AI data protection as a foundation for responsible AI use

It’s a familiar paradox of progress: the more powerful technologies become, the more risks they create. Artificial intelligence is no exception. As AI transforms how organisations collect, analyse, and act on data, it also expands the ways personal and sensitive information can be used beyond original intent, often opaquely and at scale.

AI data protection sits the centre of this conversation. With nearly half of UK SME leaders citing data privacy and security as a barrier to AI adoption, it has become a defining governance and risk discipline that enables organisations to use AI responsibly, build trust, and scale innovation with confidence. Understanding what AI data protection truly involves is essential for exploring how it helps organisations deploy AI in a lawful, ethical and sustainable way.

What does AI data protection mean in practice

AI data protection is about governing how data behaves when it enters an AI system. It encompasses the governance, strategies, and controls that ensure data used by AI systems is handled fairly, transparently, and safely throughout the AI lifecycle – from design and training to deployment and ongoing uses.

Unlike traditional data protection, which focuses on collecting, storing, accessing, and deleting data, AI transforms data. It aggregates information at scale, produces probabilistic outputs, and generates conclusions that were never provided. In short, AI data protection addresses how data is converted into models, how those models generate insights, and how decisions are made, explained, and challenged.

Key AI data protection issues organisations face

Identifying risks is the first step to managing them. Hence, before organisations can govern AI responsibly, they must understand the data protection issues that sit beneath its use and undermine even the most well-intentioned AI initiatives.

Lawful basis, transparency, and explainability

Establishing a lawful basis for using data in AI system is rarely simple. Data collected for one purpose is often later reused for model training or analytics, and while the original use may have been genuine, these downstream applications can exceed original expectation, particularly when AI generates new insights or predictions.

At the same time, explainability and transparency remain persistent challenges. McKinsey’s 2024 global AI survey found that 40% of organisations see them as critical to adopting generative AI. But the ‘black box’ nature of AI models makes it difficult for organisations to understand how decisions are reached, leaving them uncertain about the rationale behind outcomes and hesitant to rely on them.

Bias, discrimination, and data quality risks

AI systems inevitably reflect the data they are trained on. When historical data embeds bias, structural inequality, or incomplete perspectives, AI can inherit those patterns, reinforce, and amplify them at speed and scale, embedding discrimination into automated decision-making.

This risk is well exemplified by a research from the University of Washington Information School. In a study of 500 candidate resumes, AI screening tools favoured white-associated names 85% of the time, while female-associated names were favoured just 11%. The model wasn’t designed to see race or gender explicitly, but it learned bias from historical signals embedded in the data, demonstrating how data quality and fairness are inseparable from responsible AI use.

Building an AI data protection framework

A credible AI data protection framework isn’t a set of static documents. It’s an evolving capability that must keep pace with changing data, advancing models, and maturing regulatory expectations. And organisations must design it with this reality in mind. Here are a few core strategies that help.

Governance, policies, and accountability structures

Strong governance provides the backbone for responsible AI use. Organisations need clear answers to these fundamental questions to establish an effective governance:

• Who owns AI-related data risks?
• Who is accountable for AI-driven decisions and outcomes?
• How are concerns identified, escalated, and resolved?

This requires:

• Senior-level oversight to set direction and risk appetite
• Clearly defined roles across legal, privacy, risk, compliance, and technology teams
• Formal mechanisms for review, challenge, and decision-making

Integrating AI data protection into existing controls

AI data protection should strengthen, not fragment, the organisational existing risk management. Therefore, rather than creating parallel process, organisations should integrate AI into already established controls, which includes:

• Data protection impact assessments adapted for AI use cases
• Enterprise risk registers that capture model and data risks
• Incident management and escalation processes that account for AI-related failures
• Ongoing assurance and oversight activities

This integration ensures AI is governed with the same discipline as other high-risk activities. It also clarifies: AI isn’t an experimental add-on, but a core operational capability that must meet established standards of accountability, resilience, and trust.

AI data protection risks to monitor over time

Model evolution, reuse, and secondary use of data

The Challenge

AI models are dynamic in nature. They can be retrained with new data, fine-tuned to improve performance, and reused across teams or use cases. Over time, this evolution can change how a model behaves, introducing drift, reducing accuracy, altering outcomes, and reintroducing bias that are difficult to detect.

How to address it:

• Regularly monitor model how models evolve and training data are reused or repurposed across the organisation
• Periodically reassess fairness, accuracy, and proportionality assumptions as models evolve
• Establish clear review points for retraining, secondary use, and material changes

Regulatory scrutiny and enforcement trends

The challenge:

Regulatory scrutiny on AI is rising worldwide. According to the OECD, there are currently more than 2,083 initiatives with only 426 policies adopted. This signifies that although regulators are moving beyond high-level principles to set clearer expectations around AI accountability, transparency, and data protection, organisations are still lagging to keep pace with them.

How to address it:

• Build clear visibility into how AI systems operate, evolve, and make decisions
• Maintain documented justifications for data use and model design aligned with AI regulations
• Embed accountability and escalation pathways into AI governance structures
• Treat regulatory readiness as an ongoing capability, not a reactive compliance exercise

How OneAdvanced can help organisations?

At OneAdvanced, we recognise that strong data protection, governance, and transparency must be foundational to any AI capability. That’s why, we take compliance seriously. Our ISO 42001 certification demonstrate that all our products are governed through a structured, risk-based AI Management System.

This approach ensures clear accountability, robust governance, ethical design principles, and protection of data integrity, security, privacy, and transparency with continuous oversights throughout the AI lifecycle.

As a result, our customers can trust that our use of AI is responsible, well-controlled, and aligned with recognised international best practices.

Explore OneAdvanced AI to see how responsible AI can work in practice.