Open‑source technology—particularly in artificial intelligence—refers to software whose source code (or model weights) can be freely used, studied, modified, and shared under a licence that permits unrestricted commercial use, modification, distribution, and access to training data (OSI definition). The appeal lies in autonomy, cost savings and privacy: teams can run models locally and avoid per‑token API fees, and they can customise systems to their needs. However, the shift introduces concrete trade‑offs and risks. Licensing can be complex; many "open" models carry custom commercial restrictions that fall short of true OSI compliance, creating legal exposure for downstream users. Security hazards include poisoned weights and back‑doors uploaded to public repos, and unsafe serialization formats like Pickle that allow remote code execution—issues mitigated by newer formats such as Safetensors. Data‑leakage and IP concerns arise because training data provenance is often unclear, with many models trained on copyrighted web‑scraped material, posing liability for users. Operationally, running large models demands substantial GPU investment (V100, A100, H100, B200) and ongoing maintenance, which can overwhelm teams that underestimate the infrastructure burden. Because of these challenges, robust governance is essential: organizations must adopt processes that track licensing compliance, security hardening, and operational monitoring, treating governance as the set of actions and processes that create stable, reliable practice. In sum, open‑source technology offers significant benefits but requires careful risk management, clear licensing diligence, and strong governance frameworks to realize its potential without exposing organizations to legal, security, or operational pitfalls. [1] [2]