Java licensing has become one of the most pressing challenges for IT Asset Management (ITAM) and Software Asset Management (SAM) professionals. Evolving terms, complex licensing models, and the hidden sprawl of Java instances across hybrid estates make compliance a moving target. Add to this the rising risk of security vulnerabilities from outdated or unsupported versions, and ITAM/SAM teams are being asked to manage more than contracts—they are now on the front line of risk, security, and cost optimization. Artificial Intelligence (AI) provides a way forward. By applying machine learning models to real-time asset and usage data, AI can detect underutilized or unauthorized Java deployments, predict future license demand, and highlight vulnerabilities long before they become audit findings or incidents. Supervised learning models can forecast licensing trends, unsupervised models can uncover shadow IT or anomalous usage, and reinforcement learning agents can recommend proactive remediation and migration paths. In this session you will learn:

– How to address the evolving challenges of Java licensing compliance when traditional approaches fall short?
– How to leverage AI-powered ITAM and SAM practices to reduce audit exposure, optimize license spend, and enhance security posture?
– How to gather high-quality runtime and usage data to strengthen compliance and vulnerability management?
– How to expand the role of ITAM/SAM teams from managing entitlements to driving security resilience, risk mitigation, and business value?