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.”

For more on how enterprises can safely and securely deploy AI in private environments, see our latest report on AI Security.

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 and model ownership

Financial institutions often develop tailored, proprietary AI models for risk assessment, fraud detection, and portfolio optimization. Cloud-based AI solutions may limit their ability to implement the necessary model customization, architecture modifications, or integration with existing legacy systems. In addition, some cloud providers retain rights over AI model improvements trained on customer data.

Private deployments give institutions full control over their AI models, allowing them to fine-tune architectures to specific applications, retain intellectual property rights over AI model advances, and integrate AI models with internal data sources and legacy systems.

5. Business continuity and risk mitigation

Relying on third-party cloud providers introduces risks such as service outages, geopolitical restrictions, and changes in provider policies. Since service continuity is a prime requirement of financial institutions, this makes self-hosted deployments an attractive option.

Private deployments can help ensure that critical financial services remain operational even during cloud outages or connectivity disruptions. By allowing firms to store and replicate critical models and data on-premises or across multiple private data centers, they allow greater control over redundancy planning, failover mechanisms, and disaster recovery plans.

Some organizations are leveraging digital twins as an innovative solution to enhance their redundancy strategies. A digital twin is a virtual representation of a physical entity or system that allows for real-time monitoring, analysis, and simulation of behaviors and processes. Within a private setting, they allow financial firms to simulate failures, test updates, monitor performance, and assess security risks in a controlled environment.

6. Cost advantages

While private deployments may require significant upfront investments in hardware, software licenses, and infrastructure, they can offer long-term cost advantages for banks – particularly those with more predictable AI workloads.

Cloud-based deployments often involve recurring subscription fees, data transfer costs, and variable pricing based on usage. These costs add up, particularly for financial firms with high computational needs. While a cloud solution can be advantageous for dealing with fluctuating workloads, private deployments allow institutions to optimize their hardware usage, reduce reliance on third-party providers, and avoid vendor lock-in.

Some firms, therefore adopt hybrid AI architectures, balancing cloud scalability for surge workloads with private infrastructure for their core work.

The total cost of ownership for private solutions includes initial setup costs, ongoing maintenance, upgrades, and energy consumption. But many banks have already invested significant resources into this type of infrastructure as they’ve adopted machine-learning capabilities in recent years. This can give them a head start over other sectors – such as the legal industry, for example – that may have less maturity with AI-related technology.

Steps to private deployment

The type of private deployment a banking institution needs will depend on its AI use cases and data requirements. Here are some quick tips to get started:

Define a roadmap.

Start with a clear roadmap of AI application goals and the desired ROI over the next 12–18 months.

Assess hardware requirements.

Different use cases demand different levels of computational power. For instance, simple AI assistants may require entry-level GPUs, while high-volume document processing will need more powerful, higher-end chips.

Optimize for governance.

Establish strong data governance practices, including audit trails and compliance frameworks, to ensure both regulatory alignment and customer trust.

Layer security protections.

Even within a private deployment, financial firms can enhance security with additional measures such as hardware security modules and cryptographic safeguards.

Leverage an AI workplace platform.

Solutions with a pre-built application layer can enable organizations to achieve faster AI deployment and adoption. Features such as pre-built connectors and user-friendly interfaces can help boost employee productivity immediately without the need for extensive customization. Setting this up in a private environment makes it easier to streamline integration, ensure security, and enable immediate productivity gains while maintaining full control. For example, North is a secure, air-gapped AI workspace designed to accelerate secure AI adoption for regulated industries like banking. It seamlessly integrates LLMs, intelligent search, and AI agents into an intuitive platform—boosting productivity without compromising security or privacy.

As the financial sector embraces AI transformation, private deployment provides a rock-solid foundation for growth and innovation.

While cloud-based solutions offer greater initial ease of deployment and scaleability, institutions that prioritize control over their AI models and data infrastructure will gain a competitive edge, while de-risking their exposure to regulatory change, data leaks, and service interruptions.

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Derek McNeil