Publish Date
1 June, 2025
Tushar Dublish
Founder
Generative AI has emerged as a powerful way for organizations to unlock their data to produce game-changing insights. At the same time, though, this has created a growing need to ensure that these insights, and the data underpinning them, are both confidential and secure. In response, a growing number of business leaders are opting for private deployments of AI that give them more control over hardware, software, and data.
In a private deployment, companies implement and run AI system within a controlled, internal environment within their own virtual private cloud (VPC). With VPC, the AI system is deployed within organization's cloud environment which ensures the company's data remain in their own cloud boundary. In such cases, those AI models are used which are offered by the cloud provider such as Gemini in GCP, GPT in Azure and Claude in AWS. These AI model instances are secure and ensures enterprise data security.
Let’s take a closer look at the enhanced security that private cloud deployment provides, along with some other benefits and considerations.
Why businesses choose private deployments?
Businesses choose private deployments to keep their data secure, customize models for better performance, and deliver results faster.
Regulations
A thicket of regulations is growing around data and how it is used by generative AI. In the U.S., healthcare information is governed by rigorous regulations like HIPAA, while in Europe laws such as GDPR and the Artificial Intelligence Act have added to a rising number of guidelines and limitations.
Data Security
If a company isn't using any Enterprise Search platform deployed internally, there’s a heightened risk that employees may a publicly available AI Assistant and feed it sensitive information through their prompts and queries. Those prompts could then be used by the model provider or the cloud vendor to train the model, which could result in the transmission of private data to the outside world.
A recent survey by The National Cybersecurity Alliance found 38% of employees are sharing sensitive data with AI solutions without their employer’s knowledge. And that jumps to 46% for younger Gen Z employees.
Performance
We get the best performance if the company data and the AI platform using the data both resides in the same cloud environment. It leads to superior processing and response performance by minimizing the communication delays that can occur with remote servers.
Customization
Whether they’re an insurer, bank or government department; different organizations often have wildly different needs and opportunities with GenAI. And they may want an AI partner that specializes in customizing the systems for enterprises with very sensitive and domain-specific data. Companies can also fine-tune AI models in private environment which boosts accuracy, relevance and reduces hallucinations
Getting started with private deployments
To get started with private deployments, focus on how you will measure ROI from the beginning and what type of skills and teams you will need to get it right.
Measure the ROI
With every benefit there is a cost. And for organizations looking at improved data protection, customization and speed, there will be additional expenses that come with private cloud deployment. Unlike other SaaