Traceable AI is an API security platform that leverages distributed tracing technology to discover, test, and protect APIs while tracking sensitive data flows across microservices architectures.
What is Traceable AI?
Traceable AI provides comprehensive API security through a platform that was purpose-built around distributed tracing.
While other API security tools observe traffic at network boundaries, Traceable follows requests through your entire application stack, understanding how data flows between services, databases, and external APIs.
The platform was founded by the creators of OpenTelemetry, the CNCF project that standardized distributed tracing.
This heritage shows in Traceable’s deep understanding of service meshes, microservices communication patterns, and the challenges of securing distributed applications.
Traceable covers the complete API security lifecycle: discovering APIs automatically, testing them for vulnerabilities during development, protecting them at runtime, and providing forensic investigation capabilities when incidents occur.
Key Features
Distributed Tracing-Based Discovery
Traceable discovers APIs by analyzing distributed traces rather than just network traffic:
- Complete service maps: Sees every service-to-service API call, including internal APIs never exposed externally
- Data flow tracking: Follows sensitive data from ingress through every service it touches
- Dependency mapping: Understands which services depend on which APIs
- Change detection: Identifies when API behavior changes between deployments
This trace-based approach catches shadow APIs and internal services that perimeter-focused tools miss entirely.
Sensitive Data Flow Tracking
The platform tracks sensitive data as it moves through your applications:
- Identifies where PII, financial data, and credentials enter your system
- Maps how that data propagates across services
- Detects when sensitive data appears in unexpected locations
- Alerts when data reaches services that should not have access
This data lineage capability helps with compliance requirements (GDPR, CCPA) and reduces data exposure risk.
API Security Testing
Traceable tests APIs for vulnerabilities using context from production traffic:
- OWASP API Top 10: Comprehensive coverage of API-specific vulnerabilities
- Business logic testing: Uses learned API behavior to test authorization and access controls
- Attack surface analysis: Identifies API endpoints that accept sensitive data
- Fuzzing: Generates test payloads based on observed data patterns
Tests run against staging environments but benefit from production traffic analysis.
Runtime Threat Detection
Real-time protection identifies attacks against your APIs:
- Behavioral anomaly detection: Alerts when API usage patterns deviate from baseline
- Attack signature detection: Identifies known attack patterns (injection, enumeration)
- Account takeover prevention: Detects credential stuffing and session hijacking
- Rate limiting evasion: Catches distributed attacks that circumvent simple rate limits
The platform can operate in detection or blocking mode depending on your risk tolerance.
GenAI API Security
Traceable provides specific protections for LLM and GenAI applications:
- Identifies API endpoints connected to LLM services
- Detects prompt injection attempts
- Monitors for data exfiltration through AI responses
- Tracks which data is sent to third-party AI APIs
Integration
Deployment Options
Traceable integrates with your infrastructure through multiple methods:
Tracing Agent: Deploy alongside your existing OpenTelemetry or Jaeger setup:
# Kubernetes deployment with tracing agent
apiVersion: apps/v1
kind: Deployment
metadata:
name: my-service
spec:
template:
spec:
containers:
- name: my-service
env:
- name: OTEL_EXPORTER_OTLP_ENDPOINT
value: "http://traceable-collector:4317"
- name: OTEL_SERVICE_NAME
value: "my-service"
- name: traceable-agent
image: traceable/agent:latest
env:
- name: TRACEABLE_API_KEY
valueFrom:
secretKeyRef:
name: traceable-config
key: api-key
Traffic Mirroring: Analyze mirrored traffic without inline deployment:
# AWS Traffic Mirroring configuration
Resources:
TraceableMirrorTarget:
Type: AWS::EC2::TrafficMirrorTarget
Properties:
NetworkLoadBalancerArn: !Ref TraceableNLB
Description: Mirror to Traceable analyzer
API Gateway Integration: Native connectors for Kong, AWS API Gateway, and Apigee.
