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July 17, 2025<h3>Webinar Alert: Mastering Web Exposure with Vulnerability Management 2.0</h3> May 17, 2024Siemens Desigo Fire Safety UL and Cerberus PRO UL Fire Protection Systems December 1, 2023Cybersecurity Inspection Analyst March 16, 2025Cybersecurity Engineer January 1, 2022Hacker Theme for Windows 10/11 Free Download (2022 Latest) August 25, 2025<h3>The Future of Generative AI: Beyond the Cloud</h3><br /> <br /> Generative AI has rapidly become a cornerstone of technological innovation, driving advancements across various fields. However, as the technology continues to evolve, its future may not be confined to the cloud. The growing demand for faster processing, real-time applications, and enhanced privacy is prompting a shift towards decentralized computing solutions. This transition could redefine how we harness the power of generative AI, moving beyond the traditional cloud infrastructure.<br /> <br /> One of the primary reasons for this shift is the need for speed and efficiency. Cloud-based solutions often struggle with latency issues due to data transfer times between local devices and distant servers. By moving AI processing closer to the source, whether through edge computing or on-device AI, we can significantly reduce these delays. This is particularly crucial for applications like autonomous vehicles or real-time language translation, where every millisecond counts.<br /> <br /> Privacy concerns also play a pivotal role in this transition. As generative AI systems become more integrated into our daily lives, the amount of sensitive data they handle continues to increase. Decentralizing AI processing helps mitigate privacy risks by keeping data localized, reducing the need to transmit sensitive information across networks. This not only enhances data security but also aligns with growing regulatory demands for data protection.<br /> <br /> Moreover, decentralization can lead to more sustainable AI practices. Cloud computing centers consume vast amounts of energy, contributing to their significant carbon footprint. By distributing processing tasks across various devices and edge servers, we can achieve energy efficiency and reduce the environmental impact of AI operations. This approach aligns with the global push towards greener technology solutions.<br /> <br /> In conclusion, while the cloud has been instrumental in the rise of generative AI, its future may lie elsewhere. By embracing decentralized approaches, we can enhance performance, safeguard privacy, and foster sustainability. As we continue to explore the potential of generative AI, it is essential to consider these alternative pathways that promise a more efficient and responsible technological future. July 22, 2025Microsoft Identifies Three Chinese Hacker Groups behind Ongoing SharePoint Exploits October 7, 2025Cybersecurity Architect / Engineer September 24, 2023Cybersecurity Technical Services Lead April 9, 2025<h3>Timeless Principles for Teaching Cybersecurity</h3>
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  Industry News  GitLab Enhances SAST Capabilities in DevSecOps Workflow with Acquisition of Oxeye
Industry News

GitLab Enhances SAST Capabilities in DevSecOps Workflow with Acquisition of Oxeye

SecuredyouadmSecuredyouadm—March 22, 20240
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GitLab has acquired the static application security testing startup Oxeye, led by an Imperva and Check Point veteran, to improve application-layer risk detection and reduce false positives. The technology will allow for static application security testing across the software development life cycle, improving detection and vulnerability management. The acquisition, which cost between $30 million and $40 million, will integrate Oxeye’s capabilities into GitLab’s SAST product to enhance security scanning for Python, Go, Java, and JavaScript.

Oxeye’s distinctive approach to identifying and resolving application-layer risks, tracing vulnerabilities from code to cloud, sets it apart from competitors. The integration of Oxeye’s capabilities will result in a more accurate and actionable list of security findings, with fewer false positives and more true positive detections. Existing customers of both GitLab and Oxeye will benefit from these improvements, as well as enhanced security and compliance capabilities.

The acquisition demonstrates GitLab’s commitment to helping customers build more secure software efficiently. Oxeye will complement GitLab’s existing capabilities in dynamic application security testing, fuzz testing, container scanning, and dependency scanning. The goal is to help users deliver secure applications and find and correct security vulnerabilities in their software sooner.

The combined company will focus on advancing security and compliance capabilities, reinforcing GitLab’s position in the application security testing market. The acquisition will help customers build secure software faster and eliminate inefficiencies in the software development process. GitLab was recognized as a contender in last year’s static application security testing Forrester Wave and as a challenger in software composition analysis and application security testing Gartner Magic Quadrant.

Overall, the acquisition of Oxeye by GitLab promises enhanced application security, reduced false positives, and improved security scanning for various programming languages. The integration of Oxeye’s capabilities will benefit existing customers and help GitLab strengthen its position in the application security testing market, ultimately helping users deliver secure software more efficiently.

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