Build a Data and Analytics Governance Framework for AI Readiness

Organizations whose primary business model is based on making data driven decisions using data and analytics (D&A) are facing a growing need to develop successful AI initiatives. The dynamic nature of D&A-focused organizations means that different teams working with various machine learning models and BI tools, in the same organization, each needing to meet their own team goals, interact with this data. This creates a situation where governing data usage is disjointed.

As organizations move to incorporate LLMs and generative AI into their data lifecycle, they must also scale the volume of data needed to feed these models and remain competitive. This leads to uncertainty about how to govern D&A while becoming AI-ready. Because D&A governance is often dispersed across multiple teams, various data sources and BI tools’ sensitive data is usually governed manually, complicating the governance framework.

As Gartner noted, incohesive data governance frameworks impact the anticipated value of an organization’s AI use cases. One way to combat this uncertainty is to develop a strong data and analytics governance framework so organizations can become AI-ready. Satori’s AI Security Platform can help organizations develop a D&A governance framework by providing clarity and control over AI & LLM, enforcing dynamic security policies, visibility and policy enforcement. In this blog post, we outline some necessary characteristics of strong data governance for AI.

Data governance is important for both protecting sensitive data and achieving a state of AI-readiness. It is important to set the terms for your LLM to access internal data for its different users. Using dynamic security policies and redacting sensitive data is one way to help enforce AI governance which is then extended to a D&A governance framework that is evolving to meet increasingly complex and dispersed needs.

Organizations that deal with data, especially sensitive protected health information (PHI) or personally identifiable information (PII) data, understand the importance of data governance and ideally already have a strong data governance framework in place. Data governance means having a framework to ensure that data is useful (particularly for analytics), available, and secure. There are five main principles of data governance: ownership, accessibility, knowledge, quality, and security. Read more about data governance here and how Satori provides Agile Data Governance.

D&A governance includes data governance but extends it to also consider the business context. Instead of relying on a single one-size-fits-all approach, it needs to be multidisciplinary and highly sensitive to business outcomes. Ultimately, D&A governance brings together a range of governance policies, monitors and enforces them across business systems to ensure that AI ready data is usable, high-quality, and secure.

To ensure that your data is usable and can scale securely and compliant, creating a strong data and analytics governance framework is important. To do so, we recommend following these steps:

1. Establish Clear Objectives: Start by defining the goals of your framework that address specific business outcomes. Both the goals and style must align with your organization’s business context and AI objectives. Provide a transparent model that is collaborative and inclusive of multidisciplinary teams.
2. Develop Data Policies and Standards: Create comprehensive data policies and standards around data management practices that align with your organization’s business context. This includes setting specific policies for your LLMs to ensure data remains protected in all cases.
3. Assign Data Ownership: While there should be a designated owner for each data asset, this should be a collaborative, multidisciplinary team encompassing different aspects of the defined business context and outcome. This enables a forward-facing business outcome that reacts to ever-evolving AI.
4. Implement Data Access Controls: Strong automated access management is necessary to simplify permissions so that data is shared quickly and easily across different teams to achieve the business outcome. These permissions should cover diverse ML and BI stacks with complete RBAC and ABAC support.
5. Ensure Data Validation and Quality Monitoring: Set up processes for regular data validation and quality monitoring that address existing business goals and outcomes, using automation tools that can detect and correct data quality issues for you right as they come up.
6. Privacy Policies: Track data’s origin through continuously classifying and tagging sensitive data so that security and privacy policies can be applied dynamically while maintaining transparency and data integrity and enabling easier auditing.

Some extra tips for integrating data and analytics governance into your existing AI projects and workflows:

• Align with AI Objectives: When creating your governance policies, make sure they support the needs of your specific AI projects, including data accuracy and model reliability.
• Scalability: Make sure your framework can scale as your data volumes grow and your AI initiatives evolve.
• Continuous Improvement: Because new challenges will arise and AI tech will advance, make sure you regularly review and update your governance practices.

Data governance and security are crucial for becoming AI-ready. The necessity of having a strong framework is clear, especially for D&A organizations building a framework to implement AI initiatives. Setting clear data policies, automating access controls and data validation and quality monitoring is key to ensuring your AI models are built on accurate, reliable data.

A data and AI security platform is essential to save time and ensure compliance. Satori’s AI security enforcement, LLM activity monitoring, and access management tools take care of data and analytics governance for you, freeing up data teams’ time and helping you pass audits.

Book a demo with our experts to learn how Satori can help your set up a D&A governance framework for AI readiness.