How to Ensure Data Governance When Implementing Generative AI

The adoption of generative AI (Gen AI) in the field of data analytics has crossed the incubation stage. The two most anticipated advantages of this technology are productivity and improved efficiency, combined with the speed of data processing. A Gartner report predicts that by 2026, more than 80% of organizations will have used Gen AI models or APIs. According to JP Morgan, Gen AI would potentially increase global GDP by $7–10 trillion because of its contribution to the massive productivity boom.

No wonder, the technology has found a considerable number of use cases in a variety of industry verticals, such as IT and cybersecurity, marketing, sales, customer service, product development, research and development, strategy and operations, finance, supply chain and manufacturing. Most of these applications hinge on the adoption of data analytics in business applications, where Gen AI improves data preprocessing and augmentation, generates valuable data for training models, automates analytics tasks and enhances data visualization.

Data Security Concerns That Come with Gen AI

A KPMG survey revealed that the top three risks in the implementation of Gen AI are personal data breaches, network security and liability. Similarly, IBM’s X-Force Threat Intelligence Index 2024 has warned that the AI market has a 50% share of incentivizing cybercriminals towards investing in cost-effective tools to attack AI technologies. This means cyber attackers prefer stealing and selling data to encrypting it for extortion. The widespread adoption of Gen AI poses potential vulnerabilities, such as security holes, intellectual property theft, sensitive data leaks and data privacy breaches.

Gen AI models may inadvertently reveal sensitive organizational information when they are trained on datasets containing such details. In addition, these models may end up oversharing data or making information available inaccurately, leading to privacy breaches. For instance, healthcare systems use Gen AI models that are trained on patient data, such as names, addresses and health histories. If it is not properly governed, this model might unintentionally leak sensitive patterns in the data.

Gen AI applications use large language models (LLMs), which process a massive amount of data and create more new data, but it’s still susceptible to poor quality, bias and unauthorized access. This becomes particularly risky these models may publicly expose an enterprise’s trade secrets, mission-critical proprietary information and/or customer data.

Without data governance, AI outputs may result in compliance violations, inaccuracies, breach of contract, copyright infringement, false fraud alerts or harmful interactions with customers, leading to damaged goodwill.

Challenges of Data Governance While Adopting Gen AI

Data governance is a principled approach to data management within an organization that involves setting up internal standards and data policies, from acquisition to disposal. Adopting this framework empowers enterprises to enhance regulatory compliance, manage risks more efficiently, make timely decisions and ensure data security. However, Gen AI poses its own set of challenges in implementing the data governance protocols.

Here are some challenges:

Unstructured Data Management

Many LLMs depend on information that an organization draws from structured and unstructured data.  The latter is often in the form of documents, images or videos stored in varying formats across siloed systems. Companies don’t label such data within a database that may contain everything from emails to videos. Gen AI models are trained on this data, which may have incomplete information or a lack of context. The sheer volume and complexity of unstructured data make it all the more challenging to understand and use safely.

Data Life Cycle Traceability

Compared to traditional machine learning (ML) models, Gen AI models deal with data that originates from multiple channels across systems. When data is sourced from many places, tracking its lifecycle becomes doubly challenging. Lack of information about a dataset’s origin leads to false information and inaccuracies.

Biased Results

LLMs are often trained on segregated data to be used for a specific goal or purpose. This bias could be a selection bias where the training data does not represent the entire demographic or a representation bias when the training data fails to adequately represent different groups or categories. For instance, a Gen AI model automates the shortlisting of candidates for recruitment purposes. This model was trained on 100 of the best candidates in five different professions. Due to such a small sample size, the model will end up shortlisting only certain applicants for all jobs or same applicants over and over.

Data Leaks

As discussed in the previous section of this article, Gen AI models inadvertently leak sensitive data to outsiders in the absence of good data governance policies. This data may be related to customers, trade secrets, proprietary information, etc. Access to such sensitive information disrupts business operations and sometimes even has legal implications.

How to Best Ensure Data Governance When Implementing Gen AI Models

Many organizations today are reluctant to implement Gen AI models into their data analytics function due to the above-stated challenges. However, with good data governance practices and technology, they can fully utilize Gen AI capabilities and meet organizational goals more effectively.

How can organizations marry the two most efficiently?  For starters, they will need to implement a comprehensive data governance strategy requiring the implementation of quality and privacy parameters to drive responsible AI.

Organizations that use data analytics work with large language models for enterprise use cases. We learned earlier that a large part of this enterprise data comes from unstructured and siloed sources, creating many privacy and accuracy challenges. One way to mitigate these challenges is to adopt an end-to-end data management and governance policy at every step of the journey. That means it should begin right from ingesting, storing and querying data all the way through analyzing, visualizing and applying Gen AI and ML models.

A Gen AI-Powered Semantic Layer for AI Governance

LLMs provide a huge library of information gathered from large datasets using deep learning techniques. But these models generate inconsistent responses because they have been trained in the domain-specific terminology used by the organization.

A semantic layer bridges the gap between business logic and data language to filter and refine responses generated by these LLMs. It creates meaningful definitions and classifications within the datasets and allows downstream tools and apps to make data queries through it instead of directly inquiring from the database.

The semantic layer also provides context and introduces specificity to LLMs, ensuring accuracy and relevance. For instance, a query is executed about the comparison of an organization’s sales figures for two different products in Asia over a period of one year. With a Gen AI powered semantic layer like Kyvos, the Gen AI model pulls data from diverse datasets such as CRM or operations. In this case, the layer acts like a guide to ensure that the data collected is relevant, accurate and contextual. It can become a trusted source of data for AI applications and LLMs, reducing the chances of hallucinations while speeding up their development by several notches.

Similarly, Kyvos enables compliance within the AI governance framework, so LLMs follow through on filters before releasing any data, keeping in line with security and privacy standards. This prevents any data leaks or security breaches.

Final Thoughts

Data governance is a critical element of data integrity and covers a range of disciplines, such as data management, security, cataloging and quality. The approach requires clearly thought-out usage policies and strategy frameworks that help document data sources, profile data sets and create prompt libraries. When implemented through a technology solution, data governance can enhance the efficiency of Gen AI models.

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