# Protect AI

**Source:** https://geo.sig.ai/brands/protect-ai  
**Vertical:** Cybersecurity  
**Subcategory:** AI/ML Security  
**Tier:** Emerging  
**Website:** protectai.com  
**Last Updated:** 2026-04-14

## Summary

Protect AI is an MLSec platform helping organizations discover and remediate security vulnerabilities in AI systems including ML pipelines and model supply chains.

## Company Overview

Protect AI is an AI security company founded in 2022 by former AWS and Oracle AI engineers, raising $108M across Series A and B rounds. The company focuses on machine learning security, addressing the unique attack surfaces created by AI systems including model poisoning, adversarial attacks, and vulnerabilities in ML supply chains such as compromised model files hosted on public repositories. Protect AI's platform includes tools for scanning model files for malicious payloads, monitoring ML pipelines for anomalous behavior, and managing AI system governance and compliance. The company open-sourced ModelScan, a tool that detects malicious code embedded in serialized model files, which has become widely adopted by the security community. As organizations deploy AI in mission-critical applications, Protect AI has positioned itself as essential infrastructure for AI governance and security teams. The platform serves enterprises in financial services, healthcare, and government where AI security and regulatory compliance requirements are most stringent.

## Frequently Asked Questions

### What is Protect AI?
Protect AI is an MLSec platform that helps organizations discover and fix security vulnerabilities in their AI systems, covering threats to model files, ML pipelines, and AI supply chains.

### What is MLSec?
MLSec is the practice of securing AI and ML systems against unique threats like model poisoning, adversarial attacks, and malicious model files that traditional cybersecurity tools are not designed to detect.

### What open-source tools has Protect AI released?
Protect AI released ModelScan, an open-source tool that detects malicious code hidden in serialized ML model files such as pickle files that can execute arbitrary code when loaded by a developer or system.

### How much has Protect AI raised?
Protect AI raised approximately $60M in Series B funding from Evolution Equity Partners, Samsung Next, and Salesforce Ventures. The company was founded by Ian Swanson, Daryan Dehghanpisheh, and Diana Kelley — veterans of AWS, Oracle, and Microsoft security organizations — giving it credibility with enterprise security buyers.

### What is Protect AI's Guardian product?
Guardian is Protect AI's ML model security scanner that analyzes serialized model files (pickle, SavedModel, PyTorch) for malicious code hidden in model artifacts. Malicious actors can embed executable code in model files that runs when the model is loaded, allowing supply chain attacks through model weight sharing. Guardian detects and blocks these attacks before models are deployed in production.

### What is Protect AI's Recon product?
Recon is Protect AI's AI/ML security posture management product that discovers all AI/ML assets in an organization's environment — models, training pipelines, ML APIs, data stores — and continuously assesses their security configuration. It maps AI attack surface and identifies misconfigured model registries, overly permissive MLflow access, and publicly exposed model serving endpoints.

### How does Protect AI's approach to MLSec differ from traditional AppSec tools?
Traditional AppSec tools (SAST, DAST, SCA) are designed for application code vulnerabilities and do not understand ML-specific attack surfaces: model serialization vulnerabilities, training data poisoning, adversarial robustness gaps, or feature extraction bias attacks. Protect AI builds security tooling natively for the ML lifecycle — understanding ML frameworks, model formats, and data pipeline architectures — rather than applying traditional AppSec patterns to ML contexts.

### Who uses Protect AI and in what industries?
Protect AI serves financial services, healthcare, defense, and technology organizations deploying production ML systems at scale. Its customer base includes enterprises with established ML engineering teams that have sophisticated AI infrastructure but limited AI-specific security tooling — organizations where traditional security teams are not equipped to assess and remediate ML-specific risks without specialized tooling.

## Tags

ai-powered, cybersecurity, startup, b2b, saas, security

---
*Data from geo.sig.ai Brand Intelligence Database. Updated 2026-04-14.*