A Maturing AI Assurance Ecosystem: Mapping Sector Specific Applications

This paper examines how AI assurance is currently being applied across five key sectors: Justice and Emergency Services, Financial Services, Education, Health and Social Care, and Defence. These sectors share important characteristics: all are safety-critical, operate with a low-risk appetite, and function within highly regulated environments, and some as critical infrastructure.

Based on qualitative research from techUK's June 2025 Sector Specific AI Assurance Event Series, this paper reveals that while substantial sector-agnostic AI assurance expertise exists, each domain's unique risk profile, regulatory environment, and operational constraints shape how ethical principles are prioritised and implemented with assurance techniques.

By platforming existing tools, while working to understand the current sectoral approaches we can consider where sectors of different maturity could learn from each other through more formal mechanisms and concurrently support a more cohesive AI assurance ecosystem for the UK.

The paper identifies three areas requiring policy and market attention to strengthen the UK's AI assurance ecosystem:

Building on techUK's Digital Ethics Working Group's previous work on operationalising ethical principles and developing skills for responsible AI practitioners, this paper aims to continue our support of developing the UK's AI assurance ecosystem.

For public services examined in this paper, AI assurance provides benefits for building and maintaining public trust. Unlike commercial applications with market feedback mechanisms, AI in the public sector directly shapes access to essential services, often for citizens with no alternative provider. Public trust is therefore critical. However, these sectors face unique challenges: strict duties of care, sensitive data, and accountability to the public. Additionally, funding disparities mean that some services may have more resources for AI pilots than others.

Given these challenges, this paper makes the case for increasing cross-sector knowledge sharing in AI assurance and offers suggestions for how this could be achieved. It spotlights the current application of AI assurance in the financial services industry, including the specific tools and practices in use, which other sectors could learn from.

AI assurance is the 'how' of responsible AI (the practical methods and processes that demonstrate that evaluate, measure and communicate AI system trustworthiness based on reliable evidence). It differs from regulation (the 'what'), though both work together to create effective AI governance.

Such mechanisms could prevent unnecessary duplication of effort and accelerate AI assurance maturity across all sectors simultaneously.

These three recommendations, taken together, could position the UK to lead globally in responsible AI adoption and assurance whilst maintaining the innovation-friendly environment essential for technological advancement and economic competitiveness.

The UK possesses the necessary foundation, the expertise, the sectoral diversity, and the regulatory sophistication to demonstrate how nations can cultivate mature AI assurance ecosystems that give innovators, investors, and the public confidence in AI systems through evidence-based assurance, enabling both innovation and widespread AI adoption. Realising this opportunity, however, demands immediate and coordinated action. By focusing on the 'how' of AI assurance (the practical implementation of tools, standards, and shared practices), the UK can transform ambitions into tangible outcomes that support citizens and empower organisations to deploy AI responsibly and effectively.

We hope you find the report insightful, and encourage you to get in contact with the team below regarding any questions. You can also sign up here to get the latest digital ethics updates, as well as information on how to get involved in techUK's digital ethics work.

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