Traceable AI

Traceable Artificial Intelligence (AI) is an Application Programming Interface (API) security platform that monitors, analyzes, and protects API traffic for cloud-native and distributed applications.

• API discovery, risk assessment, and posture management for distributed and microservices-based environments (API security)
• Runtime protection for APIs against threats such as abuse, injection, and business logic attacks (application security)
• Use of distributed tracing and telemetry to build an activity map of application and API behavior (observability)
• Capabilities for data classification and detection of sensitive information exposure within API traffic (data security)
• Integration with DevSecOps workflows and existing security tooling for policy enforcement and incident response (DevSecOps)

More About Traceable AI

Traceable AI focuses on securing APIs used in modern, cloud-native architectures, where applications are often decomposed into microservices and communicate heavily over internal and external APIs. The platform is designed for enterprise environments that run on Kubernetes, containers, and service meshes, and where API traffic volume and complexity make manual security controls infeasible.

The platform typically ingests telemetry from application runtimes, gateways, and infrastructure to automatically discover APIs, including undocumented or “shadow” APIs. It builds an inventory of APIs and their endpoints, classifies data types that flow through them, and assesses risk based on exposure, authentication posture, and access patterns. This places the offering in the API security and application security categories, with ties to observability and cloud security.

Traceable AI uses distributed tracing concepts and techniques to reconstruct end-to-end user and service flows across microservices. By correlating calls, parameters, and identities, it creates a behavioral baseline for normal API usage. The platform then applies analytics and security policies to detect anomalies and attack patterns such as injection attempts, credential misuse, enumeration, and abuse of business logic. This combination of tracing, behavioral analysis, and policy enforcement supports runtime protection of APIs in production environments.

Architecturally, Traceable AI is compatible with common enterprise stacks that use HTTP/HTTPS APIs, Representational State Transfer (REST), and modern API management patterns. It integrates with service meshes, ingress controllers, and API gateways to capture traffic and enforce controls. The platform also connects into DevSecOps and SOC workflows through integrations with Continuous Integration and Continuous Deployment (CI/CD) pipelines, Security Information and Event Management (SIEM) tools, and incident management systems, enabling security teams and developers to share a common view of API risk and incidents.

Within an enterprise technology directory, Traceable AI can be categorized under API security, application security, runtime protection, and observability for APIs. It is relevant for organizations that expose customer-facing APIs, maintain large microservices estates, or handle regulated or sensitive data over APIs. The platform’s focus on discovery, data classification, and behavioral analysis positions it as a control point for managing API risk across development and production lifecycles.