What security frameworks should enterprises adopt when deploying large‑language‑model APIs internally?

• Use the OWASP Top 10 for LLM Applications as a core risk framework to mitigate prompt injection, training‑data poisoning, model theft, and supply‑chain vulnerabilities. [2]
• Apply a full‑stack security model that protects the LLM, its data pipelines, underlying infrastructure, and API interfaces, incorporating zero‑trust network controls and API security gateways. [2][3]
• Deploy web‑application and API protections (WAF, bot management, DDoS mitigation, SSL/TLS orchestration) and enforce least‑privilege, zero‑trust access to LLM API endpoints. [3]
• Implement continuous AI‑agent governance, including agent visibility, prompt security, and third‑party risk monitoring to detect shadow AI and data leakage. [4]
• Add model‑level safeguards such as input validation, output filtering, and secure model‑serving pipelines to defend against data poisoning and model extraction attacks. [1]

Bottom line: Enterprises should combine OWASP LLM guidance with zero‑trust, full‑stack API and data‑pipeline protections to secure internal LLM API deployments.

Sources

1. Comprehensive Guide to Large Language Model (LLM) Security | Lakera – Protecting AI teams that disrupt the world.
2. LLM Security: Protecting Models, RAG & Data Pipelines | Wiz
3. Large language model security
4. What Is LLM Security? How Large Models Introduce Enterprise Risk