Security | Threat Detection | Cyberattacks | DevSecOps | 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.

Today, we are thrilled to announce the integration of the Coalition for Content Provenance and Authenticity (C2PA) provenance standard into Cloudflare Images. Content creators and publishers can seamlessly preserve the entire provenance chain — from how an image was created and by whom, to every subsequent edit — across the Cloudflare network.

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.

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 landscape of microservices, Kubernetes, and cloud environments, attacks can come from multiple vectors, with varying degrees of complexity. Understanding these vectors and how to detect them is crucial for securing your infrastructure and applications. This post will explore various attack scenarios including SQL Injection and Cluster Takeover, structured around the 4 Cs of cloud security: Cloud, Cluster (Kubernetes), Container (workload), and Code (application).

Large language models transform how people interact with AI technology. Despite impressive capabilities, these systems struggle with consistent LLM accuracy. Users frequently encounter false information, logical errors, and confused responses. Many organizations deploy LLM-powered applications without understanding these limitations. The consequences range from minor inconveniences to major business disasters. Engineers need practical knowledge about accuracy challenges.

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.

As the adoption of large language models (LLMs) continues to surge, ensuring their security has become a top priority for organizations leveraging AI-powered applications. The OWASP LLM Top 10 for 2025 serves as a critical guideline for understanding and mitigating vulnerabilities specific to LLMs. This framework, modeled after the OWASP Top 10 for web security, highlights the most pressing threats associated with LLM-based applications and provides best practices for securing AI-driven systems.