Quantum Security, Quantum Cryptography

Gartner highlights QuSecure’s platform for quantum-safe data

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September 22, 2026 by Ivy Delaney

QuSecure has been recognized by Gartner as a Cool Vendor for its QuProtect R3 solution, a fully integrated platform for post-quantum cryptography. The acknowledgment arrives as the timeline for quantum-enabled attacks accelerates; IonQ recently published evidence suggesting current encryption could be broken as early as 2028.

Federal mandates requiring quantum safety will take effect January 1, 2027, creating immediate compliance pressure for organizations. Rebecca Krauthamer, CEO and co-founder of QuSecure, says generative AI is accelerating the rate at which cryptographic weaknesses can be discovered and exploited, while quantum computing is forcing organizations to prepare for a new class of risk.

QuProtect R3 Platform Enables Policy-Driven Post-Quantum Migration

According to a recent report by Gartner, QuSecure’s QuProtect R3 platform is the sole fully integrated, production-ready solution for cryptographic command and control. The platform delivers continuous cryptographic inventory, automated certificate rotation, and low-touch remediation to TLS 1. Gartner’s assessment spotlights vendors innovating in data security for AI-driven workflows, recognizing that static controls are insufficient to address modern threats, and specifically highlights policy-driven post-quantum cryptography migration as a key innovation, the company says.

The report notes that QuProtect R3 enables seamless migration to quantum-resistant cryptography across networks, regardless of native support, a capability becoming critical given shifting predictions for when quantum computers will pose a real threat to current encryption standards. Findings from Google, CloudFlare, and IBM further reinforce this accelerated timeline, all pointing to a heightened risk before the end of the decade.

The urgency is compounded by the practice of “harvest now, decrypt later,” where encrypted data is collected with the intention of being decrypted once quantum computers reach sufficient scale. Gartner’s report identifies specific roles within organizations that should prioritize this technology, including CIOs undertaking network security upgrades or TLS 1.3 migrations, cybersecurity leaders seeking central control, and GRC leaders needing audit-ready reports demonstrating post-quantum readiness.

The platform’s automated features address the challenge of managing cryptography across complex, legacy systems, reducing the need for extensive infrastructure or application code changes. The increasing role of generative AI in identifying cryptographic weaknesses further emphasizes the need for this agility, transforming cryptography from a periodic upgrade into a continuous operational discipline.

Gartner states the platform is particularly valuable for those with challenging legacy estates and those expediting post-quantum cryptography migration to mitigate the “harvest now, decrypt later” risk. The report also suggests CTOs and enterprise architecture leaders working on strategic improvements to crypto-agility would benefit from the platform’s capabilities, as would those responsible for demonstrating post-quantum readiness and seeking audit-ready reports.

This report spotlights vendors and innovations transforming data security for AI-driven and autonomous workflows, as traditional, static controls become increasingly inadequate to address modern threats.

Gartner
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Data security Google IBM post-quantum cryptography QuSecure
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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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