Traceable AI
API security and threat protection platform vendor
Traceable AI is an API security platform that applies distributed tracing techniques, similar to those used in application performance monitoring, to build a detailed map of how data flows through an organization's APIs and microservices,…
Definition
Traceable AI is an API security platform that applies distributed tracing techniques, similar to those used in application performance monitoring, to build a detailed map of how data flows through an organization's APIs and microservices, then uses that map to detect threats, assess risk, and block attacks in real time. Its distinguishing approach is modeling the full call chain across services rather than analyzing API requests as isolated, independent events.
Overview
Traceable AI was founded by engineers with a background in application performance monitoring and distributed tracing, and it carries that lineage directly into its security architecture. The core insight behind the platform is that modern applications built on microservices rarely handle a user request through a single API call; instead, a request typically triggers a chain of internal service calls, and understanding security risk requires visibility into that entire chain, not just the single externally facing endpoint a user directly touched. Mechanically, Traceable AI deploys lightweight instrumentation, often based on the OpenTelemetry standard used broadly in observability tooling, across an organization's services to capture distributed traces: the full path a request takes as it moves through multiple internal microservices, including what data each service accesses and returns. This tracing data feeds a security analysis engine that maps sensitive data flows (identifying, for example, which internal services ultimately touch personally identifiable information even if that data is several hops removed from the original API call), assesses attack surface and risk per service, and applies both machine learning-based anomaly detection and rule-based rules to identify and block malicious traffic patterns in real time. Within the API security category, Traceable AI is generally compared with Salt Security, Noname Security, and Wallarm; its clearest technical distinction is the depth of its distributed tracing approach, which gives it visibility into internal service-to-service calls that many API security tools, focused primarily on traffic at the network edge, do not capture as thoroughly. This makes it particularly relevant for organizations with deep, complex microservices architectures where risk can originate several hops inside the system rather than only at the externally exposed API gateway. In practice, platform and security engineering teams at organizations running large microservices deployments use Traceable AI to map exactly where sensitive data flows across internal services, identify which internal APIs represent unexpectedly high risk because of what data they ultimately touch, and detect attacks such as data exfiltration attempts that unfold across multiple internal service hops rather than in a single request. The trade-off is instrumentation overhead: achieving Traceable AI's depth of visibility requires deploying tracing instrumentation across services, which is a more involved integration effort than tools that only inspect traffic at a network edge or gateway, and organizations with simpler, less deeply layered architectures may not need, or be able to fully leverage, that level of internal visibility across every internal service they operate.
Key Features
- Distributed tracing across microservices modeled on OpenTelemetry
- Maps sensitive data flows across multi-hop internal service calls
- Machine-learning anomaly detection combined with rule-based blocking
- Risk assessment per internal service based on data sensitivity
- Detects multi-hop attacks such as internal data exfiltration
- Deeper internal visibility than edge-only API security tools
- Real-time blocking integrated with service infrastructure
- Designed for complex, deeply layered microservices architectures
Use Cases
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