Quantum Cryptography, Cybersecurity

AWS adds a new encryption method to guard data from future quantum attacks

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October 28, 2026 by Ivy Delaney

The technology enables secure data exchange by establishing a 32-byte shared secret, designed to protect against future decryption by quantum computers. NIST designed ML-KEM to replace older schemes like DH/ECDH and RSA-based key transport as part of its strategic cryptographic roadmap. However, the service is not yet available in the Middle East (UAE), Middle East (Bahrain), and China Regions.

ML-KEM Implementation Safeguards Against Future Quantum Attacks

AWS KMS now uses Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) adhering to the NIST FIPS 203 standard, a proactive measure against future decryption of currently recorded data. This implementation directly addresses the “harvest now, decrypt later” threat, where adversaries store encrypted data anticipating the arrival of sufficiently powerful quantum computers capable of breaking existing encryption. The KMS Encapsulate API generates a 32-byte shared secret and its ciphertext using the public key, while the Decapsulate API recovers the secret with the corresponding private key.

AWS KMS safeguards the private key of each ML-KEM key pair within FIPS 140-3 Level 3 validated hardware security modules, providing a robust layer of protection. Customers can use the GetPublicKey API to export public keys for local encapsulation using libraries such as AWS-LC, OpenSSL, or Bouncy Castle, facilitating secure data exchange with external partners. According to the AWS announcement, NIST designed ML-KEM to replace classical key-establishment schemes, highlighting the importance of adopting post-quantum cryptography standards. The availability of ML-KEM-512, ML-KEM-768, and ML-KEM-1024 key specifications provides customers with options tailored to their specific security requirements.

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AWS Cryptography ML-KEM NIST
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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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