The emergence of AI brings data governance into sharp focus, because grounding LLMs with secure, trusted data is the only way to ensure accurate responses.
What is AI data governance?
AI data governance is the process of managing the data product lifecycle within AI systems. It has 2 main components:
1. AI data privacy
Any Personally Identifiable Information (PII) or other sensitive data must be protected from unauthorized access and use, accessible to only one user (and nobody else), and compliant with data protection laws like CPRA, GDPR, and HIPAA.
Plus, hackers are constantly trying to trick Large Language Models (LLMs) to reveal their confidential information.
2. AI data quality
AI data quality has 2 aspects, since using data in AI systems is a 2-way street: what goes in and what comes out.
What goes in is the data used for training and augmenting AI models, which needs to be clean, complete, and current to respond to user queries as accurately and responsibly as possible.
What goes out is the data provided to users in those responses. For users to trust it, not only should all relevant sources be cited (and clickable), but the model should also be able to explain how it arrived at its decision. The data should also be as free of bias as possible to prevent discrimination.
Ensuring data privacy and quality helps you manage your risk, build trust with your customers, and use your AI apps responsibly. That’s the essence of AI data governance. Now let’s take a deeper dive into data privacy and data quality, especially in terms of the challenges they face and the things you can do to address them.
AI data privacy challenges
We recently conducted a survey of 300 companies and found that 48% listed data privacy as one of the top obstacles to integrating enterprise data with GenAI apps. We can subdivide the challenges associated with AI data privacy into 5 separate categories:
1. Data breaches are also breaches of trust
Large language models (LLMs), the core of most AI systems, are trained on vast amounts of publicly available external data. Having said that, a new breed of model, the enterprise LLM, can be augmented with your private internal company data using frameworks like retrieval-augmented generation, or RAG, for short.
But here’s the rub: Included in your internal data is PII and other sensitive information that are vulnerable to attack – like financial data or medical data.
Data breaches can reveal the confidential data you store, and expose your company to financial, legal, and reputational damage. Luckily, proper AI data governance includes sensitive data discovery tools, dynamic data masking, role-based access controls, and data isolation techniques that safeguard your company from breaches.
2.Data privacy has become a very public issue
Adhering to data protection laws is key, because non-compliance can lead to fines and penalties, as well as to loss of customer faith. Make sure the AI data governance tools you choose have capabilities like data minimization, data anonymization, and rules-based data access built in.
3. Transparency helps explain how AI thinks
LLMs are considered black boxes, making it difficult to understand how they reach decisions. This lack of transparency leads to mistrust, and possible misuse of generative AI apps, since the accuracy of the LLM responses can’t be verified.
Explaining how your model thinks (chain-of-thought reasoning, for example), and citing authoritative sources, enhances transparency and builds trust.
4. Ethical use of AI is a moral imperative
AI can be misused for purposes like surveillance or profiling, that infringe on an individual’s privacy rights. You should appoint supervisors to ensure that your AI apps align with ethical standards to prevent malpractice.
5. Algorithmic bias can lead to discrimination
LLMs often learn and pass on biases found in their training data, leading to unfair or discriminatory practices in the hiring of new employees or the lending of money, for example – potentially violating individual privacy rights.
Using diverse and representative datasets, implementing fairness-aware algorithms, and regularly auditing LLMs can reduce such bias.
Addressing the privacy issues listed above requires a comprehensive approach to AI data governance, combining technical, legal, and ethical strategies to ensure the responsible and secure use of this emerging technology.
AI data quality challenges
Ensuring AI data quality isn’t easy. In the same survey mentioned above, we found that data quality is one of the top concerns associated with building AI apps. That’s because data quality plays a critical role in building trust for AI apps inside organizations.
Using active retrieval-augmented generation to ground LLMs with trusted private data and knowledge is crucial. But ensuring AI data quality for LLM grounding is tricky due to:
1. Fragmented data
Enterprise data is often siloed in dozens of systems. Customer data, for example, is typically fragmented across CRM, billing, customer service, interaction management, call recordings, and the list goes on. This fragmentation makes it incredibly difficult to present a real-time, reliable customer view to your LLM to power your customer-facing AI apps.
To overcome this challenge, you'd need a robust data infrastructure capable of real-time data integration and unification, master data management, data transformation and validation. The more fragmented the data, the harder it is to achieve AI data quality.
2. Poor-quality metadata
Imagine an earth-bound translator trying to give instructions in Martian. That's what it feels like when AI apps encounter data with sparse metadata. Metadata is the data that describes your data. It acts as a crucial bridge between your organization's information and your LLM's ability to power your AI apps.
Rich metadata provides the context and understanding your LLM needs to effectively use data to generate accurate and personalized responses. But if your data catalog is poorly maintained, your metadata is stale, and your AI initiatives are ineffective.
3. The quality vs privacy tradeoff
AI data quality can be negatively impacted by privacy measures, such as data masking and access controls, which can hinder the retention of your data’s referential consistency.
Referential consistency refers to the accuracy of relationships between different data points. When anonymization techniques, like static or dynamic data masking, disrupt these relationships, your data quality suffers. Masked data is less reliable and meaningful for both your LLM and your user.
Essentially, the very measures designed to protect data privacy can inadvertently undermine the quality of the data itself and prevent generative AI from extracting valuable insights. For this reason, your AI data governance solution should assure referential consistency
4. The quality vs strategy dilemma
Traditionally, data quality initiatives have often been lone efforts, disconnected from core business objectives and strategies. Such isolation makes it difficult to measure the impact of data quality improvements and secure the AI investments you seek. As a result, AI struggles to gain the attention it deserves.
AI apps rely on AI quality data to minimize AI hallucinations and generate accurate, reliable results. Such dependence creates a great opportunity to point out the benefits of AI – in terms of both privacy and quality – to secure the necessary resources for continued improvement.
The disconnect between data lakes and AI
Many organizations use ETL/ETL to ingest multi-source enterprise data into centralized data lakes that are responsible for enforcing data governance. Early AI adopters used RAG tools and LLM agents to write functions that queried the data lake to respond to all possible user prompts. The problem is, the list of all possible user prompts is endless.
So, despite their advantages in scalability, accessibility, and cost, data lakes are a bad fit for AI data or RAG for the following reasons:
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Sensitive data may accidentally be leaked to the LLM or to an unauthorized user
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The cost of cleansing and querying the data at enterprise scale is extremely high.
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Data lakes don’t jive with generative AI use cases that require clean, compliant, and current data.
Making data AI-ready and governable
We were always taught to think big: big data stored in big data lakes. But the only way to make data AI-ready and governable is to think small – in fact, super small.
Imagine a data lake of one – a dedicated Micro-Database™ for each customer, employee, or product – that continuously syncs a single entity’s data with your source systems, protects it to comply with your data privacy rules, and transforms it according to your data quality standards.
Now imagine millions of instantly accessible Micro-Databases delivering AI personalization, at AI speed and scale, to millions of customers at the same time.
Learn how the K2view suite of RAG tools, GenAI Data Fusion, checks all the boxes for AI data governance.
