The momentous rise of AI continues, and more and more customers are demanding concrete results from these early implementations. The time has come for tech companies to prove what AI can do beyond adding conversational chat agents to website sidebars. Fortunately, it’s easy to see how cloud data protection has already benefited from advancements in AI and ML. Headline-grabbing large-language models are also making protecting data in the cloud easier to manage across organizations. ‍

NOTE: This research was recently presented at Black Hat USA 2024, under the title “From MLOps to MLOops – Exposing the Attack Surface of Machine Learning Platforms”. The JFrog Security Research team recently dedicated its efforts to exploring the various attacks that could be mounted on open source machine learning (MLOps) platforms used inside organizational networks.

We made our WAF Machine Learning models 5.5x faster, reducing execution time by approximately 82%, from 1519 to 275 microseconds! Read on to find out how we achieved this remarkable improvement. WAF Attack Score is Cloudflare's machine learning (ML)-powered layer built on top of our Web Application Firewall (WAF). Its goal is to complement the WAF and detect attack bypasses that we haven't encountered before.

By 2026, more than 80% of enterprises will have used generative artificial intelligence (“GenAI”) APIs, models and/or deployed GenAI-enabled application in production environments. With this fast pace of adoption, it is no wonder that artificial intelligence (AI) application security tools are already in use by 34% of organizations, a number that will no doubt increase.

GitGuardian's Confidence Scorer, a machine-learning model, is being rolled out. Learn how it advances secret detection on GitHub and drives impactful developer alerts.

Time is of the essence when it comes to protecting your data, and often, teams are sifting through hundreds or thousands of alerts to try to pinpoint truly malicious user behavior. Manual triage and response takes up valuable resources, so machine learning can help busy teams prioritize what to tackle first and determine what warrants further investigation.

Top tips is a weekly column where we highlight what’s trending in the tech world today and list ways to explore these trends. This week, we’re looking at four machine learning-related risks to watch out for. Machine learning (ML) is truly mind-blowing tech. The very fact that we’ve been able to develop AI models that are capable of learning and improving over time is remarkable.