AI Data Governance

Importance of Data Governance for the Life Sciences Industry

In an increasingly data-driven world, data governance in AI (Artificial Intelligence) is essential to ensure that Life Sciences companies operate with quality and safety, maximizing the value of their initiatives. This process involves defining policies, processes, and controls that guarantee the quality, integrity, confidentiality, and compliance of the data used in AI systems.

It is important that companies adopt a holistic approach to AI data governance, considering technical, ethical, and legal aspects. In the full article published on the FIVE Validation website, in the blog section, we present a more detailed discussion on the topics highlighted below:

The biopharmaceutical and medical products industries are highly regulated and must prove that their product, manufacturing, and development processes are robust. Patient safety is affected by the integrity of critical records, data, and decisions, as well as by factors related to the physical attributes of products.

Implementing a robust AI Data Governance framework can help the pharmaceutical, medical devices, and biotechnology industries manage and protect data assets, ensure compliance, and maintain high standards of data integrity and quality across all organizational operations.

While this process may differ in each organization, some key decisions must be considered, such as data categorization, data mapping, and data retention.

The governance structure can begin by answering key questions, such as:

1. Who are the stakeholders?
2. Where is the data stored?
3. What data will be collected?
4. Why are you collecting the data?
5. When does data flow from one stakeholder to another?
6. How will the data be modeled?

Within this scope, Infrastructure Qualification can play a fundamental role. Qualification involves assessing and ensuring the adequacy and proper functioning of systems that support GxP applications (impacting integrity, product quality, and patient/consumer safety).

Since most data is often considered a byproduct of final application processing, not all organizations have developed the necessary methods and processes to manage them.

Effective protection depends on a complete understanding of data assets. While discovery and classification define what and where your data assets reside, technical controls should define how the organization governs them. Some controls that can be applied include access control, data lineage, monitoring, encryption, data masking, data loss prevention, backup and recovery, retention policy, audit trail, quality controls, and data sharing.

Read the full article by clicking here or paste the following address into your browser: https://fivevalidation.com/data-governance-for-ai/ ?