How should companies evaluate the security and compliance risks of deploying large language models in internal tools?

• Perform a threat‑modeling exercise that maps data flows, model inputs/outputs, and integration points to identify attack surfaces such as prompt injection, model stealing, and data leakage [1][3].
• Assess data‑privacy and compliance implications by inventorying the training and fine‑tuning data, verifying consent, and ensuring adherence to regulations (e.g., GDPR, CCPA) [2][4].
• Evaluate model‑specific risks including bias propagation, hallucination‑driven misinformation, and susceptibility to adversarial prompts, using both static indicators (model cards, audit logs) and dynamic monitoring (runtime anomaly detection) [2][3].
• Implement governance controls: role‑based access, prompt‑and‑output logging, version‑controlled model deployment, and regular third‑party security assessments of the LLM supply chain [1][4].

Bottom line: Companies should adopt a continuous, integrated risk‑management framework that combines technical safeguards, policy governance, and compliance validation to securely deploy LLMs in internal tools.

Sources

1. Security Risks of Large Language Models in Enterprise: A Strategic 2026 Briefing - Dr. Daniel Glauber
2. Safety and Security Analysis of Large Language Models: Risk Profile and Harm Potential
3. Risk Assessment and Security Analysis of Large Language Models
4. What Is LLM Security? How Large Models Introduce Enterprise Risk