CI/CD Pipeline Integration
Run API security tests during development:
# GitHub Actions example
name: API Security Testing
on:
pull_request:
branches: [main]
jobs:
traceable-scan:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Deploy to staging
run: docker-compose up -d
- name: Run Traceable API tests
env:
TRACEABLE_API_KEY: ${{ secrets.TRACEABLE_API_KEY }}
run: |
# Trigger API security assessment
SCAN_ID=$(curl -s -X POST "https://api.traceable.ai/v1/assessments" \
-H "Authorization: Bearer $TRACEABLE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"environment": "staging",
"tests": ["owasp-api-top-10", "business-logic"],
"target": "http://staging.example.com"
}' | jq -r '.scanId')
echo "Scan ID: $SCAN_ID"
# Wait for completion
for i in {1..60}; do
STATUS=$(curl -s "https://api.traceable.ai/v1/assessments/$SCAN_ID" \
-H "Authorization: Bearer $TRACEABLE_API_KEY" | jq -r '.status')
if [ "$STATUS" = "completed" ]; then
break
fi
sleep 30
done
# Check results
VULNS=$(curl -s "https://api.traceable.ai/v1/assessments/$SCAN_ID/findings" \
-H "Authorization: Bearer $TRACEABLE_API_KEY" \
| jq '[.[] | select(.severity == "high" or .severity == "critical")] | length')
if [ "$VULNS" -gt 0 ]; then
echo "Found $VULNS high/critical vulnerabilities"
exit 1
fi
# GitLab CI example
stages:
- deploy
- security
api-security-scan:
stage: security
script:
- |
# Upload OpenAPI spec for testing
curl -X POST "https://api.traceable.ai/v1/specs" \
-H "Authorization: Bearer $TRACEABLE_API_KEY" \
-F "spec=@openapi.yaml" \
-F "service=my-api"
# Run security assessment
curl -X POST "https://api.traceable.ai/v1/assessments" \
-H "Authorization: Bearer $TRACEABLE_API_KEY" \
-d '{"environment": "staging", "coverage": "comprehensive"}'
rules:
- if: $CI_PIPELINE_SOURCE == "merge_request_event"
SIEM and Alerting Integration
Export security events to your SIEM:
{
"integrations": {
"splunk": {
"enabled": true,
"hecEndpoint": "https://splunk.example.com:8088",
"hecToken": "${SPLUNK_TOKEN}",
"index": "api_security"
},
"pagerDuty": {
"enabled": true,
"integrationKey": "${PAGERDUTY_KEY}",
"severityThreshold": "high"
},
"slack": {
"enabled": true,
"webhook": "${SLACK_WEBHOOK}",
"channel": "#security-alerts"
}
}
}
OpenTelemetry Integration
If you already use OpenTelemetry, Traceable can consume your existing traces:
# OpenTelemetry Collector configuration
receivers:
otlp:
protocols:
grpc:
endpoint: 0.0.0.0:4317
processors:
batch:
timeout: 1s
exporters:
traceable:
endpoint: https://collector.traceable.ai
api_key: ${TRACEABLE_API_KEY}
service:
pipelines:
traces:
receivers: [otlp]
processors: [batch]
exporters: [traceable]
When to Use Traceable AI
Ideal for organizations that:
- Run microservices architectures with service-to-service API communication
- Already use or plan to use distributed tracing (OpenTelemetry, Jaeger)
- Need to track sensitive data flows for compliance
- Want API security that understands internal APIs, not just external endpoints
- Build GenAI applications with LLM API calls
- Require deep forensic investigation capabilities for security incidents
Consider alternatives if:
- Your architecture is monolithic with few internal APIs
- You need a simple, lightweight API gateway security solution
- Budget constraints favor open-source alternatives
- You prefer detection-only without runtime protection needs
Traceable AI brings the observability mindset to API security.
By building on distributed tracing foundations, it provides visibility into API behavior that network-perimeter tools cannot match, making it particularly valuable for organizations with complex, distributed architectures.
Note: Acquired by Harness in March 2025. The merger combined Traceable's API security with Harness's DevSecOps platform.
