Easily Secure Your AI & ML Systems
Protect AI performs security scans on your ML lifecycle and helps you deliver secure and compliant ML models and AI applications.
Protect AI's November Vulnerability Report
by Dan McInerney & Marcello Salvati
The Time is now to Protect AI
by Ian Swanson
Announcing ModelScan: Open source protection against model serialization attacks
by Chris King
A Tale of Two LLMs - Safety vs. Complexity
by Daryan Dehghanpisheh
Threat Visibility
AI and ML System Threat Surface Visualization and Context Mapping
Security Testing
AI and ML Security Tests Across Your ML Assets, Tooling, and Processes
Remediation
Actions to Remediate AI and ML Security Risks
Radar
Go Beyond Traditional Security.
AI/ML systems are fragmented, complex and dynamic which creates hidden security risks that escape your existing AppSec governance and control policies. ML teams lack complete visibility of their full ML lifecycle, slowing productivity and preventing the enterprise from maintaining a proper security posture. AI/ML auditability is now business critical. AI's rapid rate of adoption requires safeguarding against potential "log4j" moments in ML, and requires enterprises to implement and meet AI governance and future regulations. New methods are needed so businesses can quickly identify, remediate, and examine unique AI risks.
Radar is the industry’s first solution for securing ML Systems, end-to-end.
NB Defense
Industry-First Security Tool for ML Notebooks, That's Free To Use
Jupyter Notebooks are a powerful tool for data scientists to explore data, create models, evaluate experiments, and share results with their peers. The notebooks contain live code, visualizations, data, and text. They introduce security risks and current cybersecurity solutions do not work to evaluate them. NB Defense is free to use, it quickly scans a single notebook or a repository of notebooks for common security issues, identifies problems, and guides your remediation.
ModelScan
Protection Against Model Serialization Attacks
Machine Learning (ML) Models are shared over the internet, between teams, and are used to make critical decisions. Despite this, models are not scanned with the rigor of a PDF file in your inbox.
ModelScan, by Protect AI, is an open source project that scans models to determine if they contain unsafe code. It is the first model scanning tool to support multiple model formats, including H5, Pickle, and SavedModel formats. This protects you when using PyTorch, TensorFlow, Keras, Sklearn, XGBoost, with more on the way.
Innovate, Model, & Collaborate More Securely
Secure your notebooks and adopt MLSecOps best practices by checking your notebooks and their environment for critical security issues. Protect AI's NB Defense keeps you productive and collaborative while helping reduce security risks, scanning for issues and surfacing insights inside Jupyter Lab.
Secure Your ML Stack, End to End With Context & Confidence
Scan and remediate all elements of your ML software supply chain, in an easy, automated fashion. Protect AI tools integrate into your existing MLOps frameworks and procedures, providing everyone who relies on your systems with a more secure AI environment.
Stay Focused on Security, not Distracted by ML Nuances.
Comprehending the unique risks of AI systems and ML supply chains can be daunting. Protect AI tools give you clarity and context in an environment and syntax familiar to you, making sure you can communicate effectively with your AI and ML development teams. Shortening the time not only to discover issues, but to mitigate them as well.
MLSecOps: Get Involved
MLSecOps is derived from "machine learning" (ML) and "security operations" (SecOps), referring to the practice of integrating security measures into machine learning workflows and pipelines. Access curated resources, learn best practices in machine learning security, listen to podcasts with thought leaders, and connect with our thriving Slack community.
The World's first AI/ML Bug Bounty Platform
huntr provides a single place for security researchers to submit vulnerabilities, to ensure the security and stability of AI/ML applications. Start your journey into AI/ML threat research today.
MLSecOps Community
MLSecOps is a new and much needed practice in application security that involves introducing security earlier in the ML model development life cycle. Join our Slack community today and help shape this new frontier in machine learning and cybersecurity.