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.

What’s happening under the hood of your AI systems? AI is now a crucial element of modern software applications, and if you don’t have visibility into its components, you’ll be left blind. Similar to a Software Bill of Materials (SBOM), an AI Bill of Materials, AI BOM, or AIBOM has become a crucial framework for documenting and securing this new and complex supply chain. This article is part of a series of articles on Shadow AI.

From supporting customers through intelligent chatbots to auto-generating code, GenAI is revolutionizing enterprise operations. But as Spiderman coined, with great power comes great responsibility. This article will look at generative AI security, including trends and best practices to stay on top of.

Imagine a world where, in the middle of programming, your helpful AI assistant tells you to import a package called securehashlib. It sounds real. It looks real. You trust your silicon co-pilot. You run pip install securehashlib. Congratulations. You’ve just opened a backdoor into your software stack—and possibly your company’s infrastructure. The package didn’t exist until yesterday, when an attacker registered it based on a hallucination the AI made last week.

A Software Bill of Materials (SBOM) is like an ingredient list for software. It provides a detailed inventory of all the components that make up an application, including open source libraries, proprietary code, packages, and containers. Just as food packaging lists ingredients to protect consumers and ensure safety, SBOMs do the same for software by giving visibility into what is inside.

Today, we’re thrilled to announce Mend Forge, our new AI native innovation engine and your window into what’s next in application security. At Mend.io, we believe that security innovation shouldn’t happen in a black box. The security landscape is shifting fast, driven by the explosive growth of AI generated code, AI powered applications, and rapidly evolving software supply chains.

As generative AI tools like ChatGPT, Claude, and others become increasingly integrated into enterprise workflows, a new security imperative has emerged: system prompt hardening. A system prompt is a set of instructions given to an AI model that defines its role, behavior, tone, and constraints for a session. It sets the foundation for how the model responds to user input and remains active throughout the conversation.