AI-Driven Anomaly Detection for GraphQL API Security
Machine learning models detecting nested query attacks and resource exhaustion in GraphQL APIs.
Seconds
Detection Time
<2%
False Positives
Real-time
Mitigation
The Challenge
A technology company exposing critical business functionality through GraphQL APIs was facing an evolving threat landscape that traditional security tools could not adequately address. Unlike REST APIs with predictable endpoint patterns, GraphQL's flexible query language enabled malicious actors to craft deeply nested queries, batch operations, and introspection attacks that bypassed conventional web application firewalls and rate limiters. The company had experienced several security incidents — including query-based denial-of-service attempts and data exfiltration through crafted field traversals — and needed an intelligent detection layer that understood GraphQL-specific attack patterns rather than relying on generic HTTP-level rules.
Our Approach
Corlence designed and built an AI-powered anomaly detection system purpose-built for the unique security challenges of GraphQL APIs.
We established query behaviour profiling by analysing historical API traffic to build baseline models of normal GraphQL usage patterns — including typical query depths, field selections, fragment usage, batch sizes, and response payload characteristics. This created a dynamic fingerprint of legitimate API behaviour unique to the client's environment, providing the foundation for accurate anomaly identification.
AI-powered anomaly detection models were trained to identify deviations from established baselines in real time. The system detected a comprehensive range of GraphQL-specific threats including deeply nested query attacks (query depth bombing), batch query abuse, unauthorised introspection attempts, field-level data scraping patterns, and resource exhaustion through aliased query multiplication — attack vectors that conventional WAF rules consistently missed.
Rather than implementing simple binary blocking, the system used contextual threat scoring that evaluated each request across multiple signals — query complexity, deviation magnitude, source reputation, and temporal patterns. This enabled graduated responses ranging from logging and alerting through to automated throttling and blocking, minimising disruption to legitimate traffic while neutralising genuine threats.
A real-time security operations dashboard provided comprehensive visibility into API traffic patterns, active anomalies, and threat trend analysis. Automated alerts integrated with the team's existing incident response workflows, ensuring rapid response to genuine threats without the alert fatigue that plagues conventional security tools.
Technology Stack
Python, Scikit-learn, TensorFlow, FastAPI, Redis, Elasticsearch, Grafana, Docker, Kubernetes
Business Value Delivered
- Detected and mitigated novel query-based attack patterns that existing WAF rules had missed entirely, closing critical security gaps
- False positive rate maintained below 2%, enabling the security team to trust and act on alerts with confidence
- Mean time to detect GraphQL-specific threats reduced from hours to seconds, dramatically improving the organisation's security posture
- Delivered a reusable AI security pattern applicable across the client's broader API portfolio, multiplying the return on investment
Why Corlence
Corlence Pty Ltd is an Australian AI and integration solutions consultancy specialising in document intelligence, generative AI, predictive analytics, and enterprise integration. We design and build production-grade AI systems that deliver measurable business outcomes — not proof-of-concepts that gather dust.
Whether you need to automate compliance workflows, build intelligent customer engagement, secure your API infrastructure, or unlock insights from unstructured data — we bring deep technical expertise paired with pragmatic business focus.