TL;DR: As organizations scale their AI initiatives in 2026, protecting sensitive information in non-text formats, such as PDFs and video transcripts, is the new frontline for compliance . Converting support tickets into AI knowledge bases risks exposing sensitive data hidden in attachments. PII/PHI scrubbing for AI solves this by using OCR and NER to sanitize PDFs and video transcripts before indexing. This “Privacy-by-Design” approach ensures compliance, preventing non-text breaches while keeping your GenAI secure.
2025 was the year that attracted the second-highest-ever number of financial penalties for resolving HIPAA cases.
Source: 2025 Healthcare Data Breach Report. The HIPAA Journal.
97% of organizations that fell victim to AI-related breaches cited a lack of robust access controls as a primary vulnerability.
Source: IBM Newsroom
Today, highly regulated industries such as Health Care, HealthTech, Legal, etc., offer some of the best use cases of Artificial Intelligence. But, losing money in litigation arising from data security violations, especially HIPAA violation is not unheard of either. A security and safety compliant AI search that secures data in all forms (text and non-text) could be the layer that’s missing in a considerable number of cases.
While AI-powered search and agentic AI offer unrivaled efficiency and efficacy in navigating vast knowledge bases, the risk of data breach, specifically the accidental exposure of Personally Identifiable Information (PII) or Protected Health Information (PHI), is a constant reason for concern.
As omnichannel user experience becomes the norm, enterprise search, Agentic AI, Agent Helpers, etc., has access to all kinds of data integrated into the system as part of the knowledge base. Consequently, ensuring that non-text data is also safe becomes a critical action item. Enterprise AI data privacy is no longer optional.
So, when questions such as the ones below are posed…
- “Does the AI see the patient data inside our PDF attachments?”
- “Can it also see data inside our video transcripts?”
- “If it does, how do we keep it from leaking?”
…the responses should be rather reassuring.
Let’s explore this in detail.
Table of Contents:
- Can AI Search Engines Read Sensitive Data in PDFs and Video Transcripts?
- Data Redaction is the Solution
- How does SearchUnify Handle Redaction for AI?
- How does the PII/PHI Scrubbing Engine Work?
- Why “Scrubbing” Beats “Encryption Only”
- Conclusion: Is your Non-Text Data Safe with AI?
- FAQ
Can AI Search Engines Read Sensitive Data in PDFs and Video Transcripts?
The generic answer to this question is “Yes.” And that’s what puts all the pain in this point. AI “sees” everything inside a PDF or an audio/video transcript. As AI systems move toward multimodal capabilities (processing images, audio, and video), the “surface area” for a breach expands into much more complex, non-textual territory.
- PDF Attachments: Medical records, legal briefs, and insurance claims often live in static PDF formats. These can also sometimes be scanned images.
- Video Transcripts: With the rise of telehealth and recorded legal depositions (often stored via integrations like Vimeo), dialogue is converted into text for searchability.
Non-Text Data Redaction for AI Is the Solution
Data redaction is the essential process of identifying and permanently removing sensitive information from documents, images, or videos to ensure privacy and regulatory compliance. As enterprises increasingly rely on multi-orchestration agents to handle complex workflows, the ability to automatically “scrub” data, specifically Personally Identifiable Information (PII) and Protected Health Information (PHI), is critical to preventing data breaches and legal penalties.
Personally Identifiable Information (PII) Redaction
PII redaction involves concealing any data that can be used to distinguish or trace an individual‘s identity. This is generally categorized into two types: Sensitive PII, such as Social Security numbers, bank account details, and biometric records; and Non-Sensitive PII, such as ZIP codes or birthdates. While non-sensitive data may seem harmless, it can often identify a person when combined with other datasets.
Redaction software uses AI and pattern matching to irreversibly obscure these elements before files are shared for legal discovery, public records (FOIA), or internal audits. This process ensures that organizations comply with global standards like GDPR and CCPA while maintaining customer trust.
Protected Health Information (PHI) Redaction
PHI redaction is a specialized form of data protection tailored for the healthcare industry to meet HIPAA compliance. Under the HIPAA Privacy Rule, PHI includes 18 specific identifiers, such as medical record numbers, dates of service, and full-face photos, held by covered entities. Redaction allows healthcare providers to share clinical data for research, insurance reviews, or training without exposing a patient’s identity.
Effective PHI redaction must be permanent, removing not only visible text but also hidden metadata and revision history. By de-identifying this data, organizations can support high-quality public health research while strictly safeguarding patient confidentiality.
How does SearchUnify Handle Redaction for AI?
SearchUnify addresses the non-text data breach risk through a robust, multimodal, multi-layered “Scrubbing Protocol” that operates during the data ingestion and indexing phases. The patient/client data is sanitized before it ever reaches the index or an LLM.
SearchUnify’s governance layer ensures that it masks PII and PCI data according to your organization’s specific requirements before engaging any third-party LLMs. This ensures confidentiality while still allowing the AI to leverage relevant context for generating responses.
Deep Scanning the PDFs
PDFs can be quite the “black boxes when it comes to data security.” SearchUnify utilizes advanced Optical Character Recognition (OCR) and Intelligent Document Processing (IDP) to treat PDFs as more than just blobs of text.
