Prompt Sanitization: 5 Steps for Protecting Data Privacy in AI Apps

As Generative AI (GenAI) and Large Language Models (LLMs) become integral to modern apps, we face a critical challenge of protecting sensitive user data from inadvertent exposure. In this article, we’ll explore the importance of content filtering in LLM-powered apps, and provide strategies for its implementation.

Looking for step-by-step tutorials on prompt sanitization for OpenAI, Langchain, Anthropic, and more? Skip down to the “Tutorials & further learning” section below.

What are the privacy risks of LLM apps?

Imagine this: you’re in a bustling coffee shop, discussing your latest business idea with a colleague. The conversation is rich with details—numbers, strategies, and confidential plans. But what if someone at the next table is recording everything? Now, think of your AI or LLM system as that person at the next table, inadvertently capturing sensitive data that should never have been exposed. This is the risk we face when sensitive data, like assets with personally identifiable information (PII) or protected health information (PHI), slips into the inputs of these systems.

This risk is categorized as OWASP LLM06, or sensitive information disclosure. Here are a few real-world examples of how sensitive information can been unintentionally disclosed with AI:

1. Customer support chatbots

Risk: Meet Sarah. She had trouble with her new smartwatch and turned to AI customer support for help. In her frustration, she blurted out her full name, address, and credit card number. The AI, being the perfect listener it is, absorbed all this information like a sponge. Sarah's details could end up in places she never intended without proper filtering.
Consequence: This is like whispering your deepest secret to someone who accidentally repeats it later—only in this case, the “someone” is your AI provider, and the consequence could be a severe breach of user trust.

2. Healthcare apps

Risk: Meet Bob, a 45-year-old father of two who's been feeling under the weather lately. He decides to use his healthcare provider's new AI-powered chatbot. The bot is designed to help with insurance appointment questions. Bob shares his symptoms and asks for advice.
Consequence: This conversation includes PHI. This sensitive data might be exposed to unintended parties without proper filtering, which constitutes a HIPAA violation. It’s akin to a doctor leaving a patient’s file open on a busy reception desk—private information is at risk, and the repercussions are serious.

What are 5 steps to implement content filtering for your AI apps?

Just as you’d take steps to secure your home with locks and alarms, you should also implement robust content filtering to protect sensitive data in AI-powered apps. Here’s how you can do it:

1. ML-based filtering: Advanced Machine Learning (ML) models act like vigilant bodyguards, and are trained to identify and redact sensitive information before it gets to the AI system. These models catch nuances that traditional methods might miss. They also minimize false positives, so you’re not flooded with notifications.

2. Tokenization and masking: Before sensitive data reaches the AI, it’s disguised; it’s either turned into tokens or masked entirely. This ensures that an AI app doesn’t inadvertently learn something that it shouldn’t.

3. User prompts and controls: Just as guardrails keep drivers on the road, user prompts and controls guide users to share only what’s necessary. For instance, an AI app might send users a warning or an option to review data before submitting a prompt so that they can avoid sharing sensitive information in the first place.

4. Audit logging: Detailed logs of filtering actions act like a security camera, recording what happens for later review. This ensures accountability and helps in audits and compliance checks.

5. Continuous model improvement: AI threats are always evolving—and so should your defenses. By regularly updating and retraining ML models, you can ensure that your filtering systems stay sharp and ready to handle new patterns of sensitive information.

What are best practices for LLM app security?

Think of these best practices as the foundation of a secure digital fortress:

Data minimization: Just as you wouldn’t bring unnecessary valuables on a trip, don’t send unnecessary data to LLM systems. Design your prompts and application flow to require only essential information.
Secure API integration: Use secure API practices, like locking your digital doors and windows, to protect data in transit.
Output sanitization: Ensure that what the AI sends back is safe, like double-checking the contents of a letter before sending it.
Regular security audits: Conduct thorough reviews to ensure your fortress remains secure, from the ground up.
Compliance alignment: Ensure your security strategies align with the laws and regulations relevant to your domain, like building codes that ensure safety and compliance.

How do I build trust in an AI-driven world?

As LLM technologies become more prevalent in app development, it’s important to protect your company and customer data with content filtering. By implementing comprehensive filtering strategies, you can harness the power of LLMs while maintaining the highest data privacy and security standards.

Remember, the goal is to create AI-powered apps that users can trust with their sensitive information. Proactive security measures like content filtering are key to building and maintaining that trust in the AI-driven future of software development.

A better way to protect your data: Nightfall AI

While it’s possible to implement content filtering in house, it often demands substantial resources and specialized expertise. For many organizations, a more efficient and effective approach may be to leverage specialized API solutions, which can deliver superior results without the burden of managing complex ML models internally.

Nightfall's cutting-edge content filtering APIs stand at the forefront of protecting LLM apps against sensitive data exposure. Our solution doesn't just match the competition—it surpasses the competition. At a glance, Nightfall offers:

Double the accuracy in detection to ensure that no sensitive data slips through the cracks.
4x fewer false positives to maintain a seamless user experience—without unnecessary interruptions.
4x lower cost of ownership so that your team can focus on innovation.

By integrating Nightfall's API into your LLM-powered apps, you're not just implementing a security measure—you're adopting a comprehensive data protection strategy. Our solution ensures:

Top-tier protection for your users' sensitive information
Streamlined compliance with stringent data protection regulations
Positioning at the vanguard of AI security best practices

Security may seem like a never-ending game of whack-a-mole, but innovation is essential to stay ahead of evolving threats. With Nightfall, you're not just keeping pace; you're setting the standard for responsible and secure AI app development.

Tutorials & further learning

Curious to try Nightfall for yourself? Give these tutorials a try.