What is Shared AI Infrastructure?
Instead of building massively complex and expensive AI systems from scratch, most banks are wisely choosing to partner with specialised technology companies. This means they are using 'shared' or 'multi-tenant' platforms where the same underlying AI model
and infrastructure serves multiple financial institutions. Think of it like a high-tech apartment building: several banks live there, each with their own secure apartment (their data), but they all share the building's foundation, plumbing, and security system. This approach gives them access to cutting-edge technology for credit scoring, fraud detection, and regulatory compliance without the prohibitive cost of building it all in-house.
The Allure of Efficiency and Power
The benefits of this model are undeniable. Banks can deploy powerful AI tools faster and more affordably than ever before. These systems can analyse transaction patterns in real-time to spot fraud before money is lost, help personalise services for millions of customers, and automate routine tasks, freeing up human employees for more complex work. For a country like India, with its diverse consumer base, AI promises to make banking more efficient, accessible, and secure. The appeal is so strong that over-dependence on a few large AI vendors is becoming a notable trend, as recently flagged by RBI Governor Sanjay Malhotra.
The Systemic Risk of a Single Target
Herein lies the paradox: the concentration that creates efficiency also creates risk. When dozens of banks rely on the same core AI provider, that provider becomes a highly valuable target for cybercriminals. A single successful attack on the shared infrastructure could have a cascading effect, disrupting multiple institutions simultaneously. This is what regulators call 'systemic risk'. Instead of a fire in one apartment, an attacker could target the building's foundation, threatening everyone inside. The International Monetary Fund has warned that this shared architecture creates correlated exposure, where a single vulnerability can be exploited across many banks at once.
New Threats: Beyond the Data Breach
The dangers of shared AI go beyond a simple data breach. Cybercriminals are developing sophisticated new attack methods specifically designed to fool or corrupt AI models. One major threat is 'data poisoning', where attackers intentionally feed malicious information into the AI's training data. Research shows that altering just a tiny fraction of the data can be enough to corrupt the entire system, causing it to misclassify fraud or make biased lending decisions. Another risk is 'adversarial attacks', where specially crafted inputs, which look normal to humans, are used to trick an AI model into making a wrong decision, such as approving a fraudulent loan application.
The Regulatory Tightrope
India's regulators are not standing still. The Reserve Bank of India (RBI) is actively addressing these emerging threats. Recently, the RBI proposed a comprehensive framework for model risk management, requiring banks to establish robust governance and internal controls for all AI systems they use, whether developed in-house or by a third party. The guidelines stress the need for banks to maintain a full inventory of their AI models, continuously monitor them for risk, and have the ability to explain AI-driven decisions that affect customers. This proactive stance aims to ensure that as banks innovate with AI, they do so in a way that is responsible, resilient, and maintains the stability of the financial system.
