Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI guardrails are structured safeguards, whether technical, security or ethical, which are designed to guide AI systems so they operate safely, responsibly, and within intended boundaries. Much like highway guardrails that prevent vehicles from veering off course, these measures ensure AI remains aligned with organizational policies, regulations, and ethical values.

Global Artificial Intelligence (AI) infrastructure spending is projected to surpass $200 billion by 2028, according to research from the International Data Corporation (IDC). As organizations rapidly deploy more complex AI systems, the demand for high-performance infrastructure, like Graphics Processing Units (GPUs) and AI accelerators, is surging. This growth exponentially increases computing power, energy consumption and data exchange across hybrid and cloud environments.

Explore enhanced secrets security across code, collaboration tools, & public repos. Discover new Agentic AI protection, NHI lifecycle automation, and smarter incident management.

Fake apps are the latest evolution of brand impersonation, and they’re proving just as dangerous as phishing sites. Fraudsters clone legitimate mobile apps, publish them on official app stores, and trick users into entering credentials — which are then reused in the real app before anyone notices. Given that over 60% of web traffic is now mobile, this form of phishing-driven credential reuse has become one of the top blind spots in mobile fraud defense.

This week marks a milestone in the evolution of mobile endpoint security. At a time when attackers are moving faster and targeting smarter, Lookout is proud to unveil a breakthrough initiative: AI-powered social engineering protection—the first solution of its kind built to detect and disrupt human-targeted attacks at the mobile edge.

Enterprise security teams commonly focus on controlling AI agent conversations through prompt filters and testing edge cases to prevent unauthorized information access. While these measures matter, they miss the bigger picture: the real challenge is granting AI agents necessary permissions while minimizing risk exposure. This isn’t a new problem—it’s the same fundamental challenge we’ve faced with human users for years.