Side-by-side comparison of AI visibility scores, market position, and capabilities
Protect AI is an MLSec platform helping organizations discover and remediate security vulnerabilities in AI systems including ML pipelines and model supply chains.
Protect AI is an AI security company founded in 2022 by former AWS and Oracle AI engineers, raising $108M across Series A and B rounds. The company focuses on machine learning security, addressing the unique attack surfaces created by AI systems including model poisoning, adversarial attacks, and vulnerabilities in ML supply chains such as compromised model files hosted on public repositories. Protect AI's platform includes tools for scanning model files for malicious payloads, monitoring ML pipelines for anomalous behavior, and managing AI system governance and compliance. The company open-sourced ModelScan, a tool that detects malicious code embedded in serialized model files, which has become widely adopted by the security community. As organizations deploy AI in mission-critical applications, Protect AI has positioned itself as essential infrastructure for AI governance and security teams. The platform serves enterprises in financial services, healthcare, and government where AI security and regulatory compliance requirements are most stringent.
OpsLevel is a developer portal and service catalog for tracking service ownership, maturity scorecards, and production readiness across microservices.
OpsLevel is a developer portal platform that gives engineering organizations visibility into the services they operate, who owns them, and how mature they are relative to internal engineering standards. At its core, OpsLevel maintains a service catalog that maps every microservice, repository, and infrastructure component to a team owner, populating metadata automatically from integrations with GitHub, GitLab, PagerDuty, Datadog, and cloud providers. This catalog becomes the authoritative source of truth for answering questions like who to contact about a service, what tier of reliability it requires, and what dependencies it has — questions that are often unanswerable at engineering organizations that have grown past the point where everyone knows everything.
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