Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As companies embed AI models into their applications, they face risks that traditional security tools weren’t designed to catch, such as prompt injection, data leakage, model poisoning, and shadow AI. Addressing these threats requires a new class of security tools built specifically for AI specific risk.

Bias in generative AI refers to the systematic errors or distortions in the information produced by generative AI models, which can lead to unfair or discriminatory outcomes. These models, trained on vast datasets from the internet, often inherit and amplify the biases present in the data, mirroring societal prejudices and inequities.

Over the past few years, generative AI has moved from research labs into mainstream industries, reshaping how people interact with technology. Advances in deep learning, especially transformer models, allowed systems like ChatGPT and Stable Diffusion to generate human-like text and realistic images. These breakthroughs sparked widespread interest because they showed that AI could create content, making it useful in writing, design, and coding.

An SBOM (Software Bill of Materials) is a structured list of components, including third-party and open-source software, that make up a software application. It’s a detailed inventory of everything that goes into a software product, similar to a list of ingredients for food. SBOMs are crucial for improving software security by providing transparency and enabling organizations to identify and address potential vulnerabilities and risks within their software supply chains.