Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Imagine accessing a giant repository of knowledge, extracting the most relevant information in response to your specific needs, and then using that information to generate intelligent, factual responses - that's the power of Retrieval-Augmented Generation (RAG). This innovative technology is taking the world of Artificial Intelligence (AI) by storm, and for good reason. Let's delve into what RAG is, why it counts, and how it can transform your organization.

Did you know that GitHub Copilot may suggest insecure code if your existing codebase contains security issues? On the other hand, if your codebase is already highly secure, Copilot is less likely to generate code with security issues. AI coding assistants can suggest insecure code due to their limited understanding of your specific codebase. They imitate learned patterns or utilize available context without providing judgment.

Let’s dive into a world where data isn’t just growing — it’s exploding — setting the stage for an AI-driven future that’s as exciting as the next sci-fi blockbuster. With innovations like ChatGPT and Neuralink pushing the boundaries of what’s possible, we’re on the precipice of a data revolution.

AI is among the most disruptive technologies of our time. While AI/ML has been around for decades, it has become a hot topic with continued innovations in generative AI (GenAI) from start-up OpenAI to tech giants like Microsoft, Google, and Meta.

With the idea in mind to “audio-jack” a live call-based banking transaction, security researchers were successful in inserting cybercriminal-controlled account details. Deepfake audio is nothing new… but it is getting very advanced. So much so, that security researchers at IBM Threat Intelligence were able to test out a hypothesis as to whether it’s possible to perform an audio-based “Man in the Middle” attack.