Key Highlights:

• Mr. Jeremy Sameulson, EVP of AI and Innovation at IQT, publishes VEIL™ Privacy-Preserving Machine Learning Framework on arXiv: Introduces an architecture designed to enable use of sensitive data without exposing raw inputs, endorsed by Dr. Mohammad Tayebi, Professor at Simon Fraser University.
• VEIL™ introduces a new paradigm in privacy-preserving AI, embedding protection directly into model architecture and aligning data representations with downstream objectives to maintain and in some cases improve predictive performance without the computational burden or scalability limits of existing approaches.
• 25-Page Technical Paper Outlines Architecture and Theory: Includes 17 figures covering mathematical foundations and system design for privacy-preserving AI.

Vancouver, British Columbia--(Newsfile Corp. - March 31, 2026) - Integrated Cyber Solutions Inc. (CSE: ICS) (OTCQB: IGCRF) (FSE: Y4G), doing business as Integrated Quantum Technologies ("IQT" or the "Company"), announced the publication of a white paper (the "Paper") by Mr. Jeremy Samuelson, EVP of AI and Innovation at IQT. The Paper introduces VEIL™ (Vector Encoded Information Layer) and the VEIL^TM architecture, a privacy-preserving machine learning framework designed for use of sensitive data, and has been published on arXiv, the globally recognized open-access scientific research repository long hosted by Cornell University. The Paper has also been endorsed by Dr. Mohammad Tayebi, Assistant Professor of Professional Practice at Simon Fraser University.

The Paper, titled "Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning," is now publicly available at https://arxiv.org/pdf/2603.15842.

The following is a summary of certain information contained in the Paper. Readers are encouraged to review the Paper in full.

The Paper introduces Informationally Compressive Anonymization (ICA) and the VEIL^TM architecture, a framework to enable supervised machine learning on sensitive and regulated data while reducing exposure to raw inputs outside of trusted environments. The research contained in the Paper examines limitations associated with existing privacy-preserving machine learning approaches, including techniques such as homomorphic encryption and differential privacy, which may introduce computational overhead, increased latency, or reductions in predictive performance depending on implementation.

Pursuant to the Paper, the ICA approach embeds a supervised, multi-objective encoder within a trusted source environment to transform raw input into low-dimensional latent representations. Only these anonymized representations leave the trusted environment, ensuring that sensitive source data is not exposed during model training or inference. The Paper demonstrates that, under the assumptions analyzed, these representations are structurally non-invertible, meaning the original data cannot be constructed from the encoded outputs.

Unlike privacy methods that rely on cryptographic computation or stochastic noise injection, the Paper claims that VEIL^TM is designed to preserve predictive utility by explicitly aligning representation learning with downstream objectives. The Paper further notes that this approach uses architectural and informational constraints to protect data, with experimental results indicating predictive performance is maintained, or in some cases improved in the evaluated scenario, without the computational or scalability limitations associated with some existing privacy-preserving techniques.

The Paper presents a theoretical foundation for non-invertibility of encoded representations using topological and information-theoretic analysis. The Paper demonstrates that under idealized attacker assumptions, reconstruction of the original data is logically infeasible and that, in practical deployment, the probability of reconstruction approaches zero as attacker uncertainty increases. The analysis contained in the Paper further describes how dimensionality reduction and attacker uncertainty jointly contribute to limiting reconstruction risk.

The VEIL™ architecture described in the Paper establishes separation between source, training, and inference environments. The architecture described in the Paper defines boundaries designed to keep raw sensitive data within trusted environments while allowing encoded representations to be used in downstream machine learning workflows. The Paper also outlines deployment considerations for distributed environments and discusses how the architecture may be applied across multi-region deployments.

The research in the Paper focuses on supervised machine learning workflows involving sensitive data inputs and provides a structured approach to encoding data prior to model training. The Paper describes how this architecture may be applicable to organizations with sensitive or regulated datasets, while minimizing data exposure in operational and governance considerations.

The Paper has been endorsed by Dr. Mohammad Tayebi, Assistant Professor of Professional Practice in the School of Computing Science at Simon Fraser University, whose research focuses on machine learning, cybersecurity, and AI Safety.

The Paper spans 25 pages and includes 17 figures detailing the architecture, mathematical foundations, and an experimental scenario described in the research. It is categorized under machine learning, artificial intelligence, and information theory on arXiv.

About Integrated Quantum Technologies

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

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 VEIL^TM, including practical applications to organizations with sensitive or regulated datasets, the privacy protection possibilities of VEIL^TM, predicative performance of VEIL^TM, 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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