Building AI Apps: The Risks You Can’t Ignore

Building AI Apps: The Risks You Can’t Ignore
Generative AI is the shiny star in the tech universe, dazzling us with its ability to create, predict, and transform. But like any powerful tool, its brilliance comes with challenges. From chatbots conjuring up fictional policies to AI systems offering bizarrely incorrect advice, the potential for mishaps is very real.

How to Stay Secure Without Sacrificing Performance

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.

Why Are Guardrails for AI Apps So Important?

AI 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.

The Core Challenges of Securing AI Apps

Before looking into solutions, let’s look at why creating guardrails for GenAI is uniquely challenging.

  1. Nondeterministic Output
    Unlike traditional software, GenAI doesn’t guarantee consistent results. For example, asking the same question multiple times can yield different answers. This unpredictability makes it harder to enforce strict rules.
  2. Vulnerability to Prompt Injections
    GenAI systems are highly susceptible to malicious input. A cleverly worded prompt can manipulate the model into generating harmful or misleading responses. For instance, users have tricked AI systems into overriding initial instructions by embedding hidden commands.
  3. Multi-modal Input Risks
    The risks aren’t limited to text. AI models working with images, PDFs, or voice data can also be exploited. For example, job applicants have hidden text in tiny fonts or images to manipulate AI-based resume screening tools.
  4. Evolving Attack Vectors
    As AI evolves, so do the ways it can be exploited. What’s safe today might be vulnerable tomorrow, making it critical to stay one step ahead.

Defining AI Guardrails: What Are They?

AI Guardrails

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:

  • Generating inappropriate or offensive content
  • Teaching users illegal or harmful behavior
  • Misrepresenting your brand or policies
  • Leaking sensitive information
  • Costly misuse of the platform

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.

Strategies for Building Guardrails in AI Apps

There’s no one-size-fits-all approach to guardrails, but here are some of the most effective strategies:

1. Content Filtering

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:

  • Vulnerable to prompt injections
  • Limited to predefined categories
  • May miss edge cases

Despite its limitations, content filtering is a must-have for 99% of use cases.

2. Preventing Product Misuse

Stopping misuse requires going beyond simple prompts. Key techniques include:

  • Siloed infrastructure: Limit what AI systems can access.
  • Rate limiting: Cap how many requests a user can make in a given timeframe.
  • Output validation: Use a separate layer to review AI outputs before they reach the user.
  • User reputation tracking: Identify and restrict bad actors based on usage history.

These strategies borrow heavily from traditional software engineering principles, adapting them to the unique challenges of GenAI.

3. Human-in-the-Loop (HITL) Approaches

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.

4. Advanced Techniques with Vector Databases

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.

Balancing Security and Performance

Security and Performance

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:

  • Minimize API Calls: Reduce network latency by processing as much as possible within your app.
  • Improve User Experience: Add progress bars or “thinking” animations to reassure users during delays.
  • Set Timeouts: If your system is taking too long, default to safety with an “I don’t know” response.
  • Leverage Multi-modal Security: Have the option to switch between different AI models (e.g., GPT-4 to Claude 3.5) if vulnerabilities arise.
  • Rate Limit Users: Prevent excessive queries by capping how often users can access the system.

Ultimately, you’ll need to prioritize. For apps handling sensitive data, security should outweigh speed. For more casual applications, lightweight guardrails may suffice.

Why This Matters: Staying Ahead of the Curve

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.

Need Help Implementing Guardrails?

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!

Ventsi Todorov
Ventsi Todorov
Digital Marketing Manager
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