blog bg left
Back to Blog

WhyLabs Achieves SOC 2 Type 2 Certification!

AI observability is mission-critical for production ML applications. At WhyLabs, we are committed to making AI observability ubiquitous and available to every AI practitioner by removing the barriers for the adoption of this essential technology. One of these barriers is the need for data privacy and security assurance.

We are very happy to announce that we successfully completed our SOC 2 Type 2 examination with zero exceptions. WhyLabs is committed to ensuring our current, and future customers are well informed about the robust capabilities and security of the WhyLabs AI Observatory platform. A part of that commitment is our guarantee to have our business policies and practices evaluated and validated by independent third parties.

What is SOC 2 Compliance?

System and Organization Controls (SOC) reports are issued to organizations that provide services like WhyLabs, and whose controls have been evaluated by a third party against defined standards. SOC 2 is one of the most comprehensive certifications within SOC and is broadly considered the most trusted third-party security verification.

WhyLabs’ successful SOC 2 Type 2 examination was focused on controls as they relate to security. This designation recognizes that WhyLabs meets all the infrastructure and data control policy requirements to regularly monitor for malicious or unrecognized activity, monitor user access levels, and document system configuration changes. The results reveal that our information and systems are thoroughly protected against unauthorized access, disclosure of information, and damage to systems. The report is available to customers and prospects evaluating the effectiveness of WhyLabs’ policies and procedures for controlling our services.

Our relentless commitment to your security

We know that security and data privacy are critical for all our customers and users. We designed our SaaS platform, the WhyLabs AI Observatory, from first principles with privacy built in. The raw data never leaves the customer perimeter.  Our approach is to profile model inputs and outputs continuously but capture only statistical profiles of the underlying data. These statistical profiles do not contain proprietary information or PII, and for added security, all statistical profiles are encrypted during transfer and at rest.

WhyLabs was designed to remove barriers for organizations to adopt and optimize ML applications - with peace of mind that their data is secure. We want customers to focus on achieving healthy models and healthy data without worrying about threats to data and privacy. Our successful SOC2 Type 2 certification is only one of the stepping stones in our commitment to security.

To learn more about security at WhyLabs, visit our security page or join our Slack. To request The WhyLabs SOC 2 Type 2 report, please contact your account manager or email [email protected]

Run AI with Certainty!

Other posts

Model Monitoring for Financial Fraud Classification

Model monitoring is helping the financial services industry avoid huge losses caused by performance degradation in their fraud transaction models.

Data and ML Monitoring is Easier with whylogs v1.1

The release of whylogs v1.1 brings many features to the whylogs data logging API, making it even easier to monitor your data and ML models!

Robust & Responsible AI Newsletter - Issue #3

Every quarter we send out a roundup of the hottest MLOps and Data-Centric AI news including industry highlights, what’s brewing at WhyLabs, and more.

Data Quality Monitoring in Apache Airflow with whylogs

To make the most of whylogs within your existing Apache Airflow pipelines, we’ve created the whylogs Airflow provider. Using an example, we’ll show how you can use whylogs and Airflow to make your workflow more responsible, scalable, and efficient.

Data Logging with whylogs: Profiling for Efficiency and Speed

Rather than sampling data, whylogs captures snapshots of the data making it fast and efficient for data logging, even if your datasets scale to larger sizes.

Data Quality Monitoring for Kafka, Beyond Schema Validation

Data quality mapped to a schema registry or data type validation is a good start, but there are a few things most data application owners don’t think about. We explore error scenarios beyond schema validation and how to mitigate them.

Data + Model Monitoring with WhyLabs: simple, customizable, actionable

The new monitoring system maximizes the helpfulness of alerts and minimizes alert fatigue, so users can focus on improving their models instead of worrying about them in production...

A Solution for Monitoring Image Data

A breakdown of how to monitor unstructured data such as images, the types of problems that threaten computer vision systems, and a solution for these challenges.

How to Validate Data Quality for ML Monitoring

Data quality is one of the most important considerations for machine learning applications—and it's one of the most frequently overlooked. We explore why it’s an essential step in the MLOps process and how to check your data quality with whylogs.
pre footer decoration
pre footer decoration
pre footer decoration

Run AI With Certainty

Book a demo
loading...