At this point, AI is everywhere, and it affects everyone. Whether it’s in the form of digital voice assistants and chatbots, monitoring for credit card fraud, or predicting what we want to see on our social media feeds, AI is rapidly infiltrating so many aspects of our daily lives, at work and at home.

As more data are added to the endless pool of data accumulating from multiple sources, data and AI governance become even more important. AI governance is the legal framework for ensuring AI and machine learning technologies are researched and developed with the goal of helping humanity navigate the adoption and use of these systems in ethical and responsible ways. AI governance aims to close the gap that exists between accountability and ethics in technological advancement.

The unique capability of AI-driven systems to learn from experience and adapt accordingly can help organizations scale their efforts to understand, manage, and use data while keeping it secure. Using AI in data governance can help businesses quickly recognize best practices as well as inefficient practices.

I recently read a report that mentioned that AI could displace 800 million jobs (30% of the global workforce) by 2030. While this can sound overwhelming, it’s essential to understand that AI is here to stay. Instead of fighting against it, my view is that people need to embrace its adoption.

I see the many benefits that AI can offer, transforming many industries and making us more productive. At the same time, if AI is not regulated, things could spiral downward quickly. This is where data governance comes in. Now is the time when business leaders need to think beyond how to incorporate AI into their business to include how to best consider the governance side of increased AI adoption.

How AI Is Reimagining Data Governance

Responsible and ethical AI governance and oversight is essential to helping establish better guidance on the development and deployment of AI technologies. The good news is that standards bodies are getting more engaged in this process and AI is playing a significant role in transforming data governance practices. Here are some of the major ways that AI is making an impact:

Data Classification and Tagging & Cleansing:

AI algorithms can analyze vast amounts of data and automatically classify and tag them based on predefined rules or machine learning models. This helps in organizing and categorizing data assets, making them more discoverable and manageable within a governance framework. AI techniques, such as machine learning and natural language processing, can be used to identify and correct data quality issues. By analyzing patterns and trends in the data, AI algorithms can detect anomalies, inconsistencies, and errors, enabling organizations to maintain high-quality data.

Data Security:

AI algorithms help identify cyber-attacks by recognizing patterns and notifying authorities before data is breached or compromised. AI applies automation to data privacy, security, and compliance. AI can automate data operations, metadata management, user identification, access management, two-step verifications, data permissions, and many other processes.

Data Governance Analytics & Automation:

AI-powered analytics tools can provide valuable insights into data governance practices. These tools can monitor and analyze data usage, compliance, and adherence to governance policies. By identifying patterns and trends, organizations can proactively address any governance gaps or risks. AI can also automate various data governance processes, reducing manual effort and improving efficiency. For instance, AI can automate the extraction of metadata from data sources, populate data catalogs, and enforce governance policies through rule-based engines.

Data Governance Decision Making:

AI can support data governance decision-making processes by providing intelligent recommendations and insights. By analyzing data usage patterns, user behavior, and compliance metrics, AI algorithms can assist data governance teams in making informed decisions regarding data policies, access controls, and data lifecycle management.

As businesses increasingly rely on AI and machine learning to drive operational efficiencies, it’s important to understand that the value of these technologies is only as high as governance standards that are in place. The implementation of a proper data governance framework is essential to enable organizations to fully unlock the value of their data and keep their businesses moving in the right direction.

— Katrina Klier