Protect AI is a cybersecurity and governance platform purpose-built to secure the machine learning (ML) development lifecycle. As AI adoption accelerates, ML systems face emerging threats such as model poisoning, supply chain attacks, and data leakage. Protect AI addresses this challenge by integrating security and compliance tools directly into MLOps workflows, helping teams detect vulnerabilities, manage risk, and ensure safe, auditable AI systems.
From vulnerability detection in model code to runtime threat monitoring and compliance documentation, Protect AI provides a full-stack security layer that complements your data science and DevOps teams. Its comprehensive platform enables organizations to operationalize AI governance and secure ML pipelines at every stage.
Features
Protect AI offers a suite of tools and services that enhance AI/ML security, transparency, and compliance:
NB Defense (Notebook Security)
Scans Jupyter and Colab notebooks for secrets, misconfigurations, and vulnerabilities to prevent supply chain risks.AI Radar
A central registry that monitors ML assets, tracks dependencies, and provides visibility into AI software supply chain risks.ModelScan
Static and dynamic scanning of ML models for security misconfigurations, unsafe code, and embedded secrets.ML Bill of Materials (MLBOM)
Automatically creates and maintains a bill of materials for models, datasets, libraries, and dependencies—enabling supply chain transparency.Continuous ML Monitoring
Tracks changes to ML artifacts, pipelines, and permissions for auditability and compliance.Threat Intelligence for AI Systems
Leverages proprietary and community-sourced knowledge of ML vulnerabilities and exploits.Audit & Compliance Automation
Generates documentation and dashboards aligned with compliance frameworks like NIST AI RMF, SOC 2, and ISO 27001.Integrations with MLOps Tools
Supports leading platforms including MLflow, SageMaker, Vertex AI, and open-source frameworks.
How It Works
Protect AI integrates directly into your MLOps infrastructure to continuously monitor, assess, and secure AI workflows:
Asset Discovery
Identify and inventory all ML assets across notebooks, datasets, pipelines, and models.Risk Assessment
Run tools like NB Defense and ModelScan to detect vulnerabilities, secrets, and compliance violations.SBOM/MLBOM Generation
Automatically generate and track ML bills of materials for traceability and supply chain insight.Governance & Reporting
Build centralized dashboards to manage risks, track changes, and prepare for audits.Threat Detection & Remediation
Monitor for suspicious behavior or unauthorized changes, and provide actionable recommendations.Collaboration & Integration
Enable security, ML, and compliance teams to work together through integrations with CI/CD pipelines and version control systems like GitHub.
Use Cases
1. Securing MLOps Pipelines
Prevent security risks by scanning notebooks, datasets, and pipelines for vulnerabilities during development.
2. Model Governance & Explainability
Track lineage, ownership, and changes across models to improve auditability and compliance readiness.
3. Regulatory Compliance
Automate reporting and documentation for compliance frameworks including GDPR, SOC 2, HIPAA, and NIST AI RMF.
4. AI Supply Chain Risk Management
Generate and monitor MLBOMs to protect against dependencies and third-party component risks.
5. Threat Intelligence Integration
Get real-time insights into emerging ML attack vectors and protect deployed models from tampering or abuse.
6. Data Science Team Enablement
Equip ML developers with secure tools that fit naturally into existing workflows and platforms.
Pricing
Protect AI follows a custom pricing model based on:
Number of ML assets (models, notebooks, pipelines)
Deployment environment (cloud-native, on-prem, hybrid)
Compliance and reporting needs
Support and training requirements
Volume of integrations and automation features
As of June 2025, no public pricing tiers are listed. Organizations interested in a quote or demo should contact the sales team directly.
Strengths
Comprehensive ML Security Coverage
Addresses vulnerabilities at the notebook, model, pipeline, and infrastructure levels.Integrated AI Governance
Combines compliance, auditing, and visibility features to meet growing regulatory demands.MLOps Tool Compatibility
Seamless integration with major cloud providers and open-source ML stacks.Proactive Threat Intelligence
Actively tracks new ML vulnerabilities and attack vectors.Purpose-Built for AI Systems
Not a retrofitted DevSecOps tool—Protect AI is engineered specifically for machine learning environments.Collaborative by Design
Enables collaboration between security, ML, and compliance teams with shared dashboards and reporting.
Drawbacks
Enterprise-Focused Offering
The platform is designed for medium to large organizations, not individual developers or early-stage startups.No Free Tier Available
Users must request access or a demo—there is no public self-service trial as of now.Requires Organizational Buy-In
Full implementation typically involves collaboration between ML, IT, and InfoSec departments.Limited Public Reviews
As an emerging category, there are few customer reviews on third-party sites like G2 or Capterra.
Comparison with Other Tools
Protect AI vs. HiddenLayer
While HiddenLayer focuses on runtime inference security, Protect AI provides full lifecycle coverage—from development to deployment, including compliance.
Protect AI vs. Robust Intelligence
Robust Intelligence emphasizes model robustness testing and validation. Protect AI adds vulnerability detection and supply chain visibility.
Protect AI vs. traditional DevSecOps tools
DevSecOps tools scan codebases but lack context for ML artifacts. Protect AI is AI-native, understanding notebooks, model weights, and datasets.
Protect AI vs. Microsoft Azure ML Security
Azure provides security within its own ecosystem. Protect AI works across cloud platforms and tools, providing centralized AI risk management.
Customer Reviews and Testimonials
Protect AI is trusted by security-conscious organizations in finance, healthcare, insurance, and AI startups. Though public reviews are limited, client feedback emphasizes:
Rapid threat detection in ML environments
Audit readiness in compliance-heavy sectors
Streamlined collaboration across security and ML teams
A compliance lead at a fintech company shared:
“Protect AI helped us build a complete view of our ML assets and risks—something we struggled with before.”
A director of MLOps in a global tech firm said:
“Security was the missing piece in our ML pipeline. Protect AI integrated quickly and now keeps our models compliant and secure.”
Conclusion
As machine learning evolves from research to production across industries, securing the ML lifecycle is no longer optional—it’s essential. Protect AI offers one of the most comprehensive platforms for AI risk management, model security, and compliance automation, designed specifically for modern MLOps teams.
Whether you’re developing in notebooks, managing ML pipelines, or deploying models at scale, Protect AI ensures your AI systems are safe, transparent, and ready for regulatory scrutiny.
