Private Ai Deployments For Banks

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Feb 27, 2025

6 minutes read

6 reasons banks opt for private AI deployments

Control over data, enhanced security, and customization make private deployment the smart strategic choice for financial firms.

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Think of a traditional bank and one of the first images that come to mind is a secure vault to protect cash and valuables.

As modern banks adopt AI for uses ranging from customer service to fraud detection, the value of a vault is again being recognized. Financial institutions are finding that deploying AI models privately confers crucial advantages over relying on third-party cloud providers.

Private deployments – whether on-prem or through virtual private cloud (VPC) – are the equivalent of a vault, giving banks close control over information storage, processing, and use.

There are many considerations to weigh when choosing between private deployments and AI managed service offerings from a cloud provider. The following are six areas in which we’re seeing private deployments deliver clear advantages for banks.

1\. The data security imperative

Data security and the customer trust that derives from it are the primary drivers for private AI deployment in financial services.

Banks handle huge amounts of sensitive customer information, including transactions, personal identification, and credit histories. By deploying their entire AI “stack” private deployments, organizations can eliminate their exposure to risk through a shared responsibility model with cloud providers.

Even though most cloud providers have robust security measures, they can’t provide the reliable air gap that private deployment ensures. Physical and network isolation protects banks from the vulnerabilities of shared environments and minimizes the risk of breaches.

A recent report revealed that nearly half of organizations experienced a cloud data breach in the past year, and research has found that security breaches are more common and costly for financial firms. Just one breach is all it takes to undermine customer trust, erode an institution’s reputation, and set back AI adoption efforts.

Maintaining strong security for its customers’ data while obtaining deeper AI-driven insights into their financial needs was a key motivator in Royal Bank of Canada’s decision to opt for a private deployment. By partnering with Cohere to launch the North for Banking platform, RBC looks to achieve strategic innovation while prioritizing security and data privacy protections and enabling it to scale the capability across banking applications.

“We feel that by having these technologies on our servers, with secure access to RBC data, we can unlock a ton of opportunity for how we use them,” says Foteini Agrafioti, RBC’s senior VP for data and AI. “You can do more interesting things with them when they can see our data sets.”

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_For more on how enterprises can safely and securely deploy AI in private environments, see our latest report on_ _AI Security_ _._

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2\. Staying ahead of regulations

This enhanced control over data also gives financial companies an advantage in navigating the growing complexity of data privacy laws and AI regulations.

For example, Europe’s General Data Protection Regulation (GDPR) contains strict data residency requirements—something banks can more easily adhere to if their data is stored on premises. Similarly, a growing number of U.S. states are introducing data privacy laws that mandate controls over how customer data is used and stored.

Private deployment also simplifies compliance with industry-specific regulations such as Basel III and PCI DSS, which emphasize data security, auditability, and risk management. The European Union AI Act identifies some financial use cases as “high risk,” making them subject to additional risk management and data governance requirements. By keeping data entirely within their control, financial firms can ensure adherence to these rules without relying on third-party assurances.

As regulations tighten around consumer privacy and the use of copyrighted data in AI training, banks investing in private deployments now are better positioned to avoid costly overhauls in the future.

3\. Reduced latency, better performance

Minimizing latency is critical for real-time financial applications such as high-frequency trading, fraud detection, and risk modeling. Privately deployed AI solutions allow financial institutions to process data locally, reducing reliance on network connectivity and cloud-based processing delays.

While cloud-based AI solutions are scalable, they may introduce unpredictable latencies due to network congestion, bandwidth limitations, or service disruptions. In contrast, private deployments provide predictable, high-performance AI processing tailored to an institution's specific workload demands.

4\. Customization...