The search for transparency and reliability in the AI era

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The search for transparency and reliability in the AI era Opinion By Michael Hanratty published 3 December 2025

Transparent and robust data residency ensure the confidentiality of clients’ data

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Generative AI is taking organizations to new realms of efficiency, innovation, and productivity. Just like the technological innovations that came before it – from the industrial revolution to the rise of the internet – the AI era will see businesses continue to adapt in order to capitalize on the most efficient processes possible.

Michael HanrattySocial Links Navigation

Chief Technology and Information Officer at HGS UK.

Amidst the AI boom, organizations can now hyper-personalize customer experiences and identify critical business trends at record speeds. However, the data required to produce these transformative outputs is often highly confidential and extremely sensitive.

If a company’s data is unknowingly passed to third or even fourth parties following the use of AI tools, the implications could not only compromise client trust but also weaken their competitiveness.

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The data security issue

The business world is now firmly in the age of AI, where companies are undoubtedly seizing the tangible benefits of the technology. Nevertheless, firms are also facing the significant risks associated with misusing this technology. Increasingly, there have been incidents of AI providers misleading clients on how their data is used.

For example, OpenAI was fined €15 million for deceptively processing European users’ data when training its AI model, while the SEC penalized investment firm Delphia for misleading clients by falsely claiming its AI used their data to create an ‘unfair investing advantage’.

These recent instances of high-profile breaches of trust are raising alarm bells among businesses. There are growing fears that AI enterprises are acting in a deceptive manner.

As a result, potential clients are reconsidering their use of AI and are hesitant to share personal data with providers. In fact, some companies are hesitant to invest in AI tools all together.

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According to KPMGs global study from earlier this year, more than half of people are unwilling to trust AI tools – expressing conflict between its clear advantages and perceived dangers, such as concerns regarding their data resides.

This poses a significant question for AI providers: how can they raise trust surrounding AI and data security?

The path to trust: data residency and transparency

For AI providers, honesty translates to transparency – this is a crucial first step to rebuilding trust. Being upfront about who data is shared with, or what it’s being used for, informs individuals before they entrust AI applications with their valuable information.

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This is essential regardless of whether the client agrees or disagrees with the policy.

Providing businesses with a transparent overview extends to clarity in data residency. Displaying the physical or geographical location of where data is stored and processed removes the uncertainty and speculation linked to AI.

If clients are given visibility into their data usage, their fear of the unknown diminishes, bringing the ‘invisible’ space into viewpoint.

A combination of transparency and residency moves beyond efforts to rebuild trust. For instance, from a compliance perspective, it helps providers take on a stronger position.

Making the disclosure of data sources used by AI a mandatory measure is the goal of the highly anticipated Data (Use and Access) Bill. Through refining these procedures prior to the implementation of such laws, providers can position themselves in a way that ensures they benefit from any future policy changes.

By implementing these practices, clients will establish trust that their data is protected against the risk of fraudulent activities. Nevertheless, providers must also ascertain that this data is secure from further threats to.

Ensuring data security

Transparency helps to build trust between organizations and their clients, but this is only a first step. Another element to maintaining trust involves data security – where cybersecurity has a crucial role to play.

A combination of outdated IT infrastructure, inadequate cybersecurity funding, and holding onto valuable data are key issues actively fueling most cyberattacks.

In order to show clients that unauthorized access to their data is not an option, AI providers must revamp their security systems. This includes implementing security measures like multi-factor authentication (MFA) and data encryption, which prevent illicit access to vital customer databases.

Moreover, regularly updating and patching security systems prevents threat actors from identifying and exploiting potential vulnerabilities.

Naturally, businesses want to take advantage of AI's unparalleled capabilities to enhance operational efficiency. However, the use of AI will decline if users cannot rely on providers to protect their data – no matter the transparency of their use cases.

Building responsible AI ecosystems

Whilst the capabilities of AI evolve and become more integral to every-day business operations, simultaneously, the responsibilities placed on AI providers continue to rise. If they neglect their duties to keep customer data safe – whether through malpractice or external threat actors – a viral element of trust will be broken between parties.

Establishing client trust requires AI providers to significantly improve data residency and transparency, as this demonstrates a serious commitment to the highest ethical standards for both current and future clients.

Further, it also ensures that enhanced security protocols are clearly perceived as foundational to all operations and data protection efforts. This commitment ultimately strengthens organizational trust.

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TOPICS AI Michael HanrattySocial Links Navigation

Chief Technology and Information Officer at HGS UK.

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