AI Data Governance – Key Aspects and Best Practices

AI’s output is only as good as the data it is trained on. Ensuring that only clean, consistent, and relevant data is used for model training and inference is key to realizing the value of any AI implementation. 

AI data governance is a set of policies and processes to ensure that the data used by AI for decision-making is of good quality, meets all compliance standards, and is secure. With AI playing a larger role in organizational decision-making, AI data governance is an essential aspect of broader AI governance to ensure the trust and transparency of your AI outputs. 

This article explores the benefits of AI data governance and the best practices in its implementation.

Summary of key AI data governance concepts 

AI data governance overview and benefits

AI data governance includes processes and policies to ensure that data used as input to models for training and inferring is of good quality, has adequate metadata, and is secure. It differs from traditional data governance in that it focuses specifically on data used by AI systems, introducing several AI-specific concepts like:

• Emphasis on model explainability and trust.
• AI-specific compliance standards.
• Data drift and related AI model degradation/hallucinations.
• Ethical AI considerations

AI data governance addresses the challenges inherent in typical AI training datasets that make implementing traditional rule-based quality controls extremely difficult.

For example, data used in AI model training and inference data is often semi-structured or unstructured, containing documents, images, videos, social media posts, or API responses pulled and aggregated from various third-party data providers. Such data sets often have inconsistent metadata tagging, leading to poor discoverability. The size of data can also increase exponentially, leading to a scale that traditional data governance systems can not handle.

Identifying sensitive data from semi-structured or unstructured data is also complex. Establishing role-based or attribute-based access controls becomes challenging. 

Implementing a systematic AI data governance policy in your organization addresses these challenges and brings several benefits. Below are a few considerations when designing your data governance strategy.

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Improves trust and transparency

Enterprise operations affect the lives of millions of people, including employees and customers. In the modern era, where AI models are responsible for key decisions, it is crucial to ensure that AI outputs are explainable. Given a specific set of input and contextual information, one must be able to reliably explain the AI output. Such explainability is possible only if training data is clean, securely stored, and has complete metadata information, including origin and lineage.

Reduces AI-specific compliance enforcement cost

Several countries and unions have developed AI-specific regulations that organizations must comply with. Examples include the EU AI Act and the NIST AI risk framework. A systematic AI data governance framework helps build compliance measures right from the start of the data life cycle, reducing the cost of meeting compliance requirements. Without flexible AI data governance, organizations incur significant additional effort while deploying and entering new geographies. 

Detects data drift and model degradation

A key assumption while deploying a model in production is that the data it receives as input will be similar to what was used during training. This is important in achieving the same accuracy levels established during the training and testing phase. In reality, data patterns can change and evolve over a period of time, leading to problems in model output. This drift in patterns is called data drift. Mathematically, data drift happens when the underlying statistical properties of training data differ from that of inference data once they are deployed. This gradual process can be caused by various factors, including behavioral changes, seasonal trends, or external factors like economic downturns or sensor malfunctions. Over time, data drift leads to model output degradation. 

Data drift can be detected using several statistical metrics, such as the KL Divergence test and the population stability index, based on the difference in the probability distribution of the data sets. Open-source frameworks like LangSmith also help detect drift through input and LLM output monitoring. However, LLM drift detection is harder because of the unpredictable nature of the inputs they may receive. 

AI data governance implementations have automated mechanisms to periodically compute drift metrics and prevent diverging data. 

Guards against adversarial risks

ML models face cyber attacks that try to trigger unfavorable output behaviors, such as sensitive data leakage and erroneous classifications. For example, an attacker may trick a chatbot by providing an adverse prompt to provide harmful responses. A fraud detection model may also be fed modified transaction details to trigger an incorrect fraud classification. AI data governance helps implement guardrails against such manipulations by emphasizing secure and consistent data. 

Ethical AI considerations

Enterprises must address transparency, accountability, fairness, privacy, and other ethical concerns to ensure the fair and responsible use of AI. 

Bias in training data can lead to skewed results that favor or reject a specific cohort. For example, a facial recognition model trained to detect known criminals that exhibits a bias toward a particular demographic could lead to catastrophic outcomes. AI data governance emphasizes keeping the training data free of such biases. 

Ethical considerations around data ownership and consent are another critical aspect of AI data governance. Individuals own their personal data, such as social media posts and health records. Users must understand how their data is collected and used, and governance structures must accommodate their preferences. 

Key aspects of AI data governance

AI data governance combines traditional data governance with AI-specific considerations.

Below are the key aspects of AI data governance.

Data quality

Poor data leads to incorrect model outcomes. High-quality data is

• Free of errors and accurate.
• Complete without any missing values.
• Correctly formatted and validated for any deviations
• Up to date with changes reflected within the defined SLAs.

Data must also demonstrate integrity. In other words, the relationships between various data elements must remain consistent throughout their lifecycle. 

Data security

Training and inference data security are essential to guard models against attacks and meet compliance standards. The first step in securing data is having role-based access control. Encryption guards against data poisoning and ensures regulatory compliance. Data anonymization and masking ensure that raw data is inaccessible to developers or as model training data. Many data integration frameworks provide automatic PII masking steps in their data workflows. 

Data lineage

Data lineage captures the origin and transformations the datasets undergo throughout their lifecycle before being used by an AI model.  A complete record of lineage improves the trust and transparency of the models trained based on the data.

Even when trained with the same data, models often struggle to produce the same results across different training iterations. This is because of the inherent randomness in the model training process. Having lineage details of training data helps with model reproducibility and debugging model outputs. Manually tracking lineage within the vast data universe is not feasible in the modern era. Organizations must use data integration tools like Nexla that automatically capture lineage information. Nexla’s automatic lineage tracking can help organizations do this seamlessly.

Nexla data lineage view for a single record

Metadata

Metadata is structured information that describes data characteristics. Metadata helps developers understand the data’s meaning and structure. It contains technical details on schema, data types, storage information, business-level information about definitions, access policies, usage guidelines, and compliance tags. Another aspect of metadata is labels and annotations used by downstream AI models for training. 

Metadata plays a key role in model explainability and quick information retrieval. Gen AI architectural patterns like RAG rely on metadata to fetch the correct context information. In reality, knowledge or data assets within a large organization often lack comprehensive metadata associated with them. For organizations operating within a vast data landscape, manually adding metadata to all datasets may not be feasible either. Traditional metadata management relies on a rule-based, static approach. In contrast, modern metadata intelligence advocates for dynamic, AI-driven, active metadata management with contextual understanding. Moving from traditional metadata management to metadata intelligence is key in the era of AI data governance. You can read more about active metadata intelligence here. 

Data integration tools with no-code metadata management speed up the migration to active metadata intelligence. For example, Nexla, a data integration platform, uses a concept called Nexsets to generate metadata automatically. Nexsets are data products bundled with metadata and context information. Nexla uses API documentation, access details, rate limits, and data samples to auto-generate metadata. It continuously updates and versions the schema. 

Nexla metadata view for Nexset

Data privacy

Data collected by organizations about individuals needs to be handled carefully, adhering to legal frameworks in the region of operation. Once this data is used for training, it becomes embedded in the models. An overfitted model can even output the same data it was trained on. Data privacy protects the rights of individuals and ensures there are no reidentification risks from models. Organizations must use systematic AI data governance models to ensure that data used for training is fully anonymized. It is possible to set up automated guardrails to ensure this. 

Compliance

An organization’s AI data governance model provides the foundation for adhering to regional compliance standards. Several governments have developed compliance guidelines specific to AI usage, such as the EU AI Act and the NIST Risk framework for Gen AI. For example, the European Union AI Act defines risk profiles and legal guidelines that organizations must consider when using AI applications. 

NIST defines a risk management framework for organizations to identify and mitigate the unique risks of generative AI. Compliance standards often include regulations that are hard to enforce in the AI world unless implemented at the beginning of the AI journey. For example, GDPR stipulates that individuals have the right to be forgotten. This poses a complex challenge for data already used for training. 

Data bias

AI models exhibit the same bias present in their training data. A key aspect of AI data governance is to ensure that data is free from bias related to gender, race, and any other demographic factors. However, writing rules to detect biased data is challenging. Statistical bias detection techniques, like mean and distribution analysis, uncover if any specific group has a higher representation within the training data. One can use fairness metrics like demographic parity to establish rules about training data. Frameworks like Fairlearn and fairness diagnostic tools like h2o.ai help with bias detection. One can implement bias detection utilities using such frameworks and link them to the data integration tools that support automatic governance enforcement. 

