Palo Alto Networks Prevents Data Loss at Enterprise Scale with NVIDIA

With NVIDIA accelerated computing and AI software, cybersecurity leaders like Palo Alto Networks can safeguard vast amounts of sensitive information with unprecedented speed and accuracy, ushering in a new era of AI-driven data protection. The integration of NVIDIA Triton Inference Server and GPU technology into Palo Alto Networks Data Security marks a significant advancement in our ability to handle complex data security challenges. That’s why Palo Alto Networks is using NVIDIA’s groundbreaking GPU and Triton technology to improve the efficiency and accuracy of our DLP machine learning models, leading to faster response times and better customer outcomes. The modern landscape is marked by an explosion of new data types, formats and sources, alongside an unprecedented volume of sensitive information, all of which pose significant challenges to effective data security. Contact your Palo Alto Networks representative to explore how our integrated data security solution can empower your business to thrive in today’s dynamic digital landscape. Palo Alto Networks Data Security addresses the issue of false positives that have often plagued data security administrators. The data demonstrates a substantial reduction in response time with GPU and Triton-based instances, showcasing the efficiency gains achieved through advanced GPU hosting and optimization with Triton. At Palo Alto Networks, we understand that traditional data loss prevention (DLP) alone is unable to keep up with the demands of today’s modern connected enterprise. Regular expression patterns and keyword-based methods are inherently prone to generating false positives, which can overwhelm security teams and dilute the effectiveness of data protection efforts. As we continue to expand our capabilities, the use of NVIDIA AI and accelerated computing will play a pivotal role in driving the future of AI-powered data protection, ensuring our solutions remain at the cutting edge of innovation. The performance and cost-effectiveness of NVIDIA’s full-stack accelerated computing provides the computational power and AI software deployment tools needed to secure vast amounts of data effectively. By leveraging the exceptional computational power of NVIDIA accelerated computing, we have dramatically improved the efficiency and accuracy of our ML models, leading to faster response times and a substantial reduction in operational costs. To mitigate this, we leverage LLM-powered ML models to provide context of the detected sensitive data and establish ground truth. The ability to generate synthetic data has proven to be highly effective in training our AI/ML detection models. Data sprawl can encompass a vast array of formats, including image files, documents with intricate layouts, proprietary datasets and dynamic web content. Sensitive data embedded within visual formats, such as scanned documents or images, can prove evasive due to the inherent complexities of visual data processing. Today’s enterprises face increased challenges in securing their data, largely driven by the massive volume and complexity of diverse data formats. One of the most daunting challenges in modern data protection is detecting sensitive data within complex data types. Our GPU deployment models have been aided by NVIDIA Triton Inference Server, part of NVIDIA AI Enterprise, providing a dynamic interface and allowing for the hosting of various kinds of models. As enterprises harness the power of generative AI, the need for robust data security scales exponentially. Artificial intelligence (AI) not only transforms workforce productivity, but it also plays a critical role in powering data security, particularly with classification. Traditional techniques frequently misidentify benign data as sensitive, resulting in unnecessary alerts and wasted resources. Sensitive data detection with and without LLM-powered detections. This powers our AI models for enhanced data discovery and classification for superior customer outcomes.

This Cyber News was published on www.paloaltonetworks.com. Publication date: Tue, 01 Oct 2024 22:13:06 +0000


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