Walled Redact
Mask → Send →Unmask.
Automatically detect and mask PII, confidential data, and trade secrets across every modality - text, images, documents, code, and audio - before they reach any LLM. LLMs need context, not content.
Backed & Trusted By Industry Leaders
How Redact Works
Intercept
Employee sends a query containing sensitive data across any modality - text prompts, uploaded images, documents, code snippets, or audio files.
Detect & Classify
WalledAI's multi-modal NER engine identifies sensitive entities across text, embedded text in images, document content, code variables, and audio transcriptions.
Mask & Send
Sensitive tokens are replaced with synthetic placeholders across all formats. The LLM receives full context without any real private data, regardless of modality.
Unmask & Deliver
The LLM response is intercepted, placeholders are replaced with original values, and the complete answer is delivered - maintaining format fidelity.
End-to-End Architecture
Mask → Send → Unmask: your data never leaves your boundary in its original form
Multi-Modal Intelligence
Every Format. Every Modality.
Modalities Supported
Entity Types Detected
Languages Covered
Sensitive data doesn't only live in text prompts. It's in screenshots, uploaded documents, code repositories, and voice recordings. WalledAI deciphers and protects them all.
Text & Chat
Real-time detection and masking of PII, financial data, and trade secrets in text prompts, chat conversations, and email drafts.
Images & Screenshots
OCR-powered extraction and masking of sensitive data embedded in screenshots, scanned documents, whiteboard photos, and diagrams.
Documents & PDFs
Deep parsing of Word docs, PDFs, spreadsheets, and presentations - detecting sensitive data in headers, tables, footers, and metadata.
Source Code
Identifies API keys, credentials, database connection strings, internal URLs, and proprietary algorithms in code snippets and repositories.
Audio & Voice
Transcription-based detection for voice recordings and audio files - masking spoken PII, account numbers, and confidential details.
Structured Data
Handles JSON, XML, CSV, and API payloads - masking sensitive fields while preserving data structure for accurate LLM processing.
The Core Insight: Context, Not Content
LLMs don't need your actual customer names, account numbers, or proprietary data to generate useful responses. They need the structure and context of your query. When an employee asks "Draft a follow-up email to John Smith at Acme Corp about his $2.4M account renewal," the LLM doesn't need to know it's John Smith or $2.4M. It needs to know this is a follow-up, about a named person, at a named company, regarding a high-value renewal.
❌ Without Walled Redact
"Draft a follow-up email to John Smith at Acme Corp regarding his $2.4M account renewal, reference account #AC-4829."
✅ With Walled Redact
"Draft a follow-up email to [PERSON_1] at [ORG_1] regarding his [AMOUNT_1] account renewal, reference account [ID_1]."
The LLM generates an equally good email - and your employee sees the final output with all real values restored. Zero compromise on productivity. Zero data exposure.
LLMs need context, not content. Your data stays sovereign.
See how enterprises across regulated industries use Walled Redact to let every employee use AI safely.
Industry Use Cases
Every industry has sensitive data that employees need to work with using AI. Here's how Walled Redact protects each one.
Teams use the same policy engine to mask privileged legal data, protect manufacturing trade secrets, and mask confidential company data in growing organisations.
Financial Services
Scenario
A wealth advisor asks ChatGPT to draft a portfolio review letter for a high-net-worth client.
Risk
Account numbers, portfolio values, and client identity are sent to OpenAI's servers.
With Walled Redact
Walled Redact masks client name, account details, and financial figures. The LLM drafts the letter with placeholders. The advisor sees the final output with real data restored - OpenAI never sees a single real value.
Healthcare
Scenario
A clinician uses an AI assistant to summarise patient notes and suggest treatment protocols.
Risk
Protected Health Information (PHI) - patient names, diagnosis codes, lab results - leaves the hospital network.
With Walled Redact
Walled Redact masks all PHI before it reaches the LLM. The AI still provides medically useful summaries and protocol suggestions because it has the clinical context, just not the identity.
Legal
Scenario
A junior associate uses AI to review a merger agreement and flag unusual clauses.
Risk
Counterparty names, deal values, and privileged legal strategy are exposed to the LLM provider.
