Building DLP for a ChatGPT World

Generative AI has gone from a novelty to an essential part of daily workflows across all teams at an organization. Whether it’s ChatGPT, Microsoft Copilot, Claude, or Google Gemini, employees are using chatbots to copy, paste, summarize, and query data at a pace and scale we have never seen before. 

Unfortunately, data security has not been a fundamental feature of generative AI as the technology’s popularity and functionality has exploded. Sensitive data can find its way into any gen AI tool simply by users copying and pasting things like API keys and secrets into the chat interface. From there, the data is exposed to the entire LLM model and can be subject to further queries and searches based on model and tool settings and permissions.

Legacy data protection tools can’t detect and protect sensitive data in chatbots and LLMs. Let’s take a look into where legacy data loss prevention (DLP) falls short and how AI-native solutions like Nightfall are built to meet the challenges of protecting sensitive data when using gen AI apps.

‍Legacy DLP Works Well, Until It Doesn’t

The previous generation of DLP (and tools based on the same technology) assume certain user behaviors apply across the board when using SaaS and endpoints:

Users worked from sanctioned devices

• Files were the only risk surface
• Policies were static, based on predictable user patterns

Chatbot risk follows the same patterns as shadow IT - as in no patterns, just chaos:

Sensitive content can be entered into text boxes as well as within uploaded files

• Users share PII in browser-based tools and client apps without understanding risks
• Gen AI tools store and retain all that data permanently 

Legacy DLP systems built on regex and rigid rules were designed to detect file transfers and keyword matches. It’s not enough to simply detect credit card numbers; modern DLP must stretch and adapt to catch when users turn to gen AI to help them work with sensitive data that can include proprietary information like a product roadmap, confidential content like customer contracts, or secrets within production error logs. 

Modern DLP Flexes to Meet Current (and Future) Security Needs

Most gen AI end users aren’t well-versed in data security. Shadow AI adds a security curveball to the mix by allowing users to move and share sensitive data anywhere that isn’t tracked or protected by established security applications and norms.

To protect data in the gen AI era, security solutions must cover:

• Clipboard transfers 
• Unmanaged device uploads
• AI-based SaaS apps to prevent sensitive data sharing via SlackGPT or Teams Copilot integrations 
• Client apps

Even the most secure and strict environments can’t maintain the same rules forever and expect the same results. Threats evolve quickly, and technology influences user behavior with every new app or platform to come out. A DLP solution should be a living element within the security stack—one that can adapt to the changes and challenges as fast as new threats emerge.

Nightfall Covers the Full Spectrum of DLP

Nightfall is designed to protect data, not just files, across SaaS, endpoints, browser, and chatbot environments. Legacy DLP solutions don’t offer the same range across attack surfaces nor have the depth to offer essentials like data lineage and context. In a nutshell, here’s how Nightfall outshines other DLP options:

• Chatbot monitoring detects and controls content pasted into gen AI tools
• Clipboard intelligence traces data lineage across copy/paste events
• Browser extensions track and block uploads, pastes, and form-based exfiltration
• No kernel drama ensures fast deployment and lightweight CPU usage

Of course, no system is perfect. Nightfall is built for that eventuality as well. Our platform generates rich contextual information of all security events, allows admins and users to automate actions when incidents occur, and educates users on the context of their behavior within each environment. Nightfall DLP delivers results within seconds, not hours, to support security teams as they build up data security and trust among users.

Gen AI Tools Are Evolving. Your DLP Should Too

If your current DLP doesn’t cover chatbots like ChatGPT, Claude, or Copilot, it’s not enough to protect your data in 2025 and beyond. DLP must be AI-native, context-rich, and built for how people actually work. See how Nightfall secures gen AI tools by scheduling a demo with us.