Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The shift to cloud-native infra has broken the traditional perimeter security model. Modern cloud environments are dynamic, heavily distributed, and identity-driven, creating security challenges that conventional security tools were never built to address. Traditional SIEM and vulnerability management tools lack native capabilities to detect issues in IAM policies, S3 bucket ACLs, or the blast radius of a misconfigured Kubernetes node pool.

NIST AI RMF and ISO/IEC 42001 answer different questions, so the choice is rarely about which one is better. One gives you a risk process your engineering teams can run. The other gives you a management system an auditor can certify. Organizations that treat them as rival options usually pick the wrong one for the problem in front of them. ‍

Directors ask for AI risk reporting because oversight failure is personally actionable. Under the Caremark line of cases, a board that cannot demonstrate it monitored a material risk carries exposure of its own, and AI has moved into that category for most enterprises. The request is rarely curiosity about the technology. ‍ The framing determines what belongs in the pack.

Has your organization been exposed? How can you act on the exposures that matter most? What’s the best way to demonstrate measurable risk reduction?

AI agents have crossed an important line from making suggestions to taking actions. They can read a repository, call internal systems, change code, open a pull request, and keep working while the human moves on.

Welcome to the 25th edition of Cloudflare's DDoS Threat Report. This is the first half-year edition in the series: rather than publishing separate reports for the first and second quarters of 2026, we have combined our coverage of Q1 and Q2 into a single volume covering January through June 2026.

Paying for a subscription does not automatically prevent the provider from storing company information or using it to train its models.

An employee pastes a customer contract into ChatGPT to summarize it. Nothing gets attached, nothing crosses the network in a file, and no alert fires. That is the gap most LLM security advice does not address. Prompt security is not the same problem as prompt injection or model hardening. It is a data problem consisting of what enters a prompt, what an agent does with it, and what comes back out.