Why input filtering? Well…
Traditional data loss prevention (DLP) systems were built for an era of emails, file uploads, and structured forms. But in today’s AI-first workplace, data is leaking in ways those systems were never designed to catch.
It’s not about files anymore. It’s about prompts.
Employees are typing, pasting, and uploading personally identifiable information (PII) into AI tools—often without realizing it. ChatGPT. Bard. Claude. Internal copilots. It’s fast, convenient, and dangerously invisible to most security teams.
This is where real-time input filtering comes in.
What Is Real-Time Input Filtering?
It’s the ability to detect and flag sensitive information—like PII—as it’s being entered into an AI tool, before it ever gets submitted.
Think of it as:
- Spellcheck, but for security
- Inline guidance, not after-the-fact alerts
- A way to nudge people in the moment, not just clean up the mess later
And why is it important to apply these filters now? Research from Lasso Security reports that 13% of generative AI prompts sent from enterprise environments contain sensitive data—ranging from proprietary code to network details and PII. This increases the attack surface leading to a data breach, which IBM has reported to cost about $4.88 million in 2024.
Why It’s Becoming Essential
1. AI Is Becoming Ubiquitous
Tools like ChatGPT aren’t niche anymore—they’re built into email clients, CRMs, note-taking apps, and browser extensions. That means prompt fields are everywhere.
2. PII Isn’t Always Obvious
It’s not just names and credit cards. Sensitive data now includes:
- Customer complaints
- Employee performance notes
- Health-related queries
- Copy-pasted snippets from internal systems
3. Existing DLP Can’t See It
Most legacy systems monitor file transfers, email attachments, or known data repositories. But they can’t detect what a user is typing into an AI chat window. By the time a prompt is submitted, the data has already left your perimeter.
According to Hacker News, 70% of modern data leaks occur directly in-browser, invisible to traditional endpoint or network-based DLP tools. Vectoredge emphasizes that traditional DLP is limited by static rule sets, lack of context, scalability issues, and poor ability to detect insider or browser-based misuse. And finally, Cyberhaven calls out legacy DLP’s inability to understand the context—like the evolution of data or the intent behind a user’s action.
What Real-Time Input Filtering Enables
- Prevention, not reaction
Flagging PII before it leaves the browser means fewer breaches to clean up after. - Context-aware nudges
Is this user allowed to upload that file to this tool? Filtering can adapt based on role, tool, or content type. - Behavioral reinforcement
When users see a nudge in real time, they learn what’s risky and change habits faster than with annual trainings.
Where This Is Headed
Expect to see more organizations building “pre-submit intelligence” into their AI usage layers:
- Browser extensions with lightweight prompt scanning
- Custom LLM guardrails that filter inputs for PII before sending to external APIs
- Role-based rulesets that adapt to use case and tool
Just as web browsers evolved from passive viewers to security enforcers (think pop-up blockers, HTTPS alerts, etc.), prompt interfaces are evolving too.
How Tripwire Supports This
Tripwire monitors browser-based AI use—including what users type into tools like ChatGPT, Claude, and Bard. When it detects sensitive patterns, it prompts the user with real-time, contextual guidance—before the data is submitted.
It’s not about surveillance. It’s about helping people pause, review, and choose safer actions.
Because protecting PII isn’t just about databases anymore.
It’s about every keystroke that feeds an AI.
Real-time input filtering is more than a feature.
It’s the next layer of modern data protection.
Want to see how we’re building it at Tripwire?
- Learn more about our solution through the post: Introducing Tripwire
- Apply for early access and get our exclusive whitepaper
- Or just follow along as we explore how to make AI use at work safer, smarter, and more human