Buzz around generative AI security is at an all-time high, but most security pros are still in “listen and learn” mode when it comes to securing generative AI in their applications. So when is it time to get serious about securing generative AI in your applications, and what steps should you take? This post outlines a Crawl - Walk - Run approach for product and application security pros when it comes to securing generative AI-enabled applications.

In a recent interview with the NYSE at the RSA Conference 2024, Traceable co-founder and CTO Sanjay Nagaraj provided a deep dive into the factors fueling the company's remarkable success since its launch in 2018.

The rise of generative AI brings new security challenges. Learn how API security can mitigate risks like prompt injection, sensitive data disclosure, and insecure output handling in your LLM-powered applications.

APIs are evolving, and so is API security. Context-aware security analyzes user behavior, data sensitivity, and environmental factors for real-time threat responses. Discover why it's the future of API protection in this comprehensive blog post.

Data poisoning is a growing threat to LLMs. Learn how attackers exploit API weaknesses to corrupt AI data. This blog covers types of attacks, their impact, and essential API security measures to protect the integrity of your AI models.

Explore the pivotal role of APIs in the execution of President Biden's Executive Order on AI, which aims to position the United States at the forefront of artificial intelligence innovation. Understand how APIs enhance AI interoperability, scalability, and accessibility, ensuring secure and efficient deployment across various sectors. Dive into the nuances of the Order's strategy for a trustworthy AI ecosystem that aligns with American values and interests.

In today's digital landscape, the transformative power of artificial intelligence (AI) cannot be overstated. Enterprises across various industries are already exploring ways to leverage AI to improve productivity and enhance customer experiences. One area that has witnessed significant advancements is the usage of Generative AI and Large Language Models (LLMs). However, as cybersecurity professionals, it is crucial to understand the potential security pitfalls associated with their implementation. In this blog, we explore how generative AI and LLMs can be utilized in enterprise software, and discuss effective strategies to guard against the inherent risks.

As we venture further into the age of artificial intelligence, the phrase "you can't put the genie back in the bottle" takes on profound significance. It describes a phenomenon that has become all too familiar with generative AI — once these systems learn and generate based on specific data sets, it's almost impossible to unlearn or retrieve that information. This means that if personal or sensitive data is accidentally fed into these systems, the potential for misuse or exposure becomes a looming threat that is virtually impossible to mitigate after the fact.

ChatGPT and other generative AI tools will transform business as we know it. However, enterprise teams need to move now to protect their sensitive data from being exposed, as it traverses APIs to answer user queries.