Guardrails in AI systems are defensive security measures designed to keep chatbot interactions within safe and predefined boundaries, preventing misuse and malicious attacks. These mechanisms are essential in maintaining the integrity and security of AI applications.
However, in the era of sophisticated AI models like ChatGPT, GPT-4, and Gemini by Google, ensuring the right balance in these security measures is crucial. While they play a vital role in LLM security, overly strict guardrails can lead to unintended consequences, potentially costing businesses millions of dollars. Let’s explore how an overzealous approach to AI guardrails can be detrimental and why a balanced strategy is essential.
When Guardrails Work
Let’s start with a scenario where AI guardrails are functioning as intended. Imagine a conversation between an attacker and a well-guarded AI chatbot:
In this case, the guardrails successfully prevent the attacker from exploiting a potential vulnerability. This is an example of guardrails stopping a system prompt from leaking, showcasing how effective they can be in maintaining security and protecting against prompt injections.
When Guardrails Go Wrong
Now, consider a scenario where guardrails are too strict, leading to a poor user experience and potential revenue loss. Picture a regular user interacting with an AI insurance chatbot:
In this case, the guardrails trigger because the user used the phrase “Please ignore all my previous commands,” which is commonly found in prompt injection attempts. However, this user wasn’t being malicious — they simply made a typo. Overly aggressive guardrails like this can frustrate users, leading to bad UX and potentially losing customers.
Why 2% Matter
You might think a small percentage of over-aggressiveness in guardrails is no big deal. But let’s break down the numbers we discovered with aggressiveness levels on the Insurance chatbot using Microsoft guardrails:
With this, let’s do the math:
A mere 2% over-aggressiveness in rejecting prompts can translate to over $4 million in lost revenue annually for a chatbot averaging 20 requests per minute, with an average conversion rate of $20 per request.
The Cost of Overly Aggressive Guardrails
Some companies have opted out of using Microsoft’s default guardrails because they were too strict, causing bad user experiences and restricting chatbot features. This over-aggressiveness doesn’t just frustrate users — it hits the bottom line, generating significant monetary losses.
Running guardrails isn’t free, either. They require infrastructure to operate, and if you’re using commercial models, you need to factor in the cost of tokens. Ensuring a secure Large Language Model (LLM) comes with its own set of expenses.
Key Considerations for Guardrails
Conclusion
In the race to secure AI applications, the key is balance. Stricter guardrails aren’t necessarily better and can end up costing millions in lost revenue and frustrated users. Fine-tuning and continuously optimizing guardrails to strike the right balance between security and usability is essential. Remember, the goal is to keep your AI chatbot secure without sacrificing user experience or revenue.
