Handling sensitive school emails with AI

Working with LLMs or AI chat tools? You’re probably leaking user data! Here’s the privacy hole no one’s talking about. When users interact with AI apps, they often share sensitive information like names, emails, internal identifiers, and even health records. Most apps send this raw data directly to the model. That means PII ends up in logs, audit trails, or third-party APIs. It’s a silent risk sitting in every prompt. Masking data sounds like a fix, but it often breaks the prompt or causes hallucinations. The model can’t reason properly if key context is missing. That’s where GPT Guard comes in. GPTGuard acts as a privacy layer that enables secure use of LLMs without ever exposing sensitive data to public models. Here's how it works: 1. PII Detection and Masking Every prompt is scanned for sensitive information using a mix of regex, heuristics, and AI models. Masking is handled through Protecto’s tokenization API, which replaces sensitive fields with format-preserving placeholders. This ensures nothing identifiable reaches the LLM. 2. Understanding Masked Inputs GPT Guard uses a fine-tuned OpenAI model that understands masked data. It preserves structure and type, so even a placeholder like `<PER>Token123</PER>` retains enough meaning for the LLM to respond naturally. The result: no hallucinations, no broken logic, just accurate answers with privacy intact. 3. Seamless Unmasking Once the LLM generates a reply, GPTGuard unmasks the tokens and returns a complete, readable response. The user never sees the masking — just the final answer with all original context restored. Key features: 🔍 Detects and masks sensitive data like PII, PHI, and internal identifiers from prompts and files 🚫 Prevents raw sensitive data from ever reaching the LLM 🔁 Unmasks the output so users still get a clear, readable response 🚀 Works with OpenAI, Claude, Gemini, Llama, DeepSeek, and other major LLMs 📄 Supports file uploads and secure chat with internal documents via RAG The best part? It works across cloud or on-prem, integrates cleanly with your existing workflows, and doesn't require custom fine-tuning or data pipelines.

Regex is fast. LLMs are smart. But when it comes to 𝘀𝗰𝗿𝘂𝗯𝗯𝗶𝗻𝗴 𝗣𝗜𝗜, neither is perfect on its own. Regex shines on well-defined patterns: 📧 Emails → caught 📞 Phone numbers → caught 💳 Credit cards → caught But then it stumbles: 👤 Names? Missed. 🌀 Obfuscated text (john[dot]doe)? Missed. LLM-powered agents (like those built with the IBM BeeAI Framework) step in with context: They can understand obfuscations, spot names, and adapt to messy real-world inputs. ⚠️ But there’s a catch → latency, compute, and (most critically) privacy risks if you’re sending sensitive data to an external API. 𝗧𝗵𝗲 𝗸𝗲𝘆? A hybrid approach: ✔️ Regex for speed and structure. ✔️ BeeAI agents (running locally) for context and flexibility. And here’s the bonus: you don’t need a massive 70B model running in the cloud. Smaller, locally run models with BeeAI — plus the right prompting — are often enough to keep your data private and get the job done. Think of it like a two-layer defense system:  1. Regex = firewall (fast, obvious blocks).  2. BeeAI agent = human inspector (context-aware, nuanced). Together → robust, privacy-preserving, real-world ready. I’ve broken this down in detail (with code, results, and benchmarks) in my latest blog. [link in comments] 👉 If you’re building systems that handle user data, this is one you can’t ignore. ----------------------------- Find me → Aakriti Aggarwal  ✔️ I build & teach stuff around LLMs, AI Agents, RAGs & Machine Learning!

3 quick DLP wins to prevent sensitive data from getting exposed to AI chatbots like DeepSeek: 1. Block sensitive uploads before they happen with data lineage. - Block files based on their origin, e.g. downloaded from highly sensitive SaaS applications. - Block files based on their destination: Allow uploads only to sanctioned locations. - Combine origin and destination: Create sanctioned data flows by controlling where files came from and where they’re headed. 2. Educate teams on AI data risks in real-time. - Notify users about unsanctioned data sharing: Send real-time notifications via Slack or email to inform users about file transfers that are in violation of company policies. - Deliver custom educational content: Educate users on best practices for data handling and promote better stewardship of sensitive data. - Collect feedback from users: Automate business justification and false positive input collection directly from users to better understand context and adjust policies accordingly. 3. Monitor AI usage by combining data classification and lineage. - Monitor transfer of sensitive content types: Detect and monitor file transfers containing regulated content types such as PCI, PII, and PHI in addition to secrets, credentials, document types, and custom detectors. - Enable dynamic policies: Integrate content inspection with lineage and user/group-based policies for highly customizable and context-aware monitoring. Learn more about you can do this with Nightfall AI on our blog: https://buff.ly/3EyxYZw