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Every week seems to bring news of another data breach, and it's no surprise why: securing sensitive data has become harder than ever. And it's not just because companies are dealing with orders of magnitude more data. Data flows and user roles are constantly shifting, and data is stored across multiple technologies and cloud environments. Not to mention, compliance requirements are only getting stricter and more elaborate.  The problem is that while the data landscape has evolved rapidly, the usual strategies for securing that data are stuck in the past. Gone are the days when data lived in predictable places, with access controlled by a chosen few. Today, practically every department in the business needs to use customer data, and AI adoption means huge datasets, and a constant flux of permissions, use cases, and tools. Security teams are struggling to implement effective strategies for securing sensitive data, and a new crop of tools, called data security platforms, have appear...

Organizations today face relentless cyber attacks, with high-profile breaches hitting the headlines almost daily. Reflecting on a long journey in the security field, it's clear this isn't just a human problem—it's a math problem. There are simply too many threats and security tasks for any SOC to manually handle in a reasonable timeframe. Yet, there is a solution. Many refer to it as SOC 3.0—an AI-augmented environment that finally lets analysts do more with less and shifts security operations from a reactive posture to a proactive force. The transformative power of SOC 3.0 will be detailed later in this article, showcasing how artificial intelligence can dramatically reduce workload and risk, delivering world-class security operations that every CISO dreams of. However, to appreciate this leap forward, it's important to understand how the SOC evolved over time and why the steps leading up to 3.0 set the stage for a new era of security operations. A brief history of the SOC For deca...

When SaaS applications started growing in popularity, it was unclear who was responsible for securing the data. Today, most security and IT teams understand the shared responsibility model, in which the SaaS vendor is responsible for securing the application, while the organization is responsible for securing their data.  What's far murkier, however, is where the data responsibility lies on the organization's side. For large organizations, this is a particularly challenging question. They store terabytes of customer data, employee data, financial data, strategic data, and other sensitive data records online.  SaaS data breaches and SaaS ransomware attacks can lead to the loss or public exposure of that data. Depending on the industry, some businesses could face stiff regulatory penalties for data breaches on top of the negative PR and loss of faith these breaches bring with them.  Finding the right security model is the first step before deploying any type of SSPM or ...

AI holds the promise to revolutionize all sectors of enterpriseーfrom fraud detection and content personalization to customer service and security operations. Yet, despite its potential, implementation often stalls behind a wall of security, legal, and compliance hurdles. Imagine this all-too-familiar scenario : A CISO wants to deploy an AI-driven SOC to handle the overwhelming volume of security alerts and potential attacks. Before the project can begin, it must pass through layers of GRC (governance, risk, and compliance) approval, legal reviews, and funding hurdles. This gridlock delays innovation, leaving organizations without the benefits of an AI-powered SOC while cybercriminals keep advancing. Let's break down why AI adoption faces such resistance, distinguish genuine risks from bureaucratic obstacles, and explore practical collaboration strategies between vendors, C-suite, and GRC teams. We'll also provide tips from CISOs who have dealt with these issues extensively as w...

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...

Every week, someone somewhere slips up—and threat actors slip in. A misconfigured setting, an overlooked vulnerability, or a too-convenient cloud tool becomes the perfect entry point. But what happens when the hunters become the hunted? Or when old malware resurfaces with new tricks? Step behind the curtain with us this week as we explore breaches born from routine oversights—and the unexpected cracks they reveal in systems we trust. ⚡ Threat of the Week Google Patches Actively Exploited Chrome 0-Day — Google has addressed a high-severity security flaw in its Chrome browser for Windows that has been exploited by unknown actors as part of a sophisticated attack aimed at Russian entities. The flaw, CVE-2025-2783 (CVSS score: 8.3), is said to have been combined with another exploit to break out of the browser's sandbox and achieve remote code execution. The attacks involved distributing specially crafted links via phishing emails that, when clicked and launched using Chrome, trig...