Building secure, privacy-preserving, and intelligent systems at the intersection of decentralized cybersecurity and artificial intelligence to protect critical infrastructures.
Explore ResearchOur work spans critical areas of cybersecurity and Artificial Intelligence, focusing on building robust, privacy-preserving systems for healthcare, industrial IoT, and smart environments.
Designing closed-loop autonomous cybersecurity systems that continuously perceive threats, reason under uncertainty, and take adaptive defensive actions with minimal human intervention.
Studying the security, reliability, and controllability of agentic and autonomous AI systems, including adversarial manipulation, unsafe emergent behavior, verification, and deployment-time guarantees.
Developing federated and decentralized learning systems that enable cross-organizational collaboration while addressing trust, robustness, auditability, and adversarial behavior.
Designing secure and auditable machine unlearning frameworks that enable the selective removal of data, knowledge, or behaviors from AI systems. Our research explores federated unlearning, agentic AI unlearning, privacy compliance, machine forgetting, verifiable deletion, and trustworthy post-deployment AI governance.
Designing privacy-preserving learning frameworks with formal guarantees for sensitive domains, integrating differential privacy, secure computation, and cryptographic safeguards into real-world AI systems.
Protecting cyber-physical and industrial IoT systems through AI-driven detection, adaptive defense, and resilient control mechanisms for critical infrastructure.
Integrating AI-driven threat intelligence with governance frameworks that support explainability, accountability, and coordinated decision-making in security operations.
Studying the security and trustworthiness of blockchain-enabled autonomous agents in Web3 ecosystems, focusing on adversarial behavior, economic manipulation, and governance.
Ongoing funded and collaborative projects at DCAILab focused on trustworthy AI, federated learning, cybersecurity, privacy-preserving systems, and AI governance.
The Decentralized Cybersecurity and Artificial Intelligence Lab (DCAILab) is dedicated to advancing the frontiers of secure and intelligent systems. Our research combines cutting-edge AI techniques with robust cybersecurity frameworks to address the most pressing challenges in protecting critical infrastructures.
We focus on developing privacy-preserving machine learning algorithms, secure federated learning systems, and autonomous cybersecurity solutions that can defend against sophisticated threats while maintaining user privacy and system integrity.
Our interdisciplinary approach brings together expertise in artificial intelligence, cryptography, distributed systems, and cybersecurity to create innovative solutions for healthcare, industrial IoT, smart cities, and emerging Web3 technologies.
Our diverse team of researchers, engineers, and students working together to advance cybersecurity and AI.
Explore our latest research contributions to the fields of cybersecurity and artificial intelligence.
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Invited talks, panels, and public engagement activities by DCAILab.
We are recruiting highly motivated students to work on cutting-edge research in Agentic AI, Federated Learning, and Cybersecurity for Critical Infrastructure.
Join DCAILab and our interdisciplinary research team on a newly funded National Cybersecurity Consortium (NCC) project: Cyber-Synthetic Risk: A Generative Multi-Agent Approach to Forecasting Advanced Cyberattack Impacts on Enterprise Ecosystems.
Research Areas: Generative AI for Cybersecurity · Multi-Agent Systems · Cyber Risk Forecasting · Enterprise Resilience · Threat Intelligence · Adversarial Simulation · AI Governance
Research topics include Agentic AI security, privacy-preserving machine learning, federated unlearning, and AI governance for critical infrastructure systems.
Work on applied AI-driven cybersecurity projects and contribute to real-world systems involving IoT, smart grids, and decentralized AI.
Recent highlights including publications, invited talks, workshops, grants, and student achievements.
Join an interdisciplinary discussion bringing together experts from academia, industry, law, public policy, healthcare, and technology to examine secure system design, privacy-preserving architectures, ethical responsibility, legal accountability, human oversight, and governance for Agentic AI.
Register for the SessionSelected conferences, workshops, and special issues closely aligned with the research interests of DCAILab in cybersecurity, federated learning, privacy-preserving AI, cyber-physical systems, and autonomous security.
International research internship opportunities at DCAILab, University of Regina, for students interested in artificial intelligence, cybersecurity, privacy-preserving AI, federated learning, autonomous systems, and blockchain security.
Three DCAILab research projects are available through the 2027 Mitacs Globalink Research Internship (GRI) program. Eligible international students can apply to conduct a 12-week research internship at the University of Regina in areas spanning artificial intelligence, cybersecurity, federated learning, privacy-preserving AI, autonomous agents, blockchain, and smart contract security.
Eligibility & ApplyThis project investigates privacy-preserving federated learning techniques for healthcare analytics. The student will explore distributed model training, secure aggregation, differential privacy, and explainable AI using publicly available healthcare datasets.
This project investigates autonomous AI agents for cybersecurity applications. The student will explore machine learning models, large language models, and multi-agent systems for cyber threat analysis, threat hunting, intelligent security decision-making, and incident response.
This project investigates methods for improving blockchain security through smart contract analysis, vulnerability detection, and intelligent monitoring. The student will gain experience with blockchain architectures, smart contract security testing, and AI-assisted vulnerability analysis.
Eligibility Note: The Mitacs Globalink Research Internship is a competitive program for eligible international undergraduate students and students enrolled in eligible combined undergraduate/master’s programs. Eligibility requirements vary by country and institution. Applicants should review the official Mitacs eligibility requirements before applying.
Applicants apply through the official Mitacs Globalink portal. DCAILab does not independently administer or award the Globalink Research Internship.
Interested in collaboration, joining our team, or learning more about our research?
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We welcome graduate students, postdocs, and visiting researchers.