- Layer 1
Text Extraction & Normalization: Our crawlers extract text from both native and scanned PDFs.
- Layer 2
PII/PHI Identification: During the extraction phase, our ML models scan for patterns (Regex) and entities (NLP) such as patient names, Medicare ID numbers, and home addresses.
- Layer 3
Permanent Redaction at the Index: Once sensitive entities are identified, they are “scrubbed” using a process called Pseudonymization or Tokenization. The sensitive data is replaced with a generic label (e.g., [PATIENT_ID_REDACTED]). This ensures that even if a search index was theoretically compromised, the sensitive data is physically absent from the indexed record.
Scanning the Video Transcript Workflow
Video content is a primary focus for modern legal and healthcare consultation and even documentation. Through video integration features, SearchUnify automates the security of audio-visual assets as well.
- Automated Transcription: SearchUnify triggers a secure transcription process for any video uploaded to connected channels.
- AI Data Masking: Just as with text, the resulting transcripts are passed through our Sensitive Data Masking engine. If a telehealth session recording contains a mention of a rare medical condition linked to a specific name, the engine identifies the proximity of these terms and redacts them.
- Metadata Sanitization: It’s not just the transcript. We also scrub the video’s metadata such as the titles, descriptions, and tag to ensure no PII/PHI is leaked through the organizational structure of the video library.
How does the PII/PHI Scrubbing Engine Work?
SearchUnify’s zero-leak security architecture relies on a “Privacy-by-Design” framework. Here is the technical breakdown of how we handle non-text redaction:
- Pattern-Based vs. Context-Aware Detection
Most tools use basic Regular Expressions (Regex) to find social security numbers. SearchUnify goes further by using Named Entity Recognition (NER).
- Regex: Finds XXX-XX-XXXX.
- NER: Understands that in the sentence “The patient was seen by Dr. Smith,” ‘Smith’ is a person and ‘patient’ is a role, requiring different levels of masking depending on your industry-specific compliance rules.
- The “Semantic Encoder” & LLM Gateway
When SearchUnify is used to power Generative AI (SearchUnifyGPT™), we implement a Response Refiner and a Sensitive Data Removal layer.
Before a query is sent to a Large Language Model (like GPT-4), the “Beyond Text” engine scrubs the context window. If the search results pulled from a PDF contain sensitive data that escaped initial detection, the LLM Gateway catches it in real-time, replacing it with tokens before the data ever leaves your secure environment.
- Role-Based Access Control (RBAC) at the Source
SearchUnify respects the “source of truth.” If a legal clerk doesn’t have permission to view a specific PDF in SharePoint or any other integrated platform, SearchUnify’s Permission Syncing ensures that neither the document nor any redacted version of it ever appears in their search results. The principle is to mirror the security of your original content sources (Vimeo, Jira, Zendesk, etc.) in real-time.
Why “Scrubbing” Beats “Encryption Only”
Many vendors claim security because their data is “encrypted at rest.” While SearchUnify uses AES-256 encryption and TLS 1.3, encryption alone doesn’t prevent an authorized user from seeing data they shouldn’t see within a document.
Redaction is active security. By scrubbing the data inside the PDF or transcript, SearchUnify ensures that the AI only “knows” what it needs to know to be helpful, without ever “seeing” what makes you vulnerable.
Conclusion: Is your Non-Text Data Safe with AI?
In the HealthTech and Legal worlds, a single leak is a catastrophic event. By moving the security focus “Beyond Text,” SearchUnify allows organizations to finally unlock the value stored in their PDF archives and video libraries.
Our scrubbing protocol ensures that:
- PDFs are searchable but sanitized.
- Video Transcripts are indexed but private.
- AI Agents are intelligent but compliant.
With SearchUnify, you don’t have to choose between the power of AI and the safety of your patients or clients. You can have both. Our entire suite of products is built with data privacy and security at its core
Don’t let hidden PII in PDFs or transcripts become your next compliance headache. Speak with one of our experts today to see how SearchUnify’s can bulletproof your data strategy.
FAQ
- How is scrubbing different from standard data encryption?
While standard encryption protects data by simply locking it behind a key. However, authorized users have access to all that data once the file is opened. Scrubbing or redaction, on the other hand, permanently masks or removes specific sensitive details (PII/PHI) within the file itself. This ensures that even authorized users, including AI, do not see any sensitive information at all.
- Does redaction make the case data less accurate?
Not at all. The processes of Pseudonymization or Tokenization replace sensitive data with generic labels like [PATIENT_ID_REDACTED]. This helps the AI understand the context and relationship of the data while still hiding personal sensitive information. Thus, results are still accurate.
- How does SearchUnify ensure HIPAA compliance when interacting with third-party LLMs?
We implement a Sensitive Data Removal layer at the LLM gateway. Our engine scrubs the data in real-time before any query or context window is sent to an external LLM. Your sensitive data never leaves your secure environment, thus maintaining strict compliance with not only with HIPAA but also GDPR.
- Can I customize what specific types of PII/PHI are redacted?
Yes, such details are customizable. SearchUnify’s governance layer allows enterprises to define specific requirements for redaction. You may choose to redact sensitive PII such Social Security numbers while retaining non-sensitive PII such as ZIP codes, in case the latter is needed for internal analytics or regional health records.
