Generative AI (GenAI) has revolutionized the tech landscape in record time. Just two years ago, chatbots were glorified “if-else” machines. Now, AI systems can hold fluid conversations, analyze images, and even mimic human creativity. Companies everywhere are rushing to add AI capabilities to their offerings—but with great power comes great responsibility.
As GenAI technology continues to mature, the risks of deploying these systems are becoming harder to ignore. From chatbots making up company policies to AI recommending illegal actions, the potential for things to go wrong is real. That’s where AI guardrails come in. If you're looking to build a GenAI application that performs reliably and safely, here’s what you need to know about implementing effective guardrails.
GenAI systems, powered by Large Language Models (LLMs), are fundamentally different from older rule-based AI. They don’t follow hard-coded instructions but instead generate outputs based on probabilistic pattern matching. While this makes them more versatile, it also introduces nondeterminism—meaning their responses can vary even when given the same input.
This flexibility makes GenAI apps susceptible to both errors and exploitation. Here are some real-world examples of what happens without proper guardrails:
These incidents range from amusing to alarming, but the consequences for your brand could be severe. Missteps can damage trust, cost you money, and even lead to legal action. Guardrails aren’t optional—they’re essential.
Before looking into solutions, let’s look at why creating guardrails for GenAI is uniquely challenging.
At their core, AI guardrails are mechanisms designed to prevent GenAI systems from going “off-script.” But what does “off-script” mean? It depends on your organization’s goals and risk tolerance. Common concerns include:
Guardrails aim to minimize these risks while preserving the AI’s utility. However, they’re not just about avoiding worst-case scenarios. Guardrails can also prevent general misuse, like users asking a product recommendation chatbot for irrelevant advice.
There’s no one-size-fits-all approach to guardrails, but here are some of the most effective strategies:
The easiest starting point is a content moderation API, such as OpenAI’s Moderation API. This tool flags inappropriate content based on categories like hate speech, violence, or harassment. While not foolproof, it’s a good baseline for many applications.
Downsides:
Despite its limitations, content filtering is a must-have for 99% of use cases.
Stopping misuse requires going beyond simple prompts. Key techniques include:
These strategies borrow heavily from traditional software engineering principles, adapting them to the unique challenges of GenAI.
For high-stakes applications, having a human review AI-generated outputs before they’re finalized can significantly reduce risks. While this slows down performance, it’s invaluable for sensitive use cases like medical advice or legal documentation.
A cutting-edge solution involves using vector databases to enhance guardrails. Vector databases convert text, images, or other data into numerical representations (vectors). By comparing user queries against stored vectors, you can detect whether a query aligns with off-limits topics.
This approach opens up new possibilities but also adds complexity. For example, you’ll need to manage the security of the vector database itself.
Implementing guardrails often comes with trade-offs. The more secure your system, the more likely you are to impact performance. Users won’t stick around if your AI app takes 90 seconds to process a query.
Here’s how to strike the right balance:
Ultimately, you’ll need to prioritize. For apps handling sensitive data, security should outweigh speed. For more casual applications, lightweight guardrails may suffice.
Building secure GenAI applications is a moving target. Attack vectors and vulnerabilities evolve almost as quickly as the technology itself. Companies like Apple and OpenAI have delayed releasing AI features to ensure proper guardrails are in place—proof that even the biggest players are grappling with this challenge.
Investing in guardrails isn’t just about protecting your business today; it’s about future-proofing against the unknown threats of tomorrow.
At NineTwoThree AI Studio, we’ve helped companies design enterprise-grade GenAI systems with built-in safeguards. Whether you’re launching an internal tool or a customer-facing product, we can help you navigate the complexities of balancing security and performance.
Let’s make your AI app safe, reliable, and ready for the future. Reach out to us today!