Performance and Scalability Considerations

SAST performance scales with code base size and analysis depth. Analyzing large applications with interprocedural taint analysis can require hours of processing time. However, SAST analysis is typically batch-oriented—running during off-hours or in parallel with other build activities. Incremental analysis, examining only changed code, dramatically improves performance for continuous integration scenarios. Cloud-based SAST leverages elastic computing for faster analysis.

IAST performance impact manifests as runtime overhead during application execution. Every request processed incurs sensor overhead for data tracking and analysis. Modern IAST solutions minimize impact through optimized sensors, selective instrumentation, and efficient data structures. Typical overhead ranges from 5-20%, varying with application architecture and workload patterns. This continuous overhead requires careful capacity planning for test environments.

Scalability challenges differ between approaches. SAST scalability involves analyzing growing code bases efficiently—solved through distributed analysis and incremental scanning. IAST scalability concerns monitoring numerous distributed applications—addressed through centralized management and lightweight sensors. Both approaches benefit from cloud architectures that provide elastic resources for varying workloads.