Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Understanding shadow AI in your endpoint environment

Generative AI–and large language models in particular–reached mass consumer adoption beginning in late 2022 and early 2023, with ChatGPT reaching 100 million users faster than any consumer application in history. Since then, AI has advanced at a breakneck pace and now seems to be incorporated in every tool, app, and website–regardless of how useful it might actually be.

Best Enterprise DLP Tools for AI Data Risk (2026 Comparison)

Employees move sensitive data into AI tools every day. Someone pastes customer records into ChatGPT to draft an email. A developer feeds proprietary source code into a coding assistant to fix a bug. A project manager drops a confidential contract into Gemini to summarize it for a meeting. According to research from Cyberhaven Labs, 39.7% of the data employees share with AI tools is sensitive, and enterprise adoption of endpoint-based AI agents grew 276% in the past year alone.

7 Generative AI Security Risks and How to Defend Your Organization

Generative AI creates new attack surfaces that traditional security tools were not designed to address. The biggest generative AI security risks include prompt injection, data leakage, shadow AI, compliance exposure, model poisoning, insecure RAG pipelines, and broken access control. Each one requires a specific defense, not a generic firewall or DLP rule.

Understanding Cloudflare's network architecture

For decades, enterprise IT relied on a “hub and spoke” security model. But between the explosion of cloud infrastructure, SaaS apps and a remote workforce, that old perimeter hasn't just cracked—it’s shattered. In an attempt to stay on top of the advancing perimeter, many different solutions from many vendors entered the market and created a "spaghetti mess" of point solutions that drive up costs and tank user experience. Cloudflare is an answer to this problem, delivering everything you need to secure your apps, networks, users, data and devices.

"It's Quite a Shock": The Quantum Deadline Is Real

In this World Quantum Day special edition of This Week in NET, host João Tomé is joined by Bas Westerbaan (Principal Research Engineer) and Sharon Goldberg (Senior Director, Product) to explain why the timeline for post-quantum cryptography may be arriving sooner than expected. Recent research suggests the number of qubits required to break today’s encryption could fall dramatically, accelerating the urgency for companies and the Internet ecosystem to migrate to post-quantum security. Google has set a 2029 migration target, and Cloudflare is working toward a similar timeline.

A Look At GitGuardian's ML-Powered Contextual EnrichmentAnd Incident Scoring

In this quick introductory video, Mathieu Bellon, Senior Product Manager at GitGuardian, sits down with Dwayne McDaniel, Developer Advocate, to cover some of the advancements GitGuardian has made by integrating machine learning directly into the secrets security platform. Mathieu describes how engineers and responders can save serious time as by automating contextual analysis, geving the humans in the loop with the best information to be able to take an informed action when it comes to secrets leaks. They also discuss the security implications and where teams can look if they want to opt out or bring their own agents.

Observability and Security for the AI Era

Datadog has always been driven by a broader vision of helping teams understand and operate complex systems. In this session, you’ll hear from Yanbing Li, Chief Product Officer, and Shri Subramanian, Group Product Manager, as they share the latest updates across the Datadog product suite and discuss how that vision continues to shape the platform’s evolution and support the next generation of AI-driven applications.

Secure private networking for everyone: users, nodes, agents, Workers - introducing Cloudflare Mesh

AI agents have changed how teams think about private network access. Your coding agent needs to query a staging database. Your production agent needs to call an internal API. Your personal AI assistant needs to reach a service running on your home network. The clients are no longer just humans or services. They're agents, running autonomously, making requests you didn't explicitly approve, against infrastructure you need to keep secure.