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DLP solutions have a challenge in detecting standard document types: financial records, source code, and customer lists. Moreover, what happens when your organization needs to protect business-critical documents that don't fit pre-built categories? Or when you need more granular classification to support specific workflows?
Traditional approaches force you to choose between brittle regex patterns that generate false positives.
Our short demo video shows how you can create prompt-based detectors to address this use-case effectively:
Read on for a deeper dive into why this matters so much for DLP and how to get started with Nightfall AI.
The Document Classification Challenge
A mortgage lender processes thousands of financial documents daily. Their traditional DLP solution detects "financial documents" broadly to catch mortgage applications, business loan forms, and grant applications. But their compliance workflow requires distinguishing completed mortgage applications from other financial paperwork.
This specific problem is common across industries:
- Healthcare organizations need to separate clinical trial documents from general medical records.
- Legal teams need to identify specific contract types among thousands of agreements.
- Financial services need to distinguish between document types that share similar data elements.
Standard file classifiers work at the category level. Business processes often require document type precision.
A Prompt-Based Approach to File Classification
Modern DLP platforms can leverage AI to create custom file classifiers without regex engineering or model training. Here's how the process works:
1. Define Classification Intent
Start by clearly articulating what you're looking for. For a mortgage application classifier, you might specify:
"A completed mortgage application document containing borrower information, personal details, employment history, income verification, and asset information. Used in the mortgage origination process. Should not include finalized loan agreements or closing documents."
This natural language description establishes classification boundaries that distinguish your target documents from similar types.
2. Provide Classification Signals
Add keywords that commonly appear in your target document type. These serve as additional signals to improve classification accuracy without requiring exhaustive pattern matching.
3. Supply a Reference Document
Upload a sanitized sample that reflects the structure and content patterns of documents you want to detect. This gives the AI a concrete reference for the document format.
4. Validate Before Deployment
Test your classifier configuration against sample documents before activating it in production policies. This verification step ensures the classifier understands your requirements and can distinguish target documents from look-alikes with high confidence.
Real-World Performance
Once configured and added to a DLP policy, the custom classifier operates alongside standard detectors. When users share files:
- Target documents trigger both the custom classifier and relevant category detectors (e.g., "Mortgage Application" + "Financial Document")
- Similar documents trigger only category detectors (e.g., business loan applications flag as "Financial Document" but not "Mortgage Application")
- Unrelated documents pass through without false positives
This layered detection approach gives security teams the granularity they need for workflow-specific controls while maintaining broad protection across document categories.
Level Up Your DLP Strategy
Custom file classification capabilities fundamentally change how organizations approach data protection:
Eliminates regex maintenance burden. No brittle pattern libraries to update when document formats change
Reduces false positives. Intent-based classification distinguishes between legitimately similar document types
Enables non-technical configuration. Security teams can create classifiers without data science or engineering resources
Supports specific workflows. Granular classification enables process-specific controls beyond broad category blocking
Scales with business needs. New document types can be protected as workflows evolve without vendor dependencies
Implementation Considerations
When implementing custom file classifiers in your DLP strategy:
- Start with high-value document types that standard classifiers miss or misclassify
- Provide clear classification criteria that distinguish your target from similar documents
- Test against both positive samples and likely false positives before production deployment
- Combine custom classifiers with standard detectors for defense-in-depth
- Review detection logs regularly to refine classification criteria as needed
Moving Beyond One-Size-Fits-All DLP
Every organization has unique intellectual property and compliance requirements. Your DLP solution should protect your specific business-critical documents—not just standard categories that work across industries.
Prompt-based file classification makes this possible without the traditional tradeoffs between specificity and operational overhead. Security teams can protect what matters most to their business, defined in their own terms, without becoming regex engineers or hiring data scientists.
More precise protection, fewer false positives, and DLP controls that actually align with how your business operates is just the beginning.
Schedule a demo with Nightfall's team to see how prompt-based file classifiers can extend your DLP strategy beyond standard categories—without regex patterns or model training. Our team will walk you through:
- Creating custom classifiers for your specific document types
- Integrating detectors into your existing workflows
- Deploying policies across your SaaS and cloud environments
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