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Data is the lifeblood of productivity, and protecting sensitive data is more critical than ever. With cyber threats evolving rapidly and data privacy regulations tightening, organizations must stay vigilant and proactive to safeguard their most valuable assets. But how do you build an effective data protection framework? In this article, we'll explore data protection best practices from meeting compliance requirements to streamlining day-to-day operations. Whether you're securing a small business or a large enterprise, these top strategies will help you build a strong defense against breaches and keep your sensitive data safe. 1. Define your data goals When tackling any data protection project, the first step is always to understand the outcome you want. First, understand what data you need to protect. Identify your crown jewel data, and where you THINK it lives. (It's probably more distributed than you expect, but this is a key step to help you define your protection ...

Companies are engaged in a seemingly endless cat-and-mouse game when it comes to cybersecurity and cyber threats. As organizations put up one defensive block after another, malicious actors kick their game up a notch to get around those blocks. Part of the challenge is to coordinate the defensive abilities of disparate security tools, even as organizations have limited resources and a dearth of skilled cybersecurity experts. XDR, or Extended Detection and Response, addresses this challenge. XDR platforms correlate indicators from across security domains to detect threats and then provide the tools to remediate incidents.  While XDR has many benefits, legacy approaches have been hampered by the lack of good-quality data. You might end up having a very good view of a threat from events generated by your EPP/EDR system but lack events about the network perspective (or vice versa). XDR products will import data from third-party sensors, but data comes in different formats. The XDR p...

In today's rapidly evolving cyber threat landscape, organizations face increasingly sophisticated attacks targeting their applications. Understanding these threats and the technologies designed to combat them is crucial. This article delves into the mechanics of a common application attack, using the infamous Log4Shell vulnerability as an example, and demonstrates how Application Detection and Response (ADR) technology effectively safeguards against such zero-day threats. View the Contrast ADR white paper The anatomy of a modern application attack: Log4Shell To illustrate the complexity and severity of modern application attacks, let's examine an attack against the infamous Log4Shell vulnerability ( CVE-2021-44228 ) that sent shockwaves through the cybersecurity world in late 2021. This attack is a prime example of attack chaining, leveraging JNDI Injection, Expression Language (EL) Injection and Command Injection. Technology note : The CVE program catalogs, which publicly...

In January 2025, cybersecurity experts at Wiz Research found that Chinese AI specialist DeepSeek had suffered a data leak, putting more than 1 million sensitive log streams at risk. According to the Wiz Research team, they identified a publicly accessible ClickHouse database belonging to DeepSeek. This allowed "full control over database operations, including the ability to access internal data", Wiz Research stated, with more than a million lines of log streams involved, containing chat history, secret keys and more. Wiz immediately reported the issue to DeepSeek, which quickly secured the exposure. Still, the incident underscored the danger of data leakage. Intentional or unintentional? Data leakage is a broad concept, covering a range of scenarios. As IBM notes, the term in general refers to a scenario where "sensitive information is unintentionally exposed to unauthorized parties" .  It could be intentional or unintentional. On the intentional side...

There's been a great deal of AI hype recently, but that doesn't mean the robots are here to replace us. This article sets the record straight and explains how businesses should approach AI. From musing about self-driving cars to fearing AI bots that could destroy the world, there has been a great deal of AI hype in the past few years. AI has captured our imaginations, dreams, and occasionally, our nightmares. However, the reality is that AI is currently much less advanced than we anticipated it would be by now. Autonomous cars, for example, often considered the poster child of AI's limitless future, represent a narrow use case and are not yet a common application across all transportation sectors. In this article, we de-hype AI, provide tools for businesses approaching AI and share information to help stakeholders educate themselves.  AI Terminology De-Hyped AI vs. ML AI (Artificial Intelligence) and ML (Machine Learning) are terms that are often used interchangeably, but the...