In an era where AI is becoming integral to enterprise operations, the adoption of large language models (LLMs) is accelerating. Many organizations are choosing to deploy these models locally to gain greater control over sensitive data and maintain regulatory compliance. While local deployment can indeed offer stronger data sovereignty and minimize third-party exposure, it is not inherently secure. Local doesn’t always mean safe. Without rigorous evaluation and proper safeguards, locally deployed LLMs can introduce significant vulnerabilities. For IT leaders, this means that trusting the deployment location alone is not enough. A local model still requires the same—or even greater—levels of scrutiny, testing, and governance as any cloud-based system. Take decisive action now to secure and strengthen your local LLM deployments—because failing to address emerging risks today could leave your organization vulnerable to breaches, operational disruptions, and reputational damage tomorrow.
Why Action is Needed
Local …