Protect AI

Protect Artificial Intelligence (AI) is an enterprise security company focused on securing Machine Learning (ML) and AI systems across the model lifecycle.

• Security and governance for ML and AI systems across the model lifecycle.
• Tools and platforms for detecting, managing, and mitigating AI and ML security risks (AI security).
• Capabilities for securing ML supply chains, including models, data, code, and dependencies (software supply chain security).
• Monitoring, observability, and policy enforcement for deployed ML workloads (ML observability / runtime security).
• Consulting, best-practices content, and community resources focused on AI and ML security programs.

More About Protect AI

Protect AI focuses on security for ML and AI systems used in enterprise and institutional environments. Its offerings address risks across the ML lifecycle, including data preparation, model training, model packaging, deployment, and ongoing operation. The company positions its platforms and tools to integrate with existing Machine Learning Operations (MLOps), DevSecOps, and cloud-native workflows, so that security controls can be applied without reconstructing existing pipelines.

The organization targets threat scenarios specific to ML, such as compromised training data, tampered model artifacts, insecure ML pipelines, and vulnerabilities in dependencies that support AI workloads. Its capabilities fit into categories such as AI security, software supply chain security, and runtime security. Protect AI’s focus includes mapping traditional application security concepts—like dependency scanning, artifact integrity, and access control—to the components and workflows that are unique to ML systems.

From an architectural perspective, Protect AI’s tools are oriented toward environments that rely on modern ML stacks and cloud infrastructure. This typically includes integration points with container orchestration platforms (container security), Continuous Integration and Continuous Deployment (CI/CD) systems (DevSecOps), and data and model registries common in MLOps pipelines. The company’s content and documentation reference frameworks and practices in AI and ML security, such as model supply chain analysis, configuration assessment of ML tools, and continuous monitoring of ML-related assets and services.

In comparison to general-purpose application or cloud security tooling, Protect AI focuses on ML-specific assets such as datasets, models, notebooks, model repositories, experiment tracking systems, and pipeline configurations. Its offerings seek to give security and platform teams visibility into these assets, correlate them with software components and infrastructure, and enforce policies appropriate for AI workloads. This includes capabilities for detecting configuration weaknesses and policy violations tied to ML tools and services.

Within an enterprise technology directory or marketplace, Protect AI aligns with solution areas such as AI security, ML security, software supply chain security for ML, MLOps security, and observability and monitoring for AI systems. It is positioned for use by security teams, ML platform and MLOps teams, and engineering organizations that deploy ML at scale and require governance and risk management for their AI assets.