Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In this lightboard session, we break down how organizations classify and protect sensitive data—and why traditional methods fall short. You’ll see how Cyberhaven reimagines data security with lineage-based protection that works across files, systems, and formats. Watch to learn: The difference between data maps, tags, and labels The limitations of legacy classification (persistence & breadth)

What exactly is phishing—and how can you avoid falling for it? In this video, we break down phishing attacks in plain terms, from classic email scams to newer tactics like smishing, vishing, and even quishing (yes, phishing via QR code!). You’ll learn how to spot the warning signs, understand the real-world consequences of falling for a phishing attempt, and discover simple yet powerful ways to protect yourself and your organization.

Insider threats are one of the most underestimated cybersecurity risks facing organizations today—and they’re coming from the inside. In this video, we break down exactly what insider threats are, the different types (including malicious, negligent, and compromised insiders), and why they’re so hard to detect. You’ll also hear about real-world examples like Edward Snowden and the Capital One breach, and learn how businesses can protect themselves with practical, real-world strategies.

What are false positives in cybersecurity — and why do they matter? In this video, we break down the concept of false positives: those annoying alerts that cry wolf when there’s no real threat. You’ll learn how they happen, the difference between false positives and false negatives, and the hidden costs they create for security teams. We’ll also walk through real-world examples, explore how false positives impact SOC efficiency, and share practical strategies to reduce them using better configurations, machine learning, and smarter alert triage.

What is DLP, and why is it critical to modern cybersecurity? In this video, we break down Data Loss Prevention—also known as DLP—into simple terms. You'll learn how DLP works, what kinds of sensitive data it protects, and why organizations use it to prevent data leaks, insider threats, and accidental exposure of confidential information. Whether you're new to data security or looking to level up your InfoSec knowledge, this is the perfect starting point to understand how DLP fits into your broader cybersecurity strategy.

In this candid fireside chat, we’ll explore why legacy DLP is no longer fit for purpose and what a modern, behavior-aware approach looks like. Join us as we unpack the technical and cultural debt holding security teams back, how new paradigms like contextual visibility and real-time decisioning are enabling faster, more effective responses. Matt Webb, Sr. Manager of Information Security at Zebra Technologies, shares his first-hand experience of making the switch to modern DLP with Cyberhaven.

Legacy data loss prevention (DLP) tools were built for a different era—a time when data sat safely behind firewalls and security meant scanning files for keywords. But today, data moves across cloud apps, personal devices, and collaboration tools faster than ever. Legacy DLP simply can’t keep up. In this video, we break down: If your organization is still relying on outdated DLP systems, it’s time to evolve. Because what your tools can’t see will hurt you.

AI use at work has exploded—nearly every employee is experimenting with AI tools. But behind the productivity gains lies a major blind spot: 71% of AI apps in use today were not approved by IT or security teams. These tools are flying under the radar, and they’re sending sensitive company data to unknown third parties. Cyberhaven Labs analyzed AI tool usage across millions of real-world events and found widespread shadow AI, uncontrolled data exposure, and risky behavior by employees—often without realizing it. The implications are clear: you can’t secure what you can’t see.

AI Took Off. We’re Launching the Controls. Discover how Cyberhaven is rewriting the rules of AI data security. Our newest innovation is too big to call a feature — it’s a new frontier. AI changed everything... fast. Productivity soared, but so did risk. Employees embraced AI, and data raced across tools without oversight. The question isn’t if your organization is using AI — it’s how much risk it’s exposing in the process.