With Walled Redact
Party names, financial terms, and strategic clauses are masked. The AI identifies structural and legal issues without ever learning who the parties are or the deal value.
Enterprise HR
Scenario
HR uses an AI tool to draft performance improvement plans and compensation recommendations.
Risk
Employee names, performance ratings, salary data, and disciplinary records are sent externally.
With Walled Redact
All employee identifiers and compensation data are masked. HR gets AI-drafted documents with real data restored on return - no employee data leaves the corporate boundary.
Why Not Just Deploy Private LLMs?
We hear this question constantly from CISOs. The math simply doesn't work. Private LLM deployments are expensive, inflexible, and a liability in a fast-changing AI world.
❌ Private LLM Deployment
- ✗$500K–$2M+ annual GPU infrastructure costs
- ✗6–12 months to deploy and tune
- ✗Model obsolescence within 6–8 months
- ✗Dedicated ML engineering team required
- ✗Limited to one or two model families
- ✗No access to latest capabilities (GPT-5, Claude 4, etc.)
- ✗Ongoing maintenance, patching, and security burden
✅ WalledAI + Public LLMs
- Fraction of the cost - governance layer, not infrastructure
- Deploy in days, not months
- Always using the latest, most capable models
- No ML team required - policy-driven configuration
- Access GPT, Claude, Gemini, Llama, and more simultaneously
- Automatically benefit from model improvements
- WalledAI handles the security - you focus on productivity
"By the time you've deployed and tuned a private LLM, the next generation has already shipped."
The AI world moves too fast for private deployments. WalledAI lets you ride the wave of innovation while keeping your data firmly on-premise.
What Gets Protected
What each team can see, unmask, or export is governed by role-based redaction controls tied to your existing identity groups.
Customer Story
Software Company Building Banking Platforms Empowers 1,000 Engineers with AI - Without Exposing Client Data
Challenge
A 1,000-employee software company builds core banking platforms for major financial institutions across Southeast Asia. Their developers regularly work with production-like data - schema designs, API contracts, test datasets modelled on real customer records, and proprietary banking logic. Engineers wanted to use AI for code generation, debugging, and documentation, but every prompt risked leaking their banking clients' proprietary architectures and data structures to external LLMs.
Solution
Deployed Walled Redact across all developer workstations and CI/CD pipelines. Every AI query - whether from an IDE copilot, a ChatGPT session, or an internal AI tool - is intercepted. Client names, bank-specific schema details, account structures, API keys, and proprietary algorithms are automatically masked. Engineers get full AI assistance with synthetic placeholders. Responses are unmasked locally before reaching the developer.
Results
1,000 engineers adopted AI tools within 6 weeks. Three major banking clients independently audited the setup and approved continued use. Code review turnaround improved by 35%. Documentation generation - previously a bottleneck - became 4x faster. Zero instances of client IP reaching external LLMs.
Engineers Enabled
Faster Code Reviews
Documentation Speed
Client Data Exposure
Related Resources
WalledEval: A Comprehensive Safety Evaluation Toolkit
Our open-source framework for evaluating LLM safety guardrails - the research behind Walled Redact's detection models.
Read more about WalledEval: A Comprehensive Safety Evaluation ToolkitRead moreThe ROI of AI Governance
Why investing in a governance layer delivers 10x returns compared to restricting AI adoption or deploying private LLMs.
Read more about The ROI of AI GovernanceRead moreData Privacy in the Age of Agentic AI
As AI agents make autonomous decisions, the data exposure surface grows exponentially. How Walled Redact adapts.
Read more about Data Privacy in the Age of Agentic AIRead moreReady to let your teams use AI without risk?
See how Walled Redact masks sensitive data in real-time - request a personalized demo or start with a Proof of Concept.
Frequently Asked Questions
Authoritative references
Primary sources behind the standards, regulations and research referenced on this page.
- NIST SP 800-122 guide to protecting PIIUS guidance on identifying and safeguarding personally identifiable information.
- GDPR, Regulation (EU) 2016/679EU personal data protection law, including data protection by design.
- HHS HIPAA Privacy RuleUS rules governing use and disclosure of protected health information.
- NIST SP 800-53 Rev. 5 security and privacy controlsControl catalogue referenced by most enterprise compliance programmes.