Vancouver, British Columbia--(Newsfile Corp. - May 26, 2026) - Integrated Cyber Solutions Inc. (CSE: ICS) (OTCQB: IGCRF) (FSE: Y4G), doing business as Integrated Quantum Technologies ("Integrated Quantum", "IQT", or the "Company"), announced today the release of the latest iteration of its white paper on its ML data security solution, VEIL™.
VEIL™, designed to tackle one of the core barriers in enterprise AI - how to use sensitive data without exposing it - enables enterprises to securely utilize data by removing sensitive information before it ever enters the ML pipeline. The latest iteration of the white paper significantly expands the scope of VEIL™'s evaluation, including broader testing across enterprise-scale datasets, machine learning tasks and simulated privacy attack environments, further advancing the Company's ongoing research into privacy-preserving AI infrastructure technologies.
Key Highlights
Reported compression levels ranged from approximately 95% to 99.96% across evaluated datasets and machine learning tasks
VEIL™ maintained predictive utility comparable to and/or exceeding baseline raw-data model performance
VEIL™ was additionally evaluated for enterprise applicability across healthcare, financial services, image recognition and enterprise-scale data environments, utilizing multiple benchmark and enterprise datasets alongside simulated privacy attack scenarios
White Paper Overview
The paper is entitled "Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning," authored by Jeremy J. Samuelson, EVP, Artificial Intelligence & Innovation.
The updated paper builds upon pre-existing research and evaluates VEIL™ across multiple supervised machine learning tasks and datasets. In line, reported compression levels range from approximately 95% to 99.96%, depending on the dataset, dimensionality and model architecture utilized. VEIL™ also maintained predictive utility, comparable to and or exceeding baseline raw-data model performance.
VEIL™'s performance was evaluated across image recognition, financial services, healthcare, regression modeling and large-scale enterprise data environments. Additional datasets evaluated include MNIST, Fashion-MNIST, Ames Housing, YearPredictionMSD, Home Credit Default Risk, Default of Credit Card Clients, CBIS-DDSM medical imaging data and the E2006 financial filings dataset.
"The updated paper significantly expands both the theoretical and empirical evaluation of VEIL across a broader range of datasets, machine learning tasks and simulated attack environments," said Jeremy J. Samuelson, EVP, Artificial Intelligence & Innovation at Integrated Quantum. "Our research continues to support the view that informational compression and architectural isolation may provide a viable framework for privacy-preserving machine learning without requiring the substantial computational overhead commonly associated with certain existing approaches. We also believe the compression characteristics demonstrated in the paper could have meaningful implications for enterprise AI efficiency and infrastructure optimization in certain deployment scenarios."
The updated white paper is also supported and endorsed by Dr. Mohammad Tayebi, Assistant Professor in the School of Computing Science at Simon Fraser University, who was referenced in the Company's original white paper announcement. Dr. Tayebi has no affiliation with Integrated Quantum and has received no compensation from the Company in connection with the endorsement, the white paper or the underlying research.
VEIL Measured Against Differential Privacy and Homomorphic Encryption
To further VEIL's capabilities, the paper highlights VEIL™'s performance against established privacy-preserving machine learning methodologies. These include Differential Privacy ("DP") and Homomorphic Encryption ("HE"), both of which are associated with predictive performance trade-offs in addition to computational overhead, privacy-budget management requirements and ciphertext expansion characteristics under certain implementations and testing conditions.
In addition, the paper includes multiple simulated privacy attack evaluations, including reconstruction attack simulations and attribute inference analyses intended to assess the resilience of VEIL™ protected latent representations under various threat scenarios and attacker assumptions. Under the evaluated testing conditions described in the paper, VEIL™ outperformed Differential Privacy across the reported attack simulations. The paper further notes that in certain enterprise deployment scenarios involving vulnerabilities elsewhere in a system environment, including leaked sensitive indices and external data correlation, VEIL™ may still permit limited sensitive information leakage under specific adversarial conditions.
The Company believes that the ability to materially reduce dataset size while preserving model utility may have broader implications for enterprise AI infrastructure efficiency, including potential reductions in storage, transfer and computational requirements associated with certain machine learning workflows.
The Company also notes that the findings, performance observations and comparative analyses contained in the paper are based on internal research, simulations, validation studies, datasets, configurations and assumptions utilized by the Company and the paper's author. Results may not be indicative of performance in all commercial deployments, production environments or customer use cases.
The updated white paper forms part of Integrated Quantum's ongoing research initiatives focused on privacy-preserving and post-quantum enterprise AI infrastructure technologies.
