Cloud-Delivered Security Services vs. Cryptomage

Palo Alto Networks Cloud-Delivered Security Services represent an all-encompassing, cloud-native security framework that protects modern networks by integrating best-in-class defenses across all users, devices, applications, and data, no matter their location. At the heart of these services is Precision AI™, which works inline analyzing real-time network traffic to detect and stop threats ranging from phishing and ransomware to advanced command-and-control attacks and zero-day vulnerabilities. The platform includes Advanced Threat Prevention, an industry-leading intrusion prevention system, alongside Advanced WildFire, the largest malware analysis engine capable of stopping even highly evasive malware on first encounter. Its Advanced URL Filtering technology proactively prevents phishing attacks, while Advanced DNS Security offers unparalleled threat coverage—over twice that of competitors—and actively defends against DNS hijacking attacks as they happen. Comprehensive IoT/OT Security implements a zero trust model to safeguard all connected devices within an organization’s infrastructure. NG-CASB provides visibility and governance for SaaS applications, ensuring organizations can control usage and data risks effectively. AI Access Security enables secure, compliant use of generative AI apps with fine-grained access controls and visibility across more than 600 applications. Leveraging the power of Palo Alto Networks’ Unit 42 Threat Research team and the collective intelligence from a vast global customer base, this cloud-delivered solution provides real-time, scalable protection that adapts to today’s rapidly evolving cyber threat landscape. It reduces the risk of “patient zero” infections by stopping threats 180 times faster than other platforms. The service is built to empower organizations to maintain robust security postures while supporting modern cloud and hybrid network environments with agility and precision.

Threat detection involves a meticulous analysis of each network packet and its data, which includes identifying and verifying network protocols to uncover both hidden and obscure ones. It utilizes machine learning methodologies that enable a forward-looking evaluation of traffic risk, employing scoring systems to quantify potential threats. The identification of network steganography plays a crucial role in detecting concealed traffic within the network, potentially revealing data breaches, espionage, and botnet activities. By applying specialized algorithms for detecting steganography, this approach effectively exposes various strategies used to hide information. In addition, a distinctive database filled with a wide range of known steganography techniques significantly boosts detection efficacy. Forensic analysis is employed to assess the ratio of security incidents in relation to their traffic origins, providing valuable insights. This also aids in isolating high-risk network traffic for targeted analysis based on specific threat levels, and storing the metadata from processed traffic in an extended format further streamlines the trend analysis process. Overall, this comprehensive strategy not only deepens the understanding of network security issues but also improves the responsiveness to new and evolving threats, ensuring that organizations remain vigilant and prepared.