Bài viết giới thiệu một trình xác thực (verifier) sản xuất cho giao thức ML-DSA, được viết hoàn toàn bằng Python với chỉ 350 dòng code, dễ đọc và đáng tin cậy.
Một lập trình viên cần đọc bài này để tìm hiểu cách triển khai một hệ thống xác minh ML-DSA (Machine Learning Digital Signature Algorithm) trong Python với hiệu suất cao và độ tin cậy, giúp tiết kiệm thời gian phát triển và tối ưu hóa cho ứng dụng sản xuất thực tế.
MySQL vừa phát hành bản Early Access đầu tiên của phiên bản 26.7 thông qua MySQL Labs, cung cấp cái nhìn sơ lược về những cải tiến mới bên trong.
Lập trình viên sử dụng MySQL cần tìm hiểu về phiên bản Early Access 26.7 để cập nhật kiến thức về các tính năng mới, cải tiến hiệu suất và tính bảo mật trong cơ sở dữ liệu, giúp tối ưu hóa ứng dụng của mình với phiên bản tương thích và hiệu suất cao hơn.
Trung tâm công nghệ lượng tử Bloch (Bloch Quantum Tech Hub) huy động được gần 55 triệu USD từ nguồn vốn công và tư, trong đó có khoản tài trợ 30 triệu USD từ chương trình EDA của Mỹ.
Là một lập trình viên muốn tham gia phát triển các ứng dụng lượng tử thực tế, bài viết này giúp bạn hiểu rõ về nguồn tài trợ quan trọng từ chính phủ Mỹ cho các trung tâm nghiên cứu lượng tử, giúp bạn tìm hiểu cơ hội hợp tác và ứng dụng công nghệ này vào dự án của mình.
Tennessee dành 3 triệu USD thu hút các nhóm nghiên cứu lượng tử NSF X-Labs thiết lập hoạt động nghiên cứu, thương mại hóa hoặc kinh doanh.
Những kiến thức về quantum computing và xu hướng mới này sẽ giúp bạn hiểu rõ hơn về những cơ hội công việc, kỹ năng tương lai và cách ứng dụng trong ngành công nghệ tiên tiến, từ đó có thể chuẩn bị sẵn sàng cho thị trường lao động đang phát triển nhanh chóng.
Sắc lệnh hành pháp 14409 của Mỹ yêu cầu các cơ quan liên bang và nhà thầu phải chuyển sang mã hóa hậu lượng tử (PQC) vào năm 2030 và xác thực hậu lượng tử vào năm 2031, nhằm ngăn chặn các cuộc tấn công "thu thập giờ đây giải mã sau". Cloudflare khuyến nghị cần làm rõ tiêu chuẩn "chuyển đổi", ưu tiên khả năng thích ứng mật mã (crypto agility) và thúc đẩy sự thống nhất toàn cầu về thuật toán NIST để tránh phân mảnh.
Lập trình viên nên đọc bài này để hiểu cách chuyển đổi sang các giải pháp mã hóa chống lượng tử (post-quantum) không chỉ là một yêu cầu pháp lý mà là một chiến lược bảo mật cấp hệ thống, giúp bảo vệ ứng dụng của bạn trước các mối đe dọa tương lai từ máy tính lượng tử trong thời gian ngắn nhất.
Protect AI agent traffic from 'Harvest Now, Decrypt Later' attacks. Learn to implement hybrid post-quantum cryptography within your Model Context Protocol stack.
