WalledAI
Customized
Overview
WalledAI offers an enterprise AI governance platform designed to enable the secure use of large language models (LLMs) within healthcare organizations. It is intended for various healthcare professionals, including clinicians, researchers, and administrators, across different care settings such as hospitals and clinics.
- What it does: WalledAI provides a control plane for masking sensitive data, monitoring AI interactions, and auditing AI usage. It aims to prevent Protected Health Information (PHI) from being exposed to LLMs by detecting and masking it in real-time across various modalities like text, images, and audio. The platform also includes content safety guardrails to filter out inappropriate content and block prompt injections, as well as hallucination detection to validate AI-generated responses against ground truth.
- Who it is for: The tool is for healthcare organizations seeking to integrate AI into their workflows while maintaining data privacy and regulatory compliance, particularly with HIPAA. This includes clinicians using AI for documentation, diagnostic research, and patient communication, as well as administrators for operational analytics.
- How it fits a clinical or practice workflow: WalledAI integrates into existing workflows by providing a governance layer that secures interactions with various AI assistants and SaaS applications. It allows healthcare teams to use their preferred AI tools while WalledAI operates in the background to enforce policies, mask sensitive data, and log interactions. The platform can be deployed on-premises, in a private cloud, or in air-gapped environments, ensuring data sovereignty.
- Notable capabilities: Key capabilities include real-time PHI detection and masking, content safety guardrails, hallucination detection, data classification, and granular access controls. It also features shadow AI detection, identifying and managing unsanctioned AI usage within the organization. WalledAI provides an audit-ready governance dashboard for compliance reporting.
Reviewed by Pouyan Golshani, MD — Interventional Radiologist
Key Features
- Walled Redact: Multi-modal PII/PHI masking (text, images, documents, code, audio)
- Walled Protect: Real-time content safety guardrails, prompt injection defense, toxic content filtering
- Walled Correct: Hallucination detection and validation against ground truth
- Data Classification: Automated sensitivity tagging
- Enterprise RBAC: Granular access controls for LLM usage and data sharing
- Governance Dashboard: Full visibility into AI usage, risk patterns, and audit-ready compliance
- Shadow AI Detection: Identifies and governs unsanctioned AI usage
- Sovereign Deployment: On-premises, private cloud, or air-gapped deployment with zero data egress
- Agentic AI Governance: Controls for autonomous AI agents
- End-to-end encryption at rest
Use Cases
- Safe AI use for clinicians (clinical documentation, diagnostic research, treatment planning, patient communication)
- Protecting sensitive data in financial services (e.g., loan processing)
- Governing proprietary code and client data in software development
- Ensuring compliance in government and critical infrastructure
- Securing customer support and success AI tools
- Managing AI risk and compliance in legal firms
What Physicians Need to Know
HIPAA-Compliant Infrastructure
WalledAI's architecture is designed to be HIPAA compliant by ensuring that Protected Health Information (PHI) is masked before it reaches any Large Language Model (LLM). It provides complete audit trails to satisfy HIPAA's access logging requirements. The platform can be deployed on-premise or in a sovereign cloud to meet cross-border data residency requirements.
Clinical NLP Capabilities
WalledAI utilizes NLP-native detection to identify PHI in natural language, including patient names, ages, locations, conditions, and combinations that could lead to re-identification. This allows clinicians to use AI for tasks like clinical documentation, diagnostic research, treatment planning, and patient communication while ensuring PHI is masked and medical outputs are validated against approved clinical databases to prevent hallucinations.
De-Identification Tools
WalledAI offers 'Walled Redact' which automatically detects and masks PII, confidential data, and trade secrets across various modalities (text, images, documents, code, audio) before they reach any LLM. The data is unmasked seamlessly upon return to the authenticated user.
Medical Terminology Support
WalledAI validates medical outputs against approved clinical databases to prevent dangerous hallucinations, implying a degree of medical terminology understanding and validation. Their NLP-native detection also identifies various clinical entities as part of PHI masking.
Sandbox/Testing Environment
WalledAI offers a free 2-week pilot program that provides full platform access (Walled Redact, Walled Protect, Walled Correct) and deployment in your environment (cloud, on-prem, or air-gapped) for up to 100,000 protected interactions. They also provide a free sandbox and onboarding program for partners, including a fully functional WalledAI platform instance for internal training, evaluation, and sales enablement with up to 50 internal user seats.
SDK Languages
WalledAI's WalledEval, a safety evaluation toolkit for LLMs, is primarily Python-based (73.7% Python). This suggests Python as a supported language for interacting with their tools.
Rate Limits & Pricing
WalledAI's pricing is customized based on deployment architecture, compliance requirements, and scale. They offer a 2-week pilot program starting at USD 499, which is credited towards an annual subscription if you choose to scale.
Certification Program
WalledAI addresses the governance pillar of Responsible AI, ensuring every clinical AI interaction meets HIPAA, FDA, and institutional safety standards. They also ship with pre-configured evidence mapping across various regulatory frameworks including EU AI Act, NIST AI RMF, UK GDPR, and India's DPDP Act, providing automated evidence generation for audit readiness.
Physician Tip
WalledAI allows clinicians to leverage AI for tasks like clinical documentation, diagnostic research, treatment planning, and patient communication without compromising patient privacy. The platform's ability to mask PHI in real-time and validate medical outputs against clinical databases helps prevent dangerous AI hallucinations, making it a safer tool for clinical use. This can help reduce administrative burden and improve care quality.
