LLM Security Best Practices: Seven Steps to Secure AI Deployments

The core thesis is that securing LLMs isn't a new discipline — it's the same classic security methodology (risk assessment, threat modeling, controls, testing, monitoring, governance) applied to a component with a few genuinely new failure modes, particularly prompt injection where models can't reliably distinguish instructions from data

The guide uses a concrete example throughout: Acme Corp building a customer-support chatbot on Azure OpenAI with RAG knowledge retrieval and internal API access, representing the most common real-world case where organizations consume vendor models rather than training their own

The seven-step method walks through the full security lifecycle: (1) risk assessment to scope the system and inventory data, (2) threat modeling using frameworks like STRIDE and DREAD, (3) mapping threats to OWASP LLM Top 10, (4) building layered controls including gateways and guardrails, (5) testing by attacking your own system, (6) monitoring with logging and drift detection, and (7) governance with clear ownership and documentation

The most critical control is access control — the model must never reach data or actions the user isn't entitled to. Prompt-layer defenses like templates and guardrails reduce risk but aren't hard boundaries; deterministic authorization and output handling are what actually contain attacks

The article emphasizes that many LLM vulnerabilities (leaked system prompts, over-permissioned agents, prompt injection) are really familiar security problems (information disclosure, least privilege violations, trust boundary issues) appearing on a new surface with the added complexity of probabilistic output and natural-language instructions