Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The rapid rise of generative AI (genAI) applications is reshaping enterprise technology strategies, pushing security leaders to reevaluate risk, compliance, and data governance policies. The latest surge in DeepSeek usage is a wake-up call for CISOs, illustrating how quickly new genAI tools can infiltrate the enterprise. In only 48 hours, Netskope Threat Labs observed a staggering 1,052% increase in DeepSeek usage across our customer base.

Last year, Cyberhaven introduced a revolutionary solution that changed how companies protect their most valuable data: Linea AI. Today, we’re excited to share its progress, success stories, and the advancements shaping its future.

DeepSeek is a Hangzhou-based startup founded in December 2023 by Liang Wenfeng. It released its first AI-based large language model in 2024. The company recently received widespread attention after releasing a new open-source AI model that rivals OpenAI’s work. The app shot to the top of the app stores’ downloads list and has sparked much interest.

In today’s AI-driven world, where data powers everything from personalized recommendations to advanced business analytics, safeguarding sensitive information is more critical than ever. As data breaches and regulatory requirements grow more complex, organizations face mounting pressure to protect personal and confidential information with a data privacy vault that ensures security and compliance.

A quantitative approach to prompt tuning and LLM evaluation Elastic has long been developing machine learning (ML) and AI-powered security detections. We constantly bring in new technologies when available to help make our users’ lives easier. So, with the rise of generative AI (GenAI), we have developed even more Elastic Security features to use this powerful, new technology. Among those are.

Imagine a sleek, high-tech sports car racing downhill without brakes. Now, imagine that car is actually the AI driving your business. Powerful yet precariously close to catastrophe. That’s why, as we accelerate AI adoption, including AI agents, we can’t afford to overlook security guardrails. This fact was front and center during the recent “large-scale cyberattack” on DeepSeek, a strategic open-source AI player from China that’s been disrupting the global AI space.

As large language models (LLMs) continue to push the boundaries of natural language processing, their widespread adoption across industries has highlighted the critical need for robust LLM security solutions. These powerful AI systems, while immensely beneficial, are vulnerable to emerging threats such as data leakage, prompt injection attacks, and compliance risks. In 2025, the landscape of LLM security tools has evolved to address these unique challenges, ensuring their safe and responsible deployment.

The initial excitement of deploying a first large language model application often wears off quickly when the first bill arrives. Many newcomers face sticker shock when they see how quickly LLM costs can escalate. Money matters in AI projects. Most teams discover this truth the hard way. The difference between success and failure often comes down to financial planning. Organizations rushing to implement AI solutions frequently overlook the financial aspects.