Mosh Streaming Data Masking

A stream of raw data flows fast. Inside it are secrets—names, emails, IDs, payment info. Let it slip into logs or analytics pipelines, and you have a breach waiting to happen. That’s why Mosh Streaming Data Masking exists. It protects sensitive fields on the fly, before they touch storage, search indexes, or downstream consumers.

Mosh Streaming Data Masking works inline. It takes structured or semi-structured data—JSON, Avro, CSV—and scans it for predefined patterns or schema-defined fields. Matches are masked, hashed, or replaced in real time. No batch jobs, no waiting. Masking happens at wire speed, to match the velocity of Kafka topics, Kinesis streams, or WebSocket feeds. You keep the utility of your data while cutting the exposure risk.

The system supports rule-based masking and regex-based detection. Rules can target personal identifiers, customer metadata, or compliance-specific fields (PCI, HIPAA, GDPR). You can define multiple masking modes: fixed placeholders, deterministic hashing for joins, or irreversible tokenization for secure analytics. Deploy it as a container, drop it into your stream processing platform, and start masking without rewriting producer or consumer code.

Integration is minimal. Mosh can sit between producers and brokers, or consumers and sinks. It’s stateless, horizontally scalable, and designed for high-throughput environments. You can manage policies via config files or APIs, update them without downtime, and monitor masking performance with built-in metrics.

Mosh Streaming Data Masking helps teams reduce compliance workload, avoid accidental data leaks, and enforce consistent policies across heterogeneous pipelines. It’s security at the speed of your stream.

See Mosh in action now. Go to hoop.dev and watch it mask streaming data live—in minutes.