Have you ever wondered how to get the most from generative AI models built into your cybersecurity tools? While there is a lot to be said for well-trained, secure, and transparent models, the impact of effective prompt writing cannot be underestimated. Here at Swimlane, we are fortunate to have a data science team that plays around with AI models for a living. It’s safe to say that we’ve learned a thing or two while building and testing Hero AI, our collection of AI-enhanced features, and private LLM. In this blog post, we’ll share some best practices for AI prompt engineering. 

5 Things to Know about AI Prompts for Cybersecurity

1. Understanding Prompts

At its core, a prompt is a set of instructions or inputs that guide an AI model’s response. Think of it as a conversation starter. By providing clear and concise prompts, you help the AI understand your intent and generate accurate results.

2. Leverage Few-Shot Learning

One powerful technique is few-shot learning. By providing the model with a few examples of the desired output format, you demonstrate the pattern you want it to follow. This approach helps the AI grasp your expectations and produce consistent results.

For example, a prompt using the few-shot learning technique may look like: 

1. Prompt: Translate the post-incident event report from English to French. 

This technique is helpful for enterprise stakeholders, or MSSP clients, who are not native English speakers. 

3. Embracing Chain of Thought

For complex problem-solving and logical reasoning, a chain of thought prompting is your ally. Encourage the model to break down the problem into intermediate steps, leading to a well-structured and comprehensive solution.

Here is an example of an AI prompt for cybersecurity tools that embrace the chain of thought technique. 

• Prompt: How should we address this computer we found with BugSleep malware? Please give us 5 discrete steps to remediate and prevent the malware from returning.

4. Utilizing Prompt Templates

Streamline your prompt creation process with pre-defined templates. These structures offer a consistent framework for different tasks, saving you time and ensuring uniformity in your outputs.

An example of a prompt that utilizes templates may look like:

• Prompt: Please write a summary of this case for our CISO. Please include the following sections:
  • Title: [Short title]
  • Summary: [One paragraph summary of the incident]
  • Outcomes: [One paragraph describing what we did to address the case and what state the system is in now.]
  • Details: [A bulleted list of details from the case including dates, user details, computer details, etc.]

5. Combining Techniques for Optimal Results

Don’t hesitate to mix and match these techniques. For instance, you can incorporate few-shot learning within a prompt template or integrate a chain of thought steps into a complex query. The possibilities are endless!

Tips for Crafting Effective Prompts

• Be Specific: Clearly articulate your task and desired outcome.
• Provide Context: Offer relevant background information to guide the AI’s understanding.
• Use Clear Language: Avoid ambiguity and complex wording.
• Iterate and Refine: Experiment with different prompt structures and learn from the results.

By mastering the art of prompt engineering, you can unlock the full potential of generative AI. Remember, practice makes perfect. So, start experimenting with these techniques today and witness the transformative power of well-crafted prompts. Check out our AI prompt primer resource here for more information about AI prompt best practices. Let me know if you have any other questions. Happy prompting!