Quantum Cryptography, Cybersecurity

Track post-quantum adoption with Amazon CloudFront’s new data

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November 2, 2026 by Ivy Delaney

Amazon CloudFront now details the security protocols used by its viewers, revealing the key exchange, signature scheme and public key type for each connection. The service can also identify the internet service provider originating each request, allowing users to isolate network-specific traffic patterns. With these new capabilities, CloudFront enables measurement of post-quantum cryptography adoption, pinpointing clients that currently do not use it. This visibility is provided through two new optional headers forwarded to origins, offering granular control over traffic and security analysis.

CloudFront-Viewer-ISP Enables Network Performance Breakdown

CloudFront-Viewer-ISP allows granular traffic breakdown by internet service provider, enabling users to pinpoint the origin of requests and isolate anomalous activity stemming from a specific network. This capability extends beyond simple identification. The service facilitates performance analysis segmented by ISP, revealing potential bottlenecks or inconsistencies within particular provider networks. By identifying the source of each request, operators can proactively address issues impacting viewers connected through a given ISP.

To implement these features, users must add the desired header to an origin request policy and then attach that policy to their CloudFront distribution. Amazon states that this service can be used to measure the share of viewers that connect with PQC and identify clients that do not yet do so, highlighting its potential for enhancing security posture and preparing for the quantum computing era.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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