Beware of PII in Testing Data: The Security Iceberg and Where PII Actually Hides
Using 'production-similar' data in pre-production is a major security risk. Learn why traditional masking fails, where hidden PII hides, and how to fix it.
Latest insights on Agentic AI workflows, cloud native architectures, and performance optimization best practices from the Speedscale team.
Using 'production-similar' data in pre-production is a major security risk. Learn why traditional masking fails, where hidden PII hides, and how to fix it.
Skip hand-writing WireMock stubs. Speedscale records your real request and response traffic and exports it straight to WireMock mappings.
Logs, metrics, and traces are a lossy compression of production. Five things you can do with a traffic data lake that observability can't.
Replay an authenticated flow and the protected calls fail with 403. Here is how proxymock recommendations fix the expired bearer token in one click.
Production traffic is the most complete record of what your system does, and most teams throw it away. One capture powers reproduce, validate, and sandbox.
MSA clauses and contractual guarantees aren't an architecture. If your production traffic leaves your cloud, you're trusting a policy, not a system.
Spring Boot upgrades silently change JSON contracts and autoconfiguration. Use traffic replay to catch every regression before production.
AI-generated code breaks traditional mocking. Here's how BYOC capture and replay with proxymock keeps verification grounded in real production traffic.
Capture production traffic and store it in your own Elasticsearch with Speedscale BYOC. Pull it locally with es-gather.py and reproduce bugs with proxymock.
I tested 100 bugs across 240 microservices the model has never seen. Alert only: 51% pass rate, wrong service 34% of the time. Traffic captures: 77%.