Quantum machine learning (QML) has surged as a prominent area of research with the objective to go beyond the capabilities of classical machine learning models. A critical aspect of any learning task is the process of data embedding, which directly impacts model performance. Poorly designed data-embedding strategies can significantly impact the success of a learning task. Despite its importance, rigorous analyses of data-embedding effects are limited, leaving many cases without effective assessment methods. In this work, we introduce a metric for binary classification tasks, the class margin, by merging the concepts of average randomness and classification margin. This metric analytically connects data-induced randomness with classification accuracy for a given data-embedding map. We benchmark a range of data-embedding strategies through class margin, demonstrating its ability to identify data-induced randomness that hinders classification performance. We expect this work to provide a new approach to evaluate QML models by their data-embedding processes, addressing gaps left by existing analytical tools
Two Threats, One Data Problem: Key Findings from the Thales Quantum and AI Threat Report lee.potok@thal… Tue, 07/28/2026 - 07:50 Artificial intelligence and quantum computing are reshaping cybersecurity faster than many organizations realize. The 2026 Thales Quantum & AI Threat Report reveals how these technologies are converging on a single target enterprise data and why "harvest now, decrypt later" has become the leading quantum concern. Explore the report's key findings and learn why strengthening your data security foundation today is essential for protecting against both current AI-driven threats and tomorrow's quantum risks. Encryption Data Security Anina Steele | Senior PR Manager More About This Author > Artificial intelligence and quantum computing are often discussed as separate technology trends. But fresh findings from the Thales Quantum and AI Threat Report suggest that treating them as separate problems is a mistake we can’t really afford right now. Based on responses from 3,120 security and technology professionals across 20 countries, the report reveals a common theme running through both technologies: they are converging on the same target - enterprise data. So, how should we go about protecting data at the crossroads of two revolutions? Key takeaways The Thales 2026 Quantum & AI Threat Report surveyed 3,120 security and IT professionals across 20 countries.98% of respondents now weigh how AI and quantum computing affect each other — both are converging on one target: enterprise data.Harvest now, decrypt later (HNDL) is the top quantum concern, cited by 61% of respondents.AI risk is already here: 59% have experienced deepfake attacks, and only 3% report no harm from AI-generated threats.Organizations with stronger data security fundamentals are consistently more ready for both the AI and the quantum shift. AI and quantum computing can’t be viewed in isolation AI doesn’t take away the need to prepare for quantum — it makes that need more urgent. AI can make attacks happen faster, while we depend more and more on encryption to keep things safe.Going back to the report, the vast majority (98%) of respondents said they are considering how AI and quantum computing interact. The conversation has already moved past viewing these technologies in isolation. While AI and quantum operate on different timelines, both are creating pressure on the same security foundations, including data discovery, classification, encryption, key management, and cloud security. The organizations making progress in one area are often further ahead in the other.That concern is not only defensive. According to 451 Research's VoTE Digital Pulse Quantum Computing study, 36% of organizations expect quantum computing to deliver material business value within one to three years, and 52% cite competitive advantage as the main driver of investment. The AI data security pressure is already here Unlike quantum computing, AI security is not a concern for some future date.Organizations are already increasing spending on AI initiatives. 25% ranked AI security as the second-highest security spending category, after cloud. 30% reported significant increases in AI-specific security budgets.At the same time, security teams are finding themselves under pressure to support AI programs where speed and innovation often take precedence. Security ranked second from the bottom among reported AI project success criteria at just 35%, suggesting that many organizations still see it as a supporting function rather than a primary objective.The major hurdles to AI implementation also relate to data. The top inhibitors to AI adoption were data quality and governance (65%) and data exposure (61%).The report also highlights how quickly AI-generated risks have become part of everyday security operations. Only 3% of respondents reported experiencing no harm or damage from AI-generated threats. Human error is the leading cause of breaches, but deepfakes emerged as the leading AI-related concern. 