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Summary

  • Yesterday’s data governance strategies are not equipped to handle the nuances of generative AI.
  • Several factors undermine organizations’ attempts to design and implement data governance for AI. These include: a lack of defined frameworks, visibility challenges, AI hallucinations, and the volume of unstructured data.
  • While creating training models with pristine data sets might be a dream, organizations can fast-track their data governance for AI by deploying data loss prevention (DLP) tools specifically built for this technology.

According to new McKinsey research, companies that fail to embrace generative AI are setting themselves up for serious competitiveness issues down the line. 

You might be thinking, “That’s not a problem for my organization. We’re already experimenting with generative AI.” But, how you deploy AI is far more important than simply using it. 

It comes down to data governance, a strategic practice that’s crucial to unleashing the true value of generative AI. Data governance is how organizations can ensure data quality, visibility, and reliability as they embed AI into their operations. More than that, it’s integral to effective risk management and compliance. Without governance, organizations are at a heightened risk of AI-related data leaks, data breaches, and bias.

Generative AI data governance challenges

Think of generative AI as an unpredictable wildcard. It can produce some impressive outputs, but it’s also prone to hallucinations, intellectual property abuse, and data chaos.

Data governance strategies of the past aren’t equipped to handle cutting-edge generative AI models. So, organizations must reimagine their playbooks for this new technological era. However, doing so isn’t necessarily easy.

Here are the major challenges organizations face when designing data governance strategies for generative AI: 

  • Lack of guidelines: Generative AI is still very much in its infancy. Because the technology has developed so quickly, established governance frameworks are still lacking. Organizations don’t have robust points of reference to help them with data governance.
  • Lack of visibility: Enterprise data today often lives in unstructured formats, scattered across emails, cloud applications, and databases. Locating and organizing this data is no walk in the park. To complicate matters, there’s a need to identify sensitive information within this vast sea of data and ensure it’s adequately protected.
  • Data quality and lineage concerns: With AI models often operating in seemingly mysterious ways, tracking the origin and lifecycle of data becomes an additional challenge in data governance. A recent article by The Washington Post highlighted that Google’s Bard had 45% of its training data from unverified sources, shaking up the trust in the tool’s data outputs for organizations.
  • Data classification: Another piece of the puzzle is effectively categorizing data to empower governance and security teams to implement the right controls. Successful data governance hinges on differentiating sensitive, regulated information from data clutter. However, the amount of unstructured data can turn this task into an implausibility.
  • Data mapping: Even without generative AI added into the mix, successful data mapping can be hard to do. Manual processes, complexity, and data silos all convolute the likelihood of a true and reliable process. This has only been made more complex by generative AI. 
  • Data security: Any sensitive information shared with an AI model can later be regurgitated in an answer to another user. If employees input regulated information into these models, that could lead to a possible data leak and compliance fine too. It’s no wonder that 50% of security executives are concerned about accidentally releasing their organization’s IP via large language learning models. 
  • Ethical concerns: If AI models are trained on skewed datasets, they can end up perpetuating unfair biases. Take, for instance, an HR AI system trained on data mostly from male sales executives. It might undervalue promotion opportunities for females in the organization, thus prolonging instances of gender bias.

The first step to generative AI data governance 

With so much flux related to AI data governance, crafting and implementing a robust strategy can seem impossible. Creating training models with pristine data sets is often too expensive and error-prone for most organizations.

Luckily, there is a way to fast-track your data governance initiative and overcome the security and compliance risks of generative AI. Teams can accomplish this through next-generation data loss prevention (DLP).

Polymer DLP was designed so organizations can enable business with generative AI while combatting the common data governance risks. Ready to skyrocket your AI governance maturity? Read this whitepaper to learn more.

Polymer is a human-centric data loss prevention (DLP) platform that holistically reduces the risk of data exposure in your SaaS apps and AI tools. In addition to automatically detecting and remediating violations, Polymer coaches your employees to become better data stewards. Try Polymer for free.

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