Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Over 11 million phishing attacks have been reported to the UK’s Suspicious Email Reporting Service (SERS) over the past year, according to new data from Action Fraud. The UK’s National Cyber Security Centre has also taken down more than 329,000 phishing sites since the SERS program started in 2020.

A crafty group of cybercriminals has been relentlessly pursuing Mexican banks, cryptocurrency platforms and other organizations in an extended campaign stretching back over two years. Their weapon of choice? A heavily customized version of the AllaKore remote access trojan (RAT). These threat actors are ruthlessly targeting any large Mexican enterprise they can get their hands on. With a sweet spot for companies pulling in over $100 million in annual revenue, they're not messing around with small fry.

In this episode of Trust Issues, Daniel Schwartzer, CyberArk’s Chief Product Technologist and leader of the company’s Artificial Intelligence (AI) Center of Excellence, joins host David Puner for a conversation that explores AI’s transformative impact on identity and access management (IAM). Schwartzer discusses how CyberArk’s AI Center of Excellence is equipping the R&D team to innovate continuously and stay ahead of AI-enabled threats.

Like everything we do, our devices have become a valuable asset in managing our lives. One of the most important things we all have to manage is our finances, and mobile banking has become the new norm for many of us to: The number of people relying on online banking is steadily increasing. By 2025, the number of people managing their finances is projected to reach 217 million. For this reason, mobile banking security is crucial to prevent cyber criminals from emptying your accounts.

A few weeks ago, my wife asked me why stopping threat actors from impacting our lives is so difficult. In this digital age, the necessity to connect online brings inherent exposure to vulnerabilities. The challenge for you as a security leader lies in reducing the sense of vulnerability by building trust. You need to protect your organization and reassure employees so they can perform their jobs without fear.

According to IBM & Ponemon, the average cost of a data breach is a staggering $4.35 million! No wonder companies feel the need to invest heavily in cybersecurity. For legal tech companies, handling a large amount of sensitive client data daily, the stakes are even higher. Beyond the immediate financial impact, a data breach can cause severe reputational damage that is often much harder to repair, making cybersecurity a top priority for legal professionals.

Welcome to the latest edition of our CloudCasa release notes. As we celebrate the 10th anniversary of Kubernetes, we continue to build on the momentum of our substantial updates focused on Kubernetes backup, migration and replication, we’re excited to introduce another set of robust features and enhancements designed to elevate your data protection and management capabilities.

The concept of Secure Access Service Edge (SASE) promised a revolution in network security. Its core intent: to simplify the deployment and consumption of both networking and security functions. However, does the reality really live up to the initial hype?

Zero Trust maturity might be one of the least understood security buzzwords of our era. The term “Zero Trust” was originally coined over a decade ago and described the principle of not assigning digital trust to any entity, ever, for free. It represented a fundamental paradigm shift from the trust-happy early internet days to the threat-filled cyber landscape we now know. Since then, companies have been striving to make all their systems Zero-Trust mature. As right they should.

Artificial Intelligence (AI) is now integrated into almost every available technology. It powers numerous real-world applications, from facial recognition to language translators and virtual assistants. AI offers significant benefits for businesses and economies by boosting productivity and creativity. However, it still faces practical challenges. Machine learning often requires substantial human effort to label training data for supervised learning.