Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A blanket “no AI” policy is not the answer for most organizations. Prohibiting something people find genuinely useful just drives it further underground.

Most security teams are being asked to "enable AI" before they have any real sense of which tools are safe to use. That gap is costing them. Cyberhaven's research found that the majority of AI tools in active enterprise use today fall into high or critical risk categories, and more than 80% of enterprise data flowing into AI is going to those risky tools, not to platforms built with serious security in mind. To help security teams cut through the noise, we built the Cyberhaven AI App Risk Checker.

Today, we’re launching System Prompt Hardening, Mend.io’s new capability that defends the hidden instructions that control how your AI systems behave. Unlike user-facing prompts, system prompts live behind the scenes, and when attackers manipulate them, the result can be data leaks, policy bypasses, or unsafe model behavior. System prompt hardening stops those attacks at the source and gives security, engineering, and risk teams a practical, auditable way to secure AI in production.

When cybersecurity teams talk about risk, they usually speak in technical terms like vulnerabilities, exploits, and attack vectors. But when they walk into the boardroom, they need to speak a different language. They need to speak about cost. In the era of AI, the cost of insecure APIs has shifted from a potential liability to a tangible line item on the balance sheet. It is no longer just about the cost of a data breach.

Long-time followers of mine know that I am not an AI hype person. Some people might even call me an AI critic. I prefer to call myself an AI realist. I do not think AI will kill us all (despite our best efforts to bypass all guardrails and common sense). I do not think AI will replace all jobs. I do not think AI will replace all cybersecurity jobs. But I do think AI allows improvements in many areas, including cyber defenses, over traditional tools and techniques.

Production deployments. That’s where multi-agent AI systems live now, not research labs. Salesforce, Microsoft, and Cognition Labs are all running agent pipelines that replaced what used to take entire ops teams. Most businesses still don’t fully understand what they’ve switched on. A multi-agent AI setup isn’t just one model doing more things.

In December 2025, Cloudflare received reports of HTTP/1.x request smuggling vulnerabilities in the Pingora open source framework when Pingora is used to build an ingress proxy. Today we are discussing how these vulnerabilities work and how we patched them in Pingora 0.8.0. The vulnerabilities are CVE-2026-2833, CVE-2026-2835, and CVE-2026-2836. These issues were responsibly reported to us by Rajat Raghav (xclow3n) through our Bug Bounty Program.

Security is traditionally a game of defense. You build walls, set up gates, and write rules to block traffic that looks suspicious. For years, Cloudflare has been a leader in this space: our Application Security platform is designed to catch attacks in flight, dropping malicious requests at the edge before they ever reach your origin. But for API security, defensive posturing isn’t enough. That’s why today, we are launching the beta of Cloudflare’s Web and API Vulnerability Scanner.

For years, the cybersecurity industry has accepted a grim reality: migrating to a zero trust architecture is a marathon of misery. CIOs have been conditioned to expect multi-year deployment timelines, characterized by turning screws, manual configurations, and the relentless care and feeding of legacy SASE vendors. But at Cloudflare, we believe that kind of complexity is a choice, not a requirement. Today, we are highlighting how our partners are proving that what used to take years now takes weeks.