CISO Dinner

An experienced information security executive with over 15 years of practitioner experience in cyber defence technologies, security operations, counter threat intelligence, data analytics and behavioural classification of cyber criminal. Within the domain of organisational operations Azeem has an extensive experience in managing P&L, driving operational excellence, change management and process reengineering. Azeem has been at the forefront of architecting cyber resilience capability against Advanced Persistent Threats (APT) for some of the best financial, government and public sector organisations across Europe, the US, Asia and the Middle East. He has worked extensively with the national and international law enforcement agencies around intelligence training, detection and investigation of cyber crime. As a subject matter expert, he has made frequent appearance on regional television and radio programmes as an expert on cyber threats. A published book author and academic criminologist, he has also authored several periodical on advanced security threats in peer reviewed journals and security magazines. He is an eminent plenary conference guest speaker both at the national and international level.

Morten Lantow offers a background rich in hands-on technical expertise and strategic leadership within the machine learning domain. Initially contributing as a Machine Learning platform engineer, Morten evolved his career into a consultancy role at EY, where he was instrumental in spearheading the Danish MLOps offering. His transition from engineering to consultancy underscores a broadened perspective on the challenges and strategies pivotal to executing successful machine learning operations. Currently leading the Machine Learning Operations team at DSV - Global Transport and Logistics, Morten's approach is characterized by a pragmatic and understated leadership style, focusing on empowering his team to build robust ML systems within strict regulatory standards. His participation in the panel will shed light on the nuanced process of talent acquisition in the MLOps sector, offering insights into how these efforts are crucial for the foundational success of machine learning projects.