blog.cloudflare.com 29 Sept 2026, 13:00 UTC

Cloudflare Uses AI to Hunt Cryptography for 2029 PQ Goal

Cloudflare Uses AI to Hunt Cryptography for 2029 PQ Goal
CyberSIXT Evidence Panel Source marked as original reporting

CLOUDFLARE outlines its audacious move to post-quantum readiness, aiming for full PQ migration by 2029. The piece explains that, while many products have already switched to post-quantum encryption over TLS 1.3, there remains work in post-quantum authentication and broader platform-wide readiness. To tackle the enormity of the task, Cloudflare has developed CryptoLabe, an internal AI-assisted cryptography discovery tool.

CryptoLabe is designed to locate cryptographic usage across Cloudflare’s vast codebase, understand how each instance operates, and map a path to upgrade, with the goal of giving customers confidence that traffic is future-proofed against quantum adversaries.

CryptoLabe operates in two stages: discovery and deep analysis. In discovery, it maps repositories and searches for cryptographic activity across code, configurations and documentation, producing raw observations. In the analysis stage, the tool re-checks findings in the runtime context, determines the repository’s role, and checks for dependencies or overrides that could affect migration.

It classifies findings with categories such as PQ-ready hybrid key exchange and PQ-ready, and flags entries requiring more evidence or external dependencies. The approach also recognises hard cases—custom or hardware-bound cryptography and protocols lacking PQ standards—where additional prerequisites block immediate upgrades.

Cloudflare demonstrates its end-to-end orchestration using a two-Worker architecture, Durable Objects for coordination, and Cloudflare Workflows for persistent, retryable tasks, all while keeping code snapshots isolated in sandboxed environments. While CryptoLabe remains internal and not customer-facing, Cloudflare shares prompts and lessons to help other organisations plan their own PQ migrations.

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Article by CyberSIXT