Integrated Quantum Technologies has today, January 22, 2026, unveiled AIQu™ VEIL™, a groundbreaking platform poised to redefine how enterprises deploy artificial intelligence. This quantum-resilient AI infrastructure tackles a critical challenge: enabling scalable AI applications while simultaneously safeguarding sensitive and proprietary data. VEIL™ utilizes a proprietary technology called Informationally Compressive Anonymization (ICA™) to operate entirely on anonymized, compressed data, promising superior speed, accuracy, and efficiency. “Enterprises are eager to adopt AI, but the reality is that today’s infrastructure was never designed for the regulatory, security, and multinational complexity they now face,” said Alan Guibord, CEO and Chairman of Integrated Quantum Technologies. “AIQu™ VEIL™ represents a foundational leap forward—one that allows organizations to deploy AI without exposing their most valuable data.”
AIQu™ VEIL™ Platform Enables Quantum-Resilient AI
Developed by Integrated Quantum Technologies, VEIL™ addresses a growing paradox: the surging demand for AI colliding with increasingly stringent data privacy regulations and the looming threat of quantum computing’s decryption capabilities. Unlike conventional approaches, VEIL™ doesn’t simply add layers of encryption – it fundamentally alters how data interacts with AI systems. The core innovation lies in proprietary Informationally Compressive Anonymization (ICA™). This process transforms data into vectorized representations before it enters the AI pipeline, ensuring raw data is never exposed, processed, or retained. This isn’t merely about compliance, but about performance.
Existing privacy-preserving methods like homomorphic encryption and differential privacy, while theoretically sound, are “impractical for enterprise-scale AI,” according to the company, due to their computational intensity and resulting performance degradation. VEIL™ bypasses these limitations by anonymizing and compressing data simultaneously, a combination management believes is unprecedented. Jeremy Samuelson, EVP of AI and Innovation, architected VEIL™ following years of experience identifying systemic issues in enterprise AI deployments. He recognized that organizations face “mounting regulatory hurdles, operational inefficiencies, and scalability constraints,” leaving them without viable options for safe and efficient AI implementation.
The platform is designed to eliminate these constraints by reimagining data handling “from first principles,” rather than relying on incremental improvements to existing architectures. Looking ahead, Integrated Quantum Technologies positions AIQu™ VEIL™ as a crucial component in the emerging landscape of quantum AI. The company envisions VEIL™ as a core infrastructure layer supporting future products and solutions across sectors like finance, healthcare, and government, unlocking AI capabilities previously hampered by privacy and security concerns.
Informationally Compressive Anonymization (ICA™) Secures Data
A new approach to data security, Informationally Compressive Anonymization (ICA™), is poised to redefine how enterprises leverage artificial intelligence without exposing sensitive information. Developed by Integrated Quantum Technologies and integrated into their AIQu™ VEIL™ platform, ICA™ moves beyond traditional encryption methods to fundamentally alter the data pipeline itself, offering a potentially game-changing solution for privacy-preserving machine learning. This isn’t simply a matter of ticking compliance boxes; it’s about maintaining performance while mitigating risk.
The innovation stems from recognizing the inherent vulnerabilities of current AI architectures. “Regardless of regulatory requirements, enterprise data are inherently proprietary, and existing AI architectures expose data to unacceptable risk,” he explains. The result is a system where raw data are never exposed, processed, or retained, offering a robust shield against increasingly sophisticated attacks. This unified approach to anonymization and compression is a key differentiator.
Management believes “this is the first time anonymization and compression have been unified to such great effect,” creating a standard for data handling that is both enterprise-ready and resilient to future threats, including those posed by quantum computing.
Limitations of Existing Privacy-Preserving AI Methods
Current approaches to safeguarding data within artificial intelligence systems are increasingly proving inadequate for real-world enterprise deployment, hindering the promise of widespread AI adoption. National Institute of Standards and Technology (NIST), they present significant practical hurdles. These methods, despite being “theoretically sound,” are demonstrably “computationally intensive, difficult to deploy, and introduce severe downstream performance degradation,” limiting their use beyond academic exercises. The core issue isn’t a lack of theoretical solutions, but a failure to translate them into scalable, efficient systems. This performance bottleneck is particularly acute for multinational corporations.
They often require “redundant, regionally-pinned AI and ML instances in multiple jurisdictions,” creating fragmented, complex architectures prone to inconsistencies and escalating operational costs. Existing privacy standards exacerbate these problems, imposing “significant performance penalties” that render them impractical for large-scale AI applications. The result is a frustrating paradox: organizations are eager to leverage AI, but current infrastructure isn’t equipped to handle the regulatory and security demands of modern data handling. Furthermore, the threat landscape is constantly evolving. “Attackers are continuing to become increasingly sophisticated, exposing more attack surfaces within ML deployments,” demanding ever-more robust defenses.
Simply stripping data of identifying information isn’t enough; enterprise data is “inherently proprietary” and existing architectures leave it vulnerable.
VEIL™ Addresses Enterprise AI Scalability & Compliance
Integrated Quantum Technologies is tackling a fundamental bottleneck in enterprise AI adoption with AIQu™ VEIL™, a newly unveiled infrastructure platform designed to reconcile the demands of powerful artificial intelligence with stringent data governance. The challenge isn’t simply about keeping data secure, but about enabling scalable AI deployments without crippling performance – a problem magnified for multinational organizations. These groups often face fragmented architectures and model inconsistencies due to regionally-specific data regulations. Rather than relying on traditional encryption or manual anonymization – methods often deemed impractical at scale – VEIL™ employs a proprietary Informationally Compressive Anonymization (ICA™) process. The development of VEIL™ stemmed from practical experience, not theoretical research. He realized existing architectures exposed data to unacceptable risk, leading to regulatory hurdles and scalability constraints. The result is an infrastructure layer designed to “eliminate those constraints entirely. By replacing the traditional AI pipeline with infrastructure that only ever ingests and processes mathematically anonymized data, we’re enabling a new generation of AI systems that are both practical and secure at scale.”
Crucially, VEIL™ isn’t just about present-day compliance; it’s built with the future in mind. Integrated Quantum Technologies designed the platform to be quantum-resilient, anticipating the threats posed by increasingly sophisticated attackers and the eventual arrival of quantum computing.
By replacing the traditional AI pipeline with infrastructure that only ever ingests and processes mathematically anonymized data, we’re enabling a new generation of AI systems that are both practical and secure at scale.
Alan Guibord, CEO and Chairman of Integrated Quantum Technologies
