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.

Exposure management is a proactive cybersecurity strategy that continuously identifies, prioritizes, and mitigates risks across an organization’s entire attack surface.

Organizations today face an unprecedented challenge: their most valuable assets can disappear in a matter of milliseconds through accidental sharing, malicious theft, or simple human error. Data Loss Prevention is a strategic approach to safeguarding information before it crosses organizational boundaries, acting as both a guardian and a gatekeeper for critical business assets.

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.

Let’s catch up on the more interesting vulnerability disclosures and cyber security news gathered from articles across the web this week. This is what we have been reading about on our coffee break! Have some people not learnt hardcoded passwords are bad?

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We’ve been busy! Our latest summer updates introduce powerful new capabilities to help you detect, respond to, and manage threats across your cloud-native workloads, and cloud environments – all from one unified platform. Here’s what’s new in ARMO Platform.

Interested in learning more about cryptography but don’t know where to start? We break down the common cryptography terms in a simple and clear way, to help you understand how companies encrypt your data in a safe and secure way. From there, you can dive deeper into the world of cryptography and learn more about this important and fascinating concept.

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.