Data relevance

Data relevance refers to how training data relates to the data input to the model while in production. Ensuring that training data comes from the same distribution as the inference data is vital to getting correct results. Training data must be relevant and specific to gender, topic, and time. For example, one can not use the men’s apparel purchasing history data during Christmas to train a recommendation model to recommend apparel during other seasons. Ensuring data relevance is more than just using data governance tools. It requires deep domain knowledge and establishing rules based on the domain knowledge. 

Best practices in AI data governance

Establish a clear responsibility matrix

Implementing and maintaining an AI data governance framework requires collaboration among several stakeholders. To avoid conflicts, define clear roles and responsibilities early in the lifecycle. 

In addition to technical roles like data scientists, data engineers, IT professionals, and product owners, several governance-specific roles must be established to execute AI data governance. For example,

• Legal and compliance experts are responsible for defining the regulations that must be adhered to according to the regional legal framework. 
• The AI governance lead is responsible for defining data governance policies and frameworks aligned with business objectives. 
• The governance committee is a stakeholder group responsible for overseeing governance policies and ensuring risks related to ethical and fair usage are adequately covered. 

Establish metrics for measurement

An AI data governance program is not something one can set up once and forget. It needs to be continuously monitored and improved as the organization evolves. It is important to define metrics for the key aspects of data governance. One should define metrics regarding data quality, security, metadata, and lineage, and develop methods to track them throughout the data governance journey. The following are some of the key metrics that organizations can track to measure the state of their AI data governance. 

Data metrics

• Accuracy
• Completeness
• Timeliness
• Percentage of duplicate records
• Integrity

Security-related metrics

• Number of incidents
• Mean time to detect
• Mean time to resolve
• Percentage of encrypted sensitive data

Lineage-related metrics

• Lineage coverage
• Lineage depth
• Number of orphan datasets

A data integration platform with built-in monitoring features can help track these metrics. Defining the metrics and tracking them alone does not solve the problem, since the original metrics can quickly become irrelevant as the journey progresses. Organizations must establish an overseeing committee to introduce new metrics as AI needs evolve and ensure continued focus.  

Define compliance and access control policies early in the lifecycle

Organizations must define compliance policies early in the AI implementation journey. Compliance standards vary according to the region of operation, and the organization’s legal team must be involved in defining them. Compliance policies regarding data ownership and consent are difficult to implement once the AI journey passes a critical point, so it is essential to start this process early. 

Democratize access through a centralized data catalog

An essential part of AI data governance is maintaining a centralized data catalog with anonymized data and role-based access control. It democratizes data access within the organization and fosters a culture of innovation. One can use specialized tools like Alation, Collibra, data.world, etc, to build a data catalogue. Such tools do a good job of storing the metadata and making it accessible to stakeholders within the organization. That said, adding the relevant information to these tools is still a task that requires developer involvement. Data integration platforms like Nexla, which can integrate with popular data catalog tools, can help reduce this developer effort. You can read more about building an enterprise data catalog here. 

Select tools and frameworks with built-in data governance

Data governance tools help to accelerate the AI data governance journey. Several stand-alone data governance tools exist in the AI data governance space. Databricks Unity Catalog and Apache Atlas are open-source tools tailor-made for AI data governance. The best-case scenario is to use a data integration framework with built-in data governance abilities. This helps streamline the development process by managing all the activities related to your AI journey in one place. Nexla is an all-in-one solution for multi-speed data and AI integration tools. It comes built-in with automated lineage capture, data quality checks, and role-based access control. 

Last thoughts

AI data governance defines a set of processes and policies to ensure that data used for AI model training and inference is secure, of good quality, and free from bias. It also defines the compliance policies that data processes must adhere to and establishes automated validation mechanisms to prevent violations. Using AI data governance tools can reduce effort during implementation. They come with pluggable components that can be integrated based on the organization’s use case. Using a data integration tool with built-in AI data governance goes one step further by keeping all the data management in one place. Nexla is a good alternative for an all-in-one data integration tool with built-in AI data governance features.