Building trust in AI
Image courtesy Expleo
AI is increasingly being used for decisionmaking, from strengthening cybersecurity in aerospace to identifying fraud in the financial world. And with this growing role comes a significant need for systems to prove their integrity to offer reliability and security in equal measure. Without such guarantees and right guardrails in place, AI’s promise risks being overshadowed by trust issues and doubts.
To make the leap, AI can learn a thing or two from sectors like aerospace, defence and finance, which have long histories of managing risk and maintaining high safety standards. These industries have built public trust and reputational equity through stringent protocols and robust oversight – something AI needs time to build.
If the UK is to unlock AI’s transformative power for business, as outlined in the AI Opportunities Action Plan, the technology will need to generate it’s own legacy of credible governance and accountability.
Also, while the UK government’s call for responsible AI and ethical data use is an important starting point, establishing real trust requires more than policy. It demands a shift in how AI systems are designed, implemented and monitored. Clear safeguards and transparency must become the foundation of AI’s development.
Understanding the trust deficit
AI is widely recognised as a transformation technology, but recognition alone doesn’t translate to trust. Outside the tech world, many people remain unsure about how AI works and whether it’s being used responsibly. For AI to gain widespread acceptance, it must address not just performance but also fairness and explainability.
A significant trust barrier lies in the ‘black box’ nature of many AI systems. Their decision-making processes often remain opaque. This is especially concerning in critical sectors like finance and defence, where human oversight can mean the difference between safety and catastrophe. As AI’s role expands, so too does the demand for systems that can clearly explain how and why they reach their conclusions.
Why security is non-negotiable
Trust in AI does not just depend on transparency, but security plays an equally important role. In industries where AI is both an asset and a potential vulnerability, strong security measures are indispensable.
A Zero Trust security model offers one solution. Unlike older frameworks that assume internal trust, Zero Trust requires ongoing verification for every access attempt, treating every user and device as a potential risk until proven otherwise. This minimises the risk of data breaches, unauthorised access, and insider threats. In AI systems, where vast amounts of sensitive data are processed and decisions are made at scale, this approach provides an essential safeguard against misuse and exploitation.
As well as this, AI systems themselves can be targeted through tactics like data manipulation, adversarial attacks or model poisoning, where malicious actors distort training data to influence AI behaviour. These vulnerabilities highlight the need for consistent monitoring, rigorous testing and advanced security frameworks. Implementing continuous auditing, anomaly detection and real-time oversight ensures AI systems remain reliable and resilient, even in the face of sophisticated cyber threats. By prioritising security at every stage of development and deployment, industries can harness the power of AI without compromising safety or trust.
Lessons from high-stakes industries
Industries that manage significant risk offer valuable guidance on how AI can build trust while driving innovation. In aerospace, AI supports flight automation and predictive maintenance, increasing efficiency while enhancing safety. But these benefits only withstand through constant oversight and adherence to trusted frameworks like the NIST Trusted AI Framework.
The defence sector brings its own set of challenges, with AI’s involvement in autonomous systems raising ethical and operational questions. AI-driven surveillance, logistics, and decision-support tools can increase efficiency and responsiveness, but they also demand careful governance. Reports from TechUK underscore the importance of keeping human oversight central to military AI applications, ensuring accountability and preventing misuse. The consequences of automated decisions in combat scenarios, for instance, make transparency and control non-negotiable.
In finance, AI’s ability to detect fraud and manage risk has transformed the industry. AI-driven tools analyse vast datasets at incredible speeds, identifying suspicious patterns and preventing fraudulent transactions before they occur.
Yet without fair and transparent compliance measures, the technology could undermine customer trust and regulatory confidence. Financial institutions must balance innovation with responsibility, ensuring that AI systems are accurate, explainable and aligned with legal and ethical standards. By maintaining this balance, the financial sector can continue using AI to enhance security and efficiency without sacrificing fairness or trust.
A roadmap for responsible AI
The experiences of aerospace, defence and finance sectors demonstrate that innovation and safety aren’t mutually exclusive. Their well-established risk management practices offer a roadmap for scaling AI responsibly.
For the UK to lead in AI adoption, it must prioritise robust regulation, clear accountability and collaboration between industry and government. By developing strong, adaptable guidelines on fairness and security, the country can foster innovation without sacrificing trust.
Equally critical is making AI systems more interpretable. In high-stakes environments, businesses and regulators must understand how AI reaches its decisions. Investing in explainability tools will help align AI systems with ethical standards and reassure the public.
Building trust in AI requires a collective effort. By working together to create standardised, secure frameworks, the UK can position itself as a global leader in responsible AI development. Learning from industries that have already mastered risk management will ensure that AI’s evolution is both innovative and trustworthy.
