Java Security Challenges Increase Operational Burden for Enterprises

Security teams in large enterprises are increasingly burdened by the need to track vulnerabilities in Java environments. According to a 2026 Azul survey of over 2,000 Java professionals, 64% reported that more than half of their organization’s applications or workloads are built with Java or run on a Java Virtual Machine. This wide usage means that security issues can escalate into enterprise-wide problems, particularly as organizations expand their cloud deployments and integrate AI functionalities.

The survey highlights that 56% of respondents find critical production security issues in the Java ecosystem on a daily or weekly basis. These issues encompass vulnerabilities in Java applications, libraries, frameworks, and supporting infrastructure. For many organizations, addressing these vulnerabilities has become a routine part of their DevOps workflows, driven by constant disclosures and ongoing scanning.

False positives are a significant challenge, with 30% of respondents indicating that their DevOps teams spend over half their time managing these alerts related to JVM-based workloads. Such false positives arise from various sources, including dependency scanners and misclassified vulnerabilities, leading to productivity issues that slow down patching priorities and remediation cycles.

Another concern is the presence of dead or unused code, which affects DevOps productivity for 63% of respondents. This unused code increases the attack surface by introducing additional dependencies and outdated libraries, complicating incident response and vulnerability remediation efforts.

Concerns about Oracle Java pricing are also prevalent, with 92% of respondents expressing worries. Many organizations are migrating to non-Oracle OpenJDK distributions, which can lead to operational disruptions and version sprawl, complicating vulnerability management and compliance audits.

The rise of AI in code generation adds another layer of complexity, as 30% of respondents reported that over half of their new Java application code is created by AI tools. This trend raises security questions regarding code provenance and the potential introduction of insecure patterns, emphasizing the need for enhanced runtime monitoring and vulnerability detection.

Implications for Security Practices

The findings from the Azul survey reveal critical insights into the current cyber risks associated with Java environments. Organizations should prioritize monitoring for vulnerabilities and consider implementing more robust scanning tools to reduce the impact of false positives. Additionally, regular code audits and the removal of dead code can help minimize the attack surface.

As companies navigate the complexities of migrating to non-Oracle distributions, they should ensure that their patching workflows and support models are well-defined to avoid operational disruptions. The integration of AI tools in coding practices necessitates a thorough review process to ensure that generated code meets security standards before deployment.