The rise of artificial intelligence (AI) presents a transformational opportunity for data management, particularly in data governance. However, many organizations rush to adopt AI without first establishing the necessary data foundations including data quality, governance, interoperability, security, access, lineage, and trusted sources. This lack of foundation can lead to unreliable outputs, compliance risks, and AI-driven decisions that perpetuate biases.

Yet, AI itself can play a critical role in strengthening data governance. While AI depends on high-quality data, it can also be a tool to help establish and enforce those foundational principles.

In this article, we will explore the risks of using AI without a strong data foundation, how AI can help build that foundation, and how AI can mitigate its own biases.

The risk of AI without a data foundation

AI is only as good as the data it learns from. If an organization lacks data governance, AI models will struggle with.

1. Poor data quality: AI models trained on incomplete, inconsistent, or duplicated data will generate unreliable insights.

2. Lack of interoperability: Siloed data limits AI’s ability to provide cross-functional insights, leading to fragmented or misleading conclusions.

3. Security and access concerns: Without strong access controls, AI may be exposed to sensitive data it should not process, leading to regulatory violations.

4. Data lineage and trust issues: AI models require transparency about where data comes from and how it has been transformed. Without lineage tracking, it is impossible to verify AI-generated results.

5. Reinforced bias and ethical issues: AI can amplify existing biases if it is trained on historical data that reflects past human or systemic prejudices.

A lack of governance in these areas results in the age-old “garbage in, garbage out” making AI unreliable or even dangerous in decision-making.

How AI Can strengthen data governance

While AI requires a strong data foundation, it can also help build and maintain that foundation. Here’s how AI-driven solutions can support key data governance pillars:

1. AI for data quality improvement: AI-powered data profiling tools can automatically…

• detect and correct duplicate records, missing values, and formatting inconsistencies.

• identify outliers and anomalies, flagging potential errors in datasets.

• apply natural language processing (NLP) to standardise unstructured data (e.g., extracting structured information from text-heavy documents).

Example – AI can scan customer databases and auto-correct errors (e.g., identifying that Jhn Smth is likely John Smith).

2. AI for interoperability and data mapping: AI-powered data catalogs can…

• identify relationships between datasets, enabling seamless integration.

• automate schema mapping across different systems, reducing manual effort.

• enhance metadata tagging for better discoverability.

Example – AI can map similar but differently named fields (e.g., recognizing that Cust_ID in one system is the same as Customer Number in another).

3. AI for security and access control: AI enhances data security by…

• detecting unusual access patterns and preventing unauthorised data use.

• automating role-based access control (RBAC) recommendations.

• using self-learning algorithms to identify and classify sensitive data.

Example: AI can recognize sensitive information in unstructured data, ensuring compliance with regulations like GDPR or HIPAA.

4. AI for data lineage and auditability: AI can automate…

• tracking data flows across an organisation, ensuring full transparency.

• flagging undocumented transformations, preventing data manipulation errors.

• generating automated documentation, reducing manual effort.

Example: AI-powered lineage tools can visualize how a data point in an actuarial risk model was derived from raw financial data.

5. AI for bias detection and mitigation

Since AI can inherit bias from historical training data, organisations must proactively use AI to detect and mitigate these biases. AI can…

• analyze historical patterns for bias (e.g., reviewing hiring data to detect gender or racial imbalances).

• generate synthetic, bias-corrected data to retrain models.

• provide explainability tools to ensure AI-driven decisions can be audited.

Example: AI used in credit scoring can identify if certain groups have been unfairly penalised due to historical biases and suggest data adjustments.

Balancing AI innovation with governance

For organizations to successfully adopt AI in data management, they must approach it as a two-way relationship.

• AI needs strong data governance to function effectively.

• AI can improve data governance by automating compliance, improving data quality, and reducing human errors.

A governance-first approach ensures that AI is deployed responsibly, with clear rules around data ethics, transparency, and accountability.

Practical next steps for organizations

1. Assess data governance maturity: Identify gaps in quality, security, and compliance before scaling AI.

2. Adopt AI-powered data tools: Use AI for data profiling, lineage tracking, and security monitoring.

3. Implement bias audits: Regularly review AI models for unintended biases and retrain with balanced datasets.

4. Enforce AI explainability: Require that all AI-driven decisions are traceable and understandable.

5. Develop cross-functional AI and data teams: Align data governance, compliance, and AI engineering teams to co-design responsible AI policies.

By embedding AI within a governance-first data strategy, organisations can enhance data integrity, compliance, and security, while unlocking AI’s full potential.

AI and data governance are not opposing forces they must work together. Organizations that rush into AI adoption without a solid data foundation will struggle with unreliable, biased, and non-compliant systems. However, when used correctly, AI can enforce governance, improve data quality, automate security, and mitigate bias.

The future of AI-driven data management lies in building governance-first AI systems, ones that are not only innovative but also transparent, ethical, and sustainable.

Would your organization rather let AI govern its data or let bad data govern its AI?

I believe the choice is clear.

About the Author

Tina Salvage is Lead Data Governance Architect – Group Functions, Bupa Global. She is an experienced management professional with a strong background in the financial services industry, specializing in data management and governance. Salvage has extensive experience in financial crime compliance and money laundering. Her passion lies in building data management strategies that enable organizations to achieve their goals.

She has a proven track record of creating and embedding strategic transformational change to business processes and systems across departments, working closely with key stakeholders, external suppliers, and the executive board. At Bupa, Salvage focuses on building strong relationships to enable others to thrive. She shares the story, attracts the right people, and helps deliver the data strategy.