Effective analysis of cybersecurity and threat intelligence data requires language models capable of interpreting specialized terminology, complex document structures, and the interplay between natural language and source code. Encoder-only transformer architectures provide efficient, context-aware representations that support critical tasks such as semantic search, technical entity extraction, and semantic analysis, which are essential for automated threat detection, incident triage, and vulnerability assessment. However, general-purpose language models often lack the domain adaptation necessary for high-precision performance in these contexts.We present SecureBERT 2.0, an enhanced encoder-based language model designed specifically for cybersecurity domain. Built on the ModernBERT architecture, SecureBERT 2.0 incorporates hierarchical encoding and improved long-context modeling, enabling effective processing of extended and heterogeneous documents, including threat reports and source code artifacts. Pretrained on a domain-specific corpus over thirteen times larger than its predecessor—comprising more than 13 billion text tokens and 53 million code tokens from diverse real-world sources, SecureBERT 2.0 achieves state-of-the-art performance across multiple cybersecurity benchmarks. Experimental results demonstrate substantial improvements in semantic search for threat intelligence, information retrieval, cybersecurity-focused named entity recognition, and code vulnerability detection.