For additional information and access to the white paper, please visit: https://arxiv.org/pdf/2603.15842
About Integrated Quantum Technologies Inc.
Integrated Quantum Technologies Inc. is building quantum-ready infrastructure to help secure and scale artificial intelligence. The Company's product offerings include AIQu™ platform that supports its long-term strategy for privacy-preserving and resilient AI systems and VEIL™ is its first commercial product designed to protect sensitive AI data and workflows in enterprise environments. IQT's proprietary technologies address emerging post-quantum security risks, growing compute demands, and the increasing complexity of deploying AI at scale, complemented by its Managed Services offering and SecureGuard360™ cybersecurity platform for end-to-end AI security and monitoring. For more information, visit: www.integratedquantum.com.
On Behalf of the Board of Directors
Alan Guibord, Director & Chief Executive Officer
Integrated Cyber Solutions Inc. dba Integrated Quantum Technologies
For further information, please contact:
Tel: +1-212-634-9534
investors@integratedquantum.com
Forward-Looking Statements
The information contained herein contains "forward-looking information" within the meaning of applicable Canadian securities legislation. "Forward-looking information" includes, but is not limited to, statements with respect to the activities, events or developments that the Company expects or anticipates will or may occur in the future, including, without limitation, statements with respect to, claims regarding the potential applicability of VEILTM, including practical applications to organizations with sensitive or regulated datasets, the privacy protection possibilities of VEILTM, predicative performance of VEILTM, viability of the theoretical foundation for non-invertible of encoded representations, Generally, but not always, forward-looking information can be identified by the use of words such as "plans", "expects", "is expected", "budget", "scheduled", "estimates", "forecasts", "intends", "anticipates", or "believes" or the negative connotation thereof or variations of such words and phrases or state that certain actions, events or results "may", "could", "would", "might" or "will be taken", "occur" or "be achieved" or the negative connotation thereof.
Such forward-looking information is based on numerous assumptions, including among others, assumptions regarding the Company's ability to execute its business strategy; successfully develop and commercialize its technology and products; obtain and maintain necessary intellectual property protections; secure adequate financing on commercially reasonable terms; operate under applicable regulatory and legal frameworks; the continued demand for and adoption of privacy-preserving artificial intelligence solutions under prevailing economic and market conditions; the concepts, methodologies, and technical conclusions described in the Paper, including the VEIL™ architecture and Informationally Compressive Anonymization framework, will continue to be viable and applicable in commercial and operational environments; that the Company will be able to further develop, refine, and implement these technologies in products; that the performance characteristics, security properties, and scalability observed in experimental and modeled scenarios can be achieved in practical deployments; that the Company will be able to operate its solutions within applicable regulatory, data protection, and governance frameworks; and that sufficient technical, financial, and human resources will be available to support ongoing research, product development, and commercialization efforts. Although the assumptions made by the Company in providing forward-looking information are considered reasonable by management at the time, there can be no assurance that such assumptions will prove to be accurate.
Forward-looking information and statements also involve known and unknown risks and uncertainties and other factors, which may cause actual events or results in future periods to differ materially from any projections of future events or results expressed or implied by such forward-looking information or statements, including, among others: risks relating to the Company's ability to further develop, implement, and commercialize the VEIL™ architecture and related technologies; uncertainties regarding whether the technical performance, security characteristics, and scalability demonstrated in the Paper's research, modeling, or experimental scenarios can be replicated in real-world commercial deployments; risks associated with evolving data protection, cybersecurity, and artificial intelligence regulatory frameworks; the Company's ability to secure and protect intellectual property rights; dependence on key personnel and technical expertise; availability of financing on acceptable terms; market acceptance of the Company's products; and the receipt of necessary governmental, regulatory,or other approvals and the risk factors with respect to the Company set out in the Company's filings with the Canadian securities regulators and available under the Company's profile on SEDAR+ at www.sedarplus.ca.
Although the Company has attempted to identify important factors that could cause actual results to differ materially from those contained in the forward-looking information or implied by forward-looking information, there may be other factors that cause results not to be as anticipated, estimated or intended. There can be no assurance that forward-looking information will prove to be accurate, as actual results and future events could differ materially from those anticipated, estimated or intended. Accordingly, readers should not place undue reliance on forward-looking statements or information. The Company undertakes no obligation to update or reissue forward-looking information as a result of new information or events except as required by applicable securities laws.
Neither the CSE nor its Market Regulator (as that term is defined in the policies of the CSE) accepts responsibility for the adequacy or accuracy of this release.
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