Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The GDPR sets out clear principles for how personal data should be handled. Here are some that AI tools can easily clash with.

Many tech professionals see integrating large language models (LLMs) as a simple process -just connect an API and let it run. At Wallarm, our experience has proved otherwise. Through rigorous testing and iteration, our engineering team uncovered several critical insights about deploying LLMs securely and effectively. This blog shares our journey of integrating cutting-edge AI into a security product.

AI security is where traditional cybersecurity meets the chaotic brilliance of machine learning. It’s the discipline focused on protecting AI systems—not just the code, but the training data, model logic, and output—from manipulation, theft, and misuse. Because these systems learn from data, not just logic, they open up fresh attack surfaces like data poisoning, model inversion, and prompt injection.

The dictionary (at least the online version) defines the word trend as a general direction in which something is developing or changing. It also does have a secondary definition relating to fashion, but unless we want to cover off on some questionable choices I made when younger, we can skip that one. When does something move beyond being a trend, and create a lasting shift in behavior–or for the purposes of our discussion, technology?

In mid-May 2025, Cloudflare blocked the largest DDoS attack ever recorded: a staggering 7.3 terabits per second (Tbps). This comes shortly after the publication of our DDoS threat report for 2025 Q1 on April 27, 2025, where we highlighted attacks reaching 6.5 Tbps and 4.8 billion packets per second (pps). The 7.3 Tbps attack is 12% larger than our previous record and 1 Tbps greater than a recent attack reported by cyber security reporter Brian Krebs at KrebsOnSecurity.

For decades, the United States National Institute of Standards and Technology (NIST) has been guiding industry efforts through the many publications in its Computer Security Resource Center. NIST has played an especially important role in the adoption of Zero Trust architecture, through its series of publications that began with NIST SP 800-207: Zero Trust Architecture, released in 2020.

Over the past few years, enterprises have rapidly integrated GenAI into an increasing number of workflows and use cases. Amidst the rush and excitement to adopt a free tool that significantly boosts productivity, business leaders de-prioritized privacy, till it became a compliance issue. As privacy tools offering a quick patch quickly flooded the market, businesses ran into a new problem – which is the best tool?