Quantum XChange warns of AI security gap amid rising attacks

Nation-state adversaries are actively collecting encrypted data in “Harvest Now, Decrypt Later” attacks, according to Quantum XChange, as the timeline for cryptographically relevant quantum computers shrinks. Industry forecasts now anticipate these computers could arrive as early as 2029, placing data gathered at risk for years to come.

Antonio Sanchez, Chief Strategy Officer at Quantum XChange, will present these findings at the ONUG AI Networking Summit, emphasizing that “organizations are investing billions in AI, but many are overlooking the encryption protecting the data that makes those systems valuable.” Sanchez’s presentation, “Protecting Your AI Systems from the Quantum Threat,” begins at 1:30 p.m. ET.

Harvest Now, Decrypt Later: Emerging Quantum Threats to AI Data

The escalating threat to artificial intelligence systems extends beyond future quantum computing capabilities; adversaries are actively collecting encrypted data for decryption once those capabilities materialize. Quantum XChange reports that nation-state actors are currently employing “Harvest Now, Decrypt Later” attacks, targeting sensitive information with long-term value. This proactive data collection positions them to exploit vulnerabilities as quantum computers advance, potentially compromising years of proprietary data and customer records.

Industry forecasts now anticipate cryptographically relevant quantum computers being available as early as 2029, creating a narrow window for organizations to bolster their defenses. The increasing reliance on AI across sectors like finance, critical infrastructure, and government amplifies the importance of securing data in transit. AI workloads frequently involve continuous exchanges of sensitive information between applications, cloud environments and external systems, making network connections a critical point of vulnerability.

Quantum XChange emphasizes that organizations do not need to overhaul existing infrastructure to begin mitigating these risks; instead, a phased approach focused on securing data in motion offers immediate protection, the company says. This strategy centers on crypto-agility, the ability to adapt cryptographic protections as threats and standards evolve, allowing for continuous improvement without disruptive replacements.

He further explained that the current focus on securing AI models and infrastructure often overshadows the foundational security of the data itself, creating a significant blind spot. This oversight is particularly concerning given the potential for long-term exposure, as adversaries can store encrypted data indefinitely, awaiting the arrival of decryption technology.

Sanchez advocates for a shift in mindset, urging organizations to prioritize the protection of data in motion rather than waiting for a complete transition to post-quantum cryptography. “Protecting AI systems against quantum threats doesn’t require organizations to replace their existing infrastructure or wait until every cryptographic vulnerability has been identified,” he added.

“They can begin securing their most sensitive data in motion today and build the flexibility to adopt stronger protections as the threat evolves. The important thing is to start now.” The company’s Phio TX platform aims to facilitate this transition, enabling quantum-safe protection across existing network infrastructure without requiring costly overhauls. Modernizing cryptographic key management is also important, as static private keys can create long-term vulnerabilities if compromised. The ability to rapidly update and replace cryptographic protections is paramount, ensuring that systems remain resilient against evolving threats.

The implications of failing to address these vulnerabilities extend beyond data breaches, potentially impacting competitive advantage and long-term AI investment. The quantum threat, therefore, demands immediate attention and a proactive approach to safeguarding the data that fuels the next generation of intelligent systems.

Protecting AI systems against quantum threats doesn’t require organizations to replace their existing infrastructure or wait until every cryptographic vulnerability has been identified.

Antonio Sanchez, Chief Strategy Officer at Quantum XChange
Stay current

See today’s quantum computing news on Quantum Zeitgeist for the latest breakthroughs in qubits, hardware, algorithms, and industry deals.

Avatar of Ivy Delaney

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.

Latest Posts by Ivy Delaney: