on mass-distribution strategies, hoping to catch a small percentage of victims.

ChatGPT impresses everyone with its writing capabilities; however, its proficiency in understanding and generating human-like text has inadvertently empowered threat actors to produce realistic and error-free phishing emails, which can be challenging to detect. The use of ChatGPT in cyberattacks poses a significant threat, particularly for attackers whose first language isn’t English. This tool helps them overcome language barriers, enabling the creation of more convincing phishing content.

Have you heard about AI yet? Just kidding. We know you have. Recently, AI’s popularity has skyrocketed among businesses and consumers alike. This surge was driven by a combination of technological advancements (e.g., machine learning, natural language processing, and data analytics) with an increase in tool accessibility and user-friendliness.

AI has undoubtedly revolutionized various industries, enhancing both efficiency and innovation through low-code and no-code platforms. Yet, this ease of development brings with it an increased burden of security. As business users and developers rapidly build applications, automations, and bots using AI, the complexity and volume of these creations amplify potential security vulnerabilities.

With the rapid adoption of AI-enabled services into production applications, it’s important that organizations are able to secure the AI/ML components coming into their software supply chain. The good news is that even if you don’t have a tool specifically for scanning models themselves, you can still apply the same DevSecOps best practices to securing model development.

Cybersecurity has become a battlefield where defenders and attackers engage in a constant struggle, mirroring the dynamics of traditional warfare. In this modern cyber conflict, the emergence of artificial intelligence (AI) has revolutionized the capabilities of traditionally asymmetric cyber attackers and threats, enabling them to pose challenges akin to those posed by near-peer adversaries.