What's New at Protect AI
Blog Byte: What’s Old is New - Natural Language as the Hacking Tool of Choice
May 25, 2023 • Daryan Dehghanpisheh • 4 min read
Blog Byte: A Tale of Two LLMs - Safety vs. Complexity
May 17, 2023 • Daryan Dehghanpisheh • 3 min read
Blog Byte: Spherical Steaks in ML. “Say what?!”
May 10, 2023 • Daryan Dehghanpisheh • 2 min read
Hacking AI: System and Cloud Takeover via MLflow Exploit
Mar 24, 2023 • Dan McInerney • 8 min read
Protect AI Offerings
Enterprises must understand the unique threat surface of their AI & ML systems across the lifecycle and quickly remediate to eliminate risks. Our products provide threat visibility, security testing, and remediation.
AI and ML System Threat Surface Visualization and Context Mapping
AI and ML Security Tests Across Your ML Assets, Tooling, and Processes
Actions to Remediate AI and ML Security Risks
Products to Secure Your AI and ML Systems
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.
Secure Your AI & ML Systems
Machine Learning's complexity extends to the systems used to scale AI delivery within organizations. Unfortunately, this complexity often leads to blindspots in traditional security practices, hindering the understanding and mitigation of risks. Protect AI is building the industry’s first Machine Learning Bill of Materials (MLBOM), AI Radar. It is a dynamic, automated engine that minimizes security risks by capturing dependencies across the ML environment. It enhances the trustworthiness of AI applications through comprehensive evaluations of third-party assets and models using both custom and pre-defined security checks. By leveraging searchable and time-stamped MLBOMs, security teams can reduce incident response times, facilitating swift investigation and mitigation of security issues for Blue Teams. AI Radar enables the entire enterprise to enhance development transparency of AI systems and improve ML security by ensuring reproducibility and versioning of the environment at any given time.
"Protect AI addresses a major gap in cybersecurity: Security and compliance of AI systems and ML code."
— Dan Plastina, Former VP and Head of Security Services at Amazon Web Services
Key Value of Protect AI Offerings
Protect AI is the first company focused on the security of AI and ML Systems creating a new category we call MLSecOps. Our offerings help your enterprise easily secure and better govern your AI and ML Systems resulting in more trusted artificial intelligence.
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.
Join the MLSecOps Community to access curated resources, learn best practices in machine learning security, listen to podcasts with thought leaders, and connect with our thriving Slack community.