Phirestream removes sensitive information from Apache Kafka streams.


The safest way to manage sensitive information in your systems is to apply safeguards before the the sensitive information enters your systems. Phirestream works in front of Apache Kafka to remove, redact, or encrypt sensitive information from your streaming data before it enters your Apache Kafka cluster.

Phirestream filters sensitive information, such as personally identifiable information (PII) and protected health information (PHI) from your Apache Kafka data streams.

Learn more about how Phirestream works with Apache Kafka to to secure sensitive information.

Phirestream filters sensitive information from data pipelines.

Works with Apache Kafka

Phirestream integrates with Apache Kafka to filter sensitive information from data streams.

Control over data redaction

Phirestream can redact, encrypt, or anonymize the sensitive information it finds.

Tailored for different domains

Phirestream’s NLP model can be changed based on your domain and use-case to offer increased performance. We offer specialized models for working with healthcare and legal. We are constantly improving our models to provide the best possible performance.

State-of-the-art NLP

Philter uses state-of-the-art natural language processing (NLP) to analyze text. Using trained models, Philter can identify person’s names in many text across many domains. You can also use your own NLP models with Philter.

Disambiguate the types of sensitive information

Some sensitive information can belong to multiple types, such as phone numbers and SSNs. Phirestream can disambiguate the sensitive information and determine the best type when a conflict occurs.

Filter profiles provide flexibility

Filter profiles are how you tell Phirestream what kinds of information to redact. Each Apache Kafka topic can be assigned to its own filter profile.

Customizable redaction logic

You can apply different redaction logic based on conditions. Philter can redact sensitive information based on conditions such as the population of a zip code or the content and type of the sensitive information.

Encrypt sensitive information

When found, sensitive information can be encrypted or replaced by a SHA-256 hash value keeping the original values secure.

Capture metrics

Philter can generate metrics while it is analyzing your text. Metrics can be published to Amazon CloudWatch, DataDog, and exposed via JMX. The metrics show the counts and types of sensitive information in your text.

Generate alerts

Philter can generate alerts when certain information is found. If sensitive information is found that satisfies a condition you provide an alert will be generated. Use alerts to be aware of what information is in your text.

Apache, Apache Kafka, Kafka, and associated open source project names are trademarks of the Apache Software Foundation.