WalledAI acts as an enterprise AI governance layer that sits between employees and any LLM (e.g., ChatGPT, Claude, Gemini, Copilot, Perplexity, Grok, Mistral). It can be deployed across various SaaS platforms like Slack, Teams, Notion, Salesforce, GitHub, and Google Workspace, intercepting sensitive data before it leaves your boundary. It supports deployment on-premises, in private clouds (AWS, Azure, GCP), or air-gapped environments. WalledAI is backed by industry leaders like Amazon, NVIDIA, and Google.
Details
| Category | Developer Tools & APIs, Legal, Compliance & Security |
| Pricing |
Customized
|
| Deployment | On-premises, Private Cloud, Hybrid, Air-gapped |
| Compliance | |
| BAA Available | Unknown AI-estimated |
| HIPAA Compliant | Unknown AI-estimated |
| FDA Status |
Unknown AI-estimated WalledAI helps healthcare organizations meet FDA safety standards for reliability, privacy, and safety by providing pre-mapped compliance controls for FDA 21 CFR Part 11. |
| Integrations | |
| EHR | Not specified |
| Specialties | Family Medicine, Hospital Medicine, Internal Medicine |
Ratings & Reviews
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Press & Coverage
WalledAI
Artificial Intelligence News | AI Updates & Insights | WalledAI
WalledAI provides daily updates and insights on artificial intelligence and AI cybersecurity news, offering concise enterprise summaries from trusted sources.
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WalledAI
EU AI Act Compliance Guide: Ready by August 2026 | WalledAI
This article from WalledAI details the EU AI Act, which entered into force on August 1, 2024, and outlines the phased implementation schedule and compliance requirements for high-risk AI systems, with obligations taking effect on August 2, 2026.
2024-08
International Journal of AI, BigData, Computational and Management Studies (IJAIBDCMS)
Red Teaming AI Systems for Security Validation
This paper, published in March 2025, discusses the growing trend of AI in safety-critical domains like healthcare and finance, highlighting the unique vulnerabilities of AI models and the importance of red teaming for security validation.
2025-03
ResearchGate
A Systematic Literature Review on LLM Defenses Against Prompt Injection and Jailbreaking: Expanding NIST Taxonomy
This systematic literature review, published in August 2026, focuses on prompt injection mitigation in LLMs, providing a comprehensive catalog of defenses and guidelines for researchers and developers.
2026-08
MDPI
Semantic Firewalls with Online Ensemble Learning for Secure Agentic RAG Systems in Financial Chatbots
This February 2026 paper proposes a semantic firewall with meta-learning and continuous learning to secure agentic RAG systems in financial chatbots, identifying and blocking potentially malicious prompts.
2026-02
arXiv
RedBench: A Universal Dataset for Comprehensive Red Teaming of Large Language Models - arXiv
This April 2026 paper introduces RedBench, a universal dataset for comprehensive red teaming of large language models, emphasizing its role in supporting trustworthy deployment of LLMs in safety-critical domains like healthcare and finance.
2026-04
OpenReview
TOWARD RELIABLE, SAFE, AND SECURE LLMS FOR SCIENTIFIC APPLICATIONS - OpenReview
This research from 2025 discusses the vulnerabilities and safety concerns of LLMs in scientific applications, including medical LLMs, and highlights the need for robust safety alignment and safeguarding frameworks.
2025
WalledAI
WalledAI - Enterprise AI Governance Platform
WalledAI offers an enterprise AI governance platform designed for safely deploying LLMs by masking sensitive data, blocking prompt injections, and ensuring compliance with various regulatory regimes.
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Videos
Product demos, reviews, and walkthroughs for WalledAI.
DAY 3391: Walled AI
Keller
What is a Walled Garden? | fullthrottle.ai Rapid Fire
fullthrottleAI
WalledAI | SaaS Explainer video
Piximove Studios - Explainer Videos for SaaS & AI
Rishabh Bhardwaj (CEO, Walled AI) at TGB AI Village
The Generative Beings
WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models (EMNLP '24 Demo)
Prannaya Gupta
pov ultra realistic scene inside a large glass walled #ai #relax #lifeisbutadream #asmr #relaxing
ASMR VIDEOS
Frequently Asked Questions
WalledAI is a developer platform designed to help physicians build and deploy AI solutions securely within healthcare environments. It offers APIs and SDKs that allow integration with existing EHR systems and other clinical applications, enabling the development of custom AI tools for diagnostics, treatment planning, and administrative tasks.
WalledAI is built with a strong focus on data privacy and security, adhering to HIPAA and other relevant healthcare regulations. It employs robust encryption, access controls, and data anonymization techniques to protect patient information, ensuring that all AI models and applications developed on the platform meet stringent compliance standards.
WalledAI offers tiered pricing models for its developer tools, which may include a free tier for basic usage, a pay-as-you-go model for increased consumption, and enterprise plans for larger organizations. Specific costs for a physician or small practice would depend on the scale of AI development and deployment, with detailed pricing available upon request or on their platform.
While WalledAI supports a wide range of AI models and data types, there might be limitations regarding highly specialized or experimental AI architectures. Additionally, the platform's data handling capabilities, while robust, may have specific constraints on the volume or velocity of data processing depending on the chosen service tier.
Several alternatives to WalledAI exist, including cloud-based AI platforms from major providers like Google Cloud Healthcare API, Microsoft Azure for Health, and Amazon Web Services (AWS) for Healthcare. Key differences often lie in their specific feature sets, integration capabilities with existing healthcare systems, pricing structures, and the level of healthcare-specific compliance and security offered.
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| Last Verified: 2026-08-23 | First Added: 2026-08-23