59% of respondents reported experiencing deepfake attacks, while 48% reported reputational damage linked to AI-generated threats, reflecting the growing sophistication of AI-enabled social engineering.The growing adoption of agentic AI and autonomous systems within organizations is driving a surge in the volume and velocity of data use. As such, cybersecurity professionals are under pressure to control data access and understand where the data resides, how it is used, and whether it is adequately protected as it is used in complex AI workflows. Quantum risk is no longer future tense: harvest now, decrypt later If AI is today's pressure, quantum computing is tomorrow's deadline.The report shows that organizations are already adjusting their security priorities in response. Harvest-Now, Decrypt-Later (HNDL) attacks are now the leading quantum-related concern, cited by 61% of respondents.In a harvest now, decrypt later attack, adversaries collect encrypted data today and store it to decrypt in the future, once quantum computers can break the encryption that protects it. The concern is straightforward. Sensitive data stolen today could be retained and decrypted years later once sufficiently powerful quantum systems become available.A significant portion of organizations are already preparing. Nearly a third (32%) are either experimenting with or surveying quantum computing initiatives, while 59% plan to prototype or evaluate post-quantum cryptography (PQC) algorithms within the next 18 to 24 months.Current trends reveal that discussions about timing are becoming pressing. A recent preprint (not yet peer-reviewed) from a Caltech-led research team found that Shor’s algorithm could run at cryptographically relevant scales with as few as 10,000 reconfigurable atomic qubits — dramatically fewer than earlier estimates for attacks on schemes such as 256-bit elliptic curve cryptography.The timing of when a true "Q-Day" will arrive remains contested, but some estimates say it could be as early as 2030. Irrespective of whether that timeline is right, transitioning to quantum-resistant security controls will take years, so preparation must start as soon as possible. Leaders and Laggards: The Data Gap The report also compared technology leaders and laggards.AI security leaders were defined as organizations that have invested in AI-specific security and consider themselves ahead of peers in AI adoption. Quantum leaders were organizations actively experimenting with or surveying quantum computing opportunities.Across both groups, stronger data security fundamentals consistently appeared alongside greater readiness for emerging technologies.Among AI leaders, 36% reported complete knowledge of where their data is stored, compared with 28% of laggards. 43% reported being able to fully classify their data, compared with 35% among laggards.The relationship also works in the other direction. AI leaders are further along in their quantum journey, with 33% actively experimenting with or surveying quantum projects compared with 22% of laggards. Meanwhile, 89% of quantum leaders have invested in AI-specific security compared with 80% of quantum laggards.The implication is difficult to ignore. Organizations that build stronger foundations around data security appear better positioned to adapt to both technological shifts. Cloud Remains the Common Attack Surface The report also reinforces the central role cloud environments play in modern security risk.According to 451 Research's VoTE Digital Pulse Quantum Computing, cloud security has been the leading enterprise security pain point every year since 2021.That trend appears throughout the report's findings. The three most frequently targeted assets are all cloud-related: cloud storage (35%), cloud applications (34%), and cloud management infrastructure (32%).The average organization now uses 2.3 cloud providers, while 39% use more than three. As AI initiatives depend on cloud infrastructure for training, inference, and data storage, the attack surface continues to widen.For security teams, visibility becomes increasingly important as environments become more distributed and data moves across multiple platforms, providers, and AI services. The leader/laggard split in the report suggests that organizations investing in data fundamentals now are not just managing today's AI risk. They are also narrowing their exposure to a quantum threat that will not announce itself in advance. Frequently asked questions What is harvest now, decrypt later (HNDL)?Harvest now, decrypt later (HNDL) is an attack strategy in which adversaries steal and store encrypted data today so they can decrypt it later, once quantum computers are powerful enough to break current encryption. In the Thales 2026 Quantum & AI Threat Report, 61% of respondents named HNDL their top quantum-related concern.Is quantum computing a cybersecurity threat?Yes. Large-scale quantum computers are expected to break the public-key encryption — such as RSA and elliptic curve cryptography — that protects most data in transit and at rest. The most immediate risk is harvest now, decrypt later, where data stolen today is decrypted once that capability exists.When is Q-Day?“Q-Day” is the point at which a quantum computer can break today’s encryption. Estimates vary and remain contested, but some place it as early as 2030. Because migrating to quantum-resistant controls takes years, most experts advise starting preparation now.How can organizations prepare for AI and quantum data threats?The report points to strong data security fundamentals: knowing where data lives, classifying it, and protecting it with encryption and key management. Practical first steps include building crypto-agility and evaluating post-quantum cryptography, and using data security posture management to discover and protect sensitive data across cloud and hybrid environments.Download the full Thales Quantum and AI Threat Report to explore the complete findings and recommendations. Schema <script type="application/ld+json"> { "@context": "https://schema.org", "@graph": , "potentialAction": { "@type": "SearchAction", "target": "https://cpl.thalesgroup.com/search?search={search_term_string}", "query-input": "required name=search_term_string" } }, "keywords": , "articleSection": "Encryption", "inLanguage": "en-US" } ] } </script> studio THALES BLOG Two Threats, One Data Problem: Key Findings from the Thales Quantum and AI Threat Report July 28, 2026
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