Conference Description
Key Takeaways
- Four-day hybrid conference examining the convergence of artificial intelligence and cybersecurity
- Technical focus on AI-driven threat detection, secure AI system development, and intelligent intrusion prevention
- Coverage spans IoT security, cloud and edge computing, healthcare data protection, and critical infrastructure defence
- Addresses ethical governance frameworks and trustworthy AI implementation challenges
- Designed for researchers, security professionals, AI engineers, and technology executives
Introduction
The 2nd IEEE 2026 International Conference on Cybersecurity and AI-Based Systems (CYBER-AI 2026) convenes in Bucharest, Romania, bringing together researchers, practitioners, and technology leaders to examine how artificial intelligence is reshaping cybersecurity practice. As organisations face increasingly sophisticated threat actors deploying AI-enhanced attack methodologies, the security community has responded with machine learning-driven detection systems, automated response mechanisms, and predictive threat intelligence platforms. This conference addresses both sides of that equation: leveraging AI to strengthen defensive capabilities while simultaneously securing AI systems against adversarial exploitation.
About CYBER-AI 2026
Organised under the auspices of the IEEE Romania Section in collaboration with the Romanian-American University, CYBER-AI 2026 operates as a hybrid event accommodating both in-person and virtual participation. The four-day programme combines keynote presentations, technical workshops, and interactive sessions designed to facilitate knowledge exchange between academic researchers and industry practitioners. This format reflects the increasingly interdisciplinary nature of cybersecurity work, where theoretical advances in machine learning must be validated against operational realities.
Technical Focus Areas
The conference programme spans multiple technical domains where AI and cybersecurity intersect. Core machine learning topics include deep learning architectures for pattern recognition, federated learning approaches that enable model training across distributed datasets without centralising sensitive information, and adversarial machine learning techniques that probe the vulnerabilities inherent in AI systems themselves.
On the defensive side, sessions examine intelligent intrusion detection systems capable of identifying novel attack patterns, network forensics methodologies enhanced by automated analysis, and malware detection frameworks that adapt to polymorphic threats. The programme also addresses blockchain applications in cybersecurity, exploring how distributed ledger technologies can strengthen authentication, audit trails, and data integrity verification.
Natural language processing features prominently in discussions of misinformation mitigation, an area of growing concern as synthetic media and AI-generated content complicate efforts to maintain information integrity. Extended reality technologies, including augmented, virtual, and mixed reality systems, present their own security considerations as these platforms become more prevalent in enterprise and consumer applications.
IoT and Infrastructure Security Challenges
A substantial portion of the conference addresses security challenges in Internet of Things deployments across healthcare, education, smart home, automotive, and drone applications. These environments present particular difficulties: resource-constrained devices often lack the computational capacity for traditional security controls, while the sheer scale of IoT networks creates expansive attack surfaces. Cloud and edge computing architectures introduce additional complexity, requiring security frameworks that function effectively across distributed processing environments with varying trust boundaries.
Healthcare applications receive dedicated attention, particularly privacy-by-design approaches that embed data protection principles into system architecture rather than treating privacy as an afterthought. This reflects broader regulatory pressures and the sensitive nature of medical information, where breaches carry significant consequences for both patients and institutions.
Governance and Ethical Considerations
Beyond purely technical matters, CYBER-AI 2026 examines the governance frameworks necessary for responsible AI deployment in security contexts. Trustworthy AI development requires addressing questions of transparency, accountability, and bias that become particularly consequential when automated systems make decisions affecting security posture or incident response. These discussions acknowledge that technical capability must be balanced against ethical obligations and regulatory requirements that continue to evolve across jurisdictions.
Who Should Attend
The conference serves a diverse professional community. Academic researchers and doctoral students will find opportunities to present findings and engage with peers working on related problems. Industry practitioners, including cybersecurity analysts, AI and machine learning engineers, and IT security managers, can explore emerging techniques with potential operational applications. Technology executives and research and development leaders may benefit from the strategic perspectives on where the field is heading and how organisations can prepare for evolving threat landscapes.
The hybrid format ensures accessibility for international participants while preserving the collaborative atmosphere that distinguishes in-person technical conferences.