AI Responsibly: Securing AI Assets with Unity Catalog

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Lastly, ensuring the right “checks and balances” are applied to the datasets to ensure integrity and validity of the data and AI/ML models is critical for accurate and insightful predictions and having a mechanism to monitor the drift of data is crucial.

Databricks Lakehouse Monitoring is a native feature that enables organizations to monitor the quality and performance of data and AI assets across their entire data pipeline. It provides real-time insights into statistical properties of data, tracks machine learning model performance, and offers automated alerting for anomalies without requiring additional tools or complexity

The Need for AI Governance

As AI systems become more sophisticated and integral to business processes, they also present unique security risks. These risks range from data breaches and model theft to unintended biases and compliance violations. Traditional security measures often fall short in addressing the complex needs of AI systems, which require protection not just for data, but also for models, model registries, notebooks, and other AI artifacts. More recently, there with the introduction of RAG and CAG systems, the masking of PII sensitive data and formation of the knowledge base for these systems needs to be tightly controlled, ensuring AI and machine learning m0dels don’t consume sensitive data when generating predictions to prevent bias.

How zeb and Databricks Data Intelligence Platform Secures your AI Real Estate

Here comes Databricks, with advanced and robust data governance capabilities built on Unity Catalog.

• The data foundation that powers the AI and machine learning models will be governed under one unified layer inside Unity Catalog to allow for metadata management, lineage tracking of tables, views, pipelines and even training and testing models.
• Sensitive and PII data masking with automatic dataset and attribute recognition by Unity Catalog to ensure sensitive data is not exposed to the models and third parties that consume the model via an API.
• Comprehensive Auditing and lineage tracking of pipelines along with logging of user actions and data access provides transparency and aids in compliance efforts.
• Access to tables and views can be restricted at the role level (RBAC) and the attribute level (ABAC) to ensure tightly regulated and governed use of data in the Databricks workspace.
• Lastly, data sharing within your partner ecosystem or over multiple Databricks workspaces is done securely because it is governed by Unity Catalog.

Support for AI Governance

Given the criticality of AI in the current data landscape, Databricks has a plethora of services that govern and regulate AI and machine learning models within Unity Catalog itself.

Models, similar to tables and views can be regulated the same way in Unity Catalog by leveraging Feature governance and model lineage, and combining this with MLflow to deliver and manage the entire AIOps lifecycle will ensure that your AI workspace stays tightly governed and regulated.

• Leverage AI Gateway in conjunction with Unity Catalog to manage access to machine learning and AI models in your Databricks workspace from both an internal and external perspective with centralized API management, RBAC and ABAC, comprehensive monitoring and auditing capabilities, and policy enforcement from the cloud provider of your choice enabling organizations to implement consistent governance policies, track API usage, detect anomalies, and maintain compliance with data protection regulations.
• Lakehouse Monitoring contributes to AI governance by enabling real-time tracking of data quality and AI/ML model performance. It allows organizations to monitor statistical properties across tables, track changes in model inputs and predictions, and provides lineage graphs with monitor alerts. These features help identify and resolve issues quickly, ensuring the reliability and accuracy of AI initiatives. By offering insights into data and model performance, Lakehouse Monitoring supports ongoing governance and quality assurance efforts in AI development and deployment.
• With Databricks Delta Sharing, in addition to tables and views, AI objects like Models, Feature Sets and Registries can be shared securely over a Databricks workspace or over multiple Databricks workspaces as it is governed by Unity Catalog ensuring the same policies applied on the models in the development environment are reflected when it is shared with your own partner ecosystem.
• Lastly, secure your model deployment process with Managed MLflow on Databricks enhancing AI governance by centralizing the management of the machine learning lifecycle, ensuring transparency and compliance. The MLflow Model Registry enables version control and approval workflows, while experiment tracking supports reproducibility and traceability. By integrating with CI/CD pipelines and providing automated lineage tracking, it facilitates seamless governance across model transitions, ensuring responsible management of AI assets.

Partner with zeb to build secure and compliant AI systems for your organizations!