At a recent OWASP event, I shared key factors I consider before integrating Large Language Models (LLMs) into the software development lifecycle.

Here's a quick rundown:

1. Risk Management: Avoid LLMs if the downside risk can't be easily recovered from.

2. LLM Model Choice: Select models based on your target domain and whether you need narrow or wide-ranging capabilities.

3. Input/Output Nature: Recognize that LLMs excel at human language tasks, especially converting intent to structured outputs. For code generation, know your chosen model's sweet spot.

4. Integration Approach: Weigh using live LLM inference in your application against using LLMs to build tools that your code can use in a workflow (possibly after intent decoding by an LLM).

5. Implementation: Plan for guardrails and manage code complexity.

6. Expertise: Evaluate both the LLM operator's and the model's expertise.

7. Deployment: Consider model hosting options and inference costs.

Each of these factors can significantly impact the security and effectiveness of your AI integration. I'll be diving deeper into these in future tips, as some can be quite nuanced.

LLMs can be powerful tools, but like any tool, they need to be wielded with care and understanding.

Which of these factors do you find most challenging when integrating LLMs into your security workflows?