TL;DR: Protect AI is a cybersecurity company focused exclusively on securing AI and machine learning systems. Their platform scans ML models for vulnerabilities, detects model poisoning, prevents prompt injection attacks, and secures the entire AI/ML supply chain — from training data to production inference. View Protect AI tool page
What Is Protect AI?
Protect AI was founded in 2022 by Ian Swanson (former head of AI at AWS and Oracle) with a singular focus: securing AI/ML systems. As organizations deploy AI models at unprecedented scale, a new attack surface has emerged — model poisoning, adversarial inputs, prompt injection, data leakage through models, and supply chain attacks on ML pipelines. Traditional security tools like Snyk and CrowdStrike are not designed to address these AI-specific threats.
Protect AI fills this gap. The company has raised over $100 million and built a comprehensive platform that covers the entire ML lifecycle: scanning models before deployment, monitoring models in production, securing training data pipelines, and testing models against adversarial attacks. Their open-source tool, NB Defense, has become a popular Jupyter Notebook security scanner.
In 2026, with AI models becoming critical infrastructure (powering healthcare decisions, financial trading, autonomous vehicles), securing these models is no longer optional. Protect AI positions itself as the "Snyk for AI" — developer-friendly security tools specifically designed for ML engineers and data scientists.
Key Features
| Feature | Description |
|---|---|
| Guardian | ML model scanner that detects vulnerabilities, backdoors, and serialization attacks in model files |
| Radar | AI/ML bill of materials (AIBOM) — tracks all AI components across your organization |
| Layer | Model access control and governance — ensures only authorized models are deployed |
| NB Defense (Open Source) | Jupyter Notebook security scanner that detects credentials, PII, and vulnerable packages |
| ModelScan (Open Source) | CLI tool to scan ML model files for unsafe operations and deserialization attacks |
| AI/ML Vulnerability Database | Curated database of known vulnerabilities in ML frameworks, libraries, and models |
| Red Team Tools | Adversarial testing capabilities to stress-test your models before deployment |
Protect AI Pricing in 2026
| Tier | Price | Includes |
|---|---|---|
| Open Source | Free | NB Defense, ModelScan — community-supported open-source tools |
| Team | Custom pricing | Guardian scanning, Radar AIBOM, team collaboration, basic support |
| Enterprise | Custom pricing | Full platform including Layer governance, red team tools, dedicated support, SLA |
The open-source tools (NB Defense and ModelScan) are genuinely useful standalone and can be integrated into CI/CD pipelines at no cost. The commercial platform provides centralized management, continuous monitoring, and enterprise governance features.
How to Use Protect AI
- Start with open source: Install NB Defense (
pip install nbdefense) to scan your Jupyter Notebooks for security issues - Scan models: Use ModelScan (
pip install modelscan) to check ML model files for unsafe operations - Inventory your AI: If using the commercial platform, deploy Radar to create an AIBOM of all AI components
- Set up Guardian: Integrate Guardian into your ML pipeline to automatically scan models before deployment
- Enable governance: Use Layer to enforce policies on which models can be promoted to production
- Red team your models: Use adversarial testing tools to probe your models for prompt injection, data leakage, and bias
Pros and Cons
| Pros | Cons |
|---|---|
| Only platform focused specifically on AI/ML security | Relatively new company — platform still maturing |
| Strong open-source tools (NB Defense, ModelScan) | Commercial pricing not publicly available |
| Founded by former AWS/Oracle AI security leaders | Niche market — ROI may be hard to justify for smaller teams |
| Covers entire ML lifecycle from training to production | Requires ML expertise to configure and interpret results |
| AI-specific vulnerability database is unique | Integration with some ML platforms still evolving |
Protect AI Alternatives
| Tool | Best For | Key Difference |
|---|---|---|
| Robust Intelligence | AI model validation | Focuses on model testing and validation rather than supply chain |
| Calypso AI | AI governance | Emphasizes compliance and regulatory governance for AI |
| HiddenLayer | ML model defense | Focuses on runtime model protection and adversarial defense |
Try Protect AI
Ready to secure your AI pipeline? Visit Protect AI to explore their security platform, or check out our Protect AI tool page for more details and comparisons.
Frequently Asked Questions
Is Protect AI free?
Protect AI offers free open-source tools (NB Defense and ModelScan) that any team can use immediately. The commercial platform (Guardian, Radar, Layer) requires a paid subscription with custom pricing.
Why do I need AI-specific security if I already have Snyk or CrowdStrike?
Traditional security tools protect code and infrastructure. Protect AI addresses threats unique to AI/ML: model poisoning, adversarial inputs, data leakage through models, unsafe model serialization, and prompt injection. These are completely different attack vectors that Snyk and CrowdStrike were not designed to detect.
What is model poisoning and should I worry about it?
Model poisoning is when an attacker manipulates training data or model weights to make a model behave in unintended ways. For example, a poisoned medical AI could misclassify certain conditions. If you deploy AI models that affect real-world decisions, model poisoning is a serious concern.
