Comment: The UK can lead AI growth without compromising trust

From aerospace cybersecurity to detecting fraud in financial institutions, AI is already part of making decisions that influence lives, businesses and even national security. And as its application grows, trust in the reliability of AI systems becomes not only a nice-to-have; it becomes an essential.

Sectors like aerospace, defence and finance have spent decades refining safety and risk management practices to earn public trust and build industry confidence in the process. But AI, as a new kid on the block, is at the start of building trust in its capabilities. So, if businesses are to integrate AI successfully into their operations, as outlined in the government’s AI Opportunities Action Plan, the smart approach might just be to learn from the sectors that have mastered risk while maintaining innovation.

And while the government’s plan to focus on responsible AI governance and ethical data use is a step in the right direction, balancing technological progress with security and transparency remains a challenge. If the UK is to scale AI adoption and build confidence, industry leaders, regulators and policymakers should ensure that AI systems are secure and explainable to maintain public confidence.

AI awareness is high, but trust is fragile

While awareness of AI is widespread, understanding of how it works and what it can be applied to is mixed, particularly among people outside the technology sector. Public trust in AI isn’t just about perception; it’s about action. Many remain unconvinced, with transparency and fairness emerging as major fault lines in the debate. If AI is to gain real credibility, these concerns can’t be brushed aside.

The lack of clear explanations about how AI makes decisions has contributed to growing scepticism, especially in sectors where human oversight is necessary. In finance and defence, where AI is increasingly being integrated into risk-sensitive environments, concerns over accountability and bias remain high.

As AI takes on a greater role in decision-making, questions arise about how and why these systems reach their conclusions. The more AI influences critical choices, the stronger the demand for what is being called ‘explainability’ and fairness arise. Without proper safeguards, trust in AI could be hard to build, limiting the UK’s ability to scale adoption across critical industries.

Security first

Trust is not just about public perception. Organisations must also have confidence in AI systems, particularly in high-stakes sectors where AI is both a security enabler and a potential risk factor.

Zero Trust architecture is a foundational security model for AI deployment. Unlike traditional security approaches, which assume implicit trust within a network, Zero Trust requires continuous verification at every access point, reducing the risk of unauthorised access, data breaches and AI-driven cyber threats. This is particularly important as AI itself plays a dual role in cybersecurity, enhancing threat detection and response, while also introducing new vulnerabilities.

Bad actors can manipulate AI systems by altering training data or injecting misleading information to exploit decision-making gaps in machine learning systems. These vulnerabilities highlight why AI security frameworks must be rigorously tested and continuously monitored. Implementing Zero Trust alongside AI-driven security models can help organisations mitigate risks and build the resilience of AI systems in critical applications.

Cybersecurity is a constant game of cat and mouse, with companies and their consultants working behind the scenes to stay ahead of bad actors. Because of this, examples of successful implementation are often kept under wraps, but what’s clear is that organisations are doubling down on AI-driven security to keep pace with evolving threats.

Lessons from high-stakes industries

What lessons can be learnt then from sectors where AI is advancing? In aerospace it’s being used to great effect to support flight automation and predictive maintenance, while also playing a role in cybersecurity. However, as outlined in the NIST Trusted AI Framework, ensuring AI reliability requires rigorous validation and continuous oversight to prevent unintended failures or security breaches. Without clear governance structures, AI systems could introduce new safety risks rather than mitigating them.

In the defence sector, AI is increasingly being integrated into command-and-control systems, raising ethical concerns about autonomy in decision-making. A TechUK report stresses that transparency, accountability and human oversight must remain central to AI adoption in defence. Without clear ethical guidelines, the deployment of AI in military applications risks public backlash and regulatory challenges.

The financial sector, where AI is being used for fraud detection, risk analysis and algorithmic trading, faces similar pressures to maintain trust. AI-driven compliance mechanisms are needed to ensure regulatory alignment, particularly in areas like automated credit scoring and algorithmic decision-making. If customers and regulators lack confidence in the fairness and security of AI-driven financial systems, adoption could stall and limit innovation in one of the UK’s most important industries.

The way forward

Industries like aerospace, defence and finance have long had to balance innovation with security. Their experience in enforcing strict safety protocols, navigating complex regulations and managing risks provides a blueprint for how AI can be scaled responsibly while keeping public and industry confidence intact.

Sustaining AI growth without compromising trust requires a balance of strong regulation, clearer transparency in decision-making and closer ties between industry and government. Stronger regulation would help keep pace with AI advancements and provide clear guidelines to address concerns around fairness and security while ensuring accountability. Establishing a legal and ethical framework for AI will not only help mitigate risks but will also reassure businesses and consumers that AI is being deployed responsibly.

Transparency should also be a priority. AI systems should be auditable and interpretable, rather than operating as black-box decision-makers. If AI is making decisions in high-stakes industries, businesses and regulators must have clear insights into how those decisions are reached. Investing in explainability tools will help AI operate within ethical boundaries and maintain the trust of both users and the wider public.

The AI trust challenge cannot be solved in isolation. By working together, businesses, regulators and cybersecurity experts can develop standardised, secure AI frameworks that foster trust while allowing innovation to thrive. AI governance must adapt to emerging threats and shifting public expectations as technology evolves.

AI can either follow the example of industries that have built trust over time, or risk losing credibility before it ever reaches its potential. Sectors like aerospace, defence and finance have shown that innovation and safety can go hand in hand. The UK has an opportunity to lead, but success will depend on making trust a priority from the outset rather than something to fix later.

Jeff Hoyle, EVP/ MD UK & NA at Expleo