For banks trying to put AI into real use, the hardest questions often come before any model is trained. Can the data be used at all? Where is it allowed to be stored? Who is responsible once the system goes live? At Standard Chartered, these privacy-driven questions now shape how AI systems are built, and deployed at the bank.
\nFor global banks operating in many jurisdictions, these early decisions are rarely straightforward. Privacy rules differ by market, and the same AI system may face very different constraints depending on where it is deployed. At Standard Chartered, this has pushed privacy teams into a more active role in shaping how AI systems are designed, approved, and monitored in the organisation.
\n\u201cData privacy functions have become the starting point of most AI regulations,\u201d says David Hardoon, Global Head of AI Enablement at Standard Chartered. In practice, that means privacy requirements shape the type of data that can be used in AI systems, how transparent those systems need to be, and how they are monitored once they are live.
\nPrivacy shaping how AI runs
\nThe bank is already running AI systems in live environments. The transition from pilots brings practical challenges that are easy to underestimate early on. In small trials, data sources are limited and well understood. In production, AI systems often pull data from many upstream platforms, each with its own structure and quality issues. \u201cWhen moving from a contained pilot into live operations, ensuring data quality becomes more challenging with multiple upstream systems and potential schema differences,\u201d Hardoon says.
\nDavid Hardoon, Global Head of AI Enablement at Standard Chartered
\nPrivacy rules add further constraints. In some cases, real customer data cannot be used to train models. Instead, teams may rely on anonymised data, which can affect how quickly systems are developed or how well they perform. Live deployments also operate at a much larger scale, increasing the impact of any gaps in controls. As Hardoon puts it, \u201cAs part of responsible and client-centric AI adoption, we prioritise adhering to principles of fairness, ethics, accountability, and transparency as data processing scope expands.\u201d
\nGeography and regulation decide where AI works
\nWhere AI systems are built and deployed is also shaped by geography. Data protection laws vary in regions, and some countries impose strict rules on where data must be stored and who can access it. These requirements play a direct role in how Standard Chartered deploys AI, particularly for systems that rely on client or personally identifiable information.
\n\u201cData sovereignty is often a key consideration when operating in different markets and regions,\u201d Hardoon says. In markets with data localisation rules, AI systems may need to be deployed locally, or designed so that sensitive data does not cross borders. In other cases, shared platforms can be used, provided the right controls are in place. This results in a mix of global and market-specific AI deployments, shaped by local regulation not a single technical preference.
\nThe same trade-offs appear in decisions about centralised AI platforms versus local solutions. Large organisations often aim to share models, tools, and oversight in markets to reduce duplication. Privacy laws do not always block this approach. \u201cIn general, privacy regulations do not explicitly prohibit transfer of data, but rather expect appropriate controls to be in place,\u201d Hardoon says.
\nThere are limits: some data cannot move in borders at all, and certain privacy laws apply beyond the country where data was collected. The details can restrict which markets a central platform can serve and where local systems remain necessary. For banks, this often leads to a layered setup, with shared foundations combined with localised AI use cases where regulation demands it.
\nHuman oversight remains central
\nAs AI becomes more embedded in decision-making, questions around explainability and consent grow harder to avoid. Automation may speed up processes, but it does not remove responsibility. \u201cTransparency and explainability have become more crucial than before,\u201d Hardoon says. Even when working with external vendors, accountability remains internal. This has reinforced the need for human oversight in AI systems, particularly where outcomes affect customers or regulatory obligations.
\nPeople also play a larger role in privacy risk than technology alone. Processes and controls can be well designed, but they depend on how staff understand and handle data. \u201cPeople remain the most important factor when it comes to implementing privacy controls,\u201d Hardoon says. At Standard Chartered, this has pushed a focus on training and awareness, so teams know what data can be used, how it should be handled, and where the boundaries lie.
\nScaling AI under growing regulatory scrutiny requires making privacy and governance easier to apply in practice. One approach the bank is taking is standardisation. By creating pre-approved templates, architectures, and data classifications, teams can move faster without bypassing controls. \u201cStandardisation and re-usability are important,\u201d Hardoon explains. Codifying rules around data residency, retention, and access helps turn complex requirements into clearer components that can be reused in AI projects.
\nAs more organisations move AI into everyday operations, privacy is not just a compliance hurdle. It is shaping how AI systems are built, where they run, and how much trust they can earn. In banking, that shift is already influencing what AI looks like in practice \u2013 and where its limits are set.
\n(Photo by Corporate Locations)
\nSee also: The quiet work behind Citi\u2019s 4,000-person internal AI rollout
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\nThe post Inside Standard Chartered\u2019s approach to running AI under privacy rules appeared first on AI News.<\/p>\n","protected":false},"excerpt":{"rendered":"
For banks trying to put AI into real use, the hardest questions often come before any model is trained. Can the data be used at all? Where is it allowed to be stored? Who is responsible once the system goes live? At Standard Chartered, these privacy-driven questions now shape how AI systems are built, and [\u2026]<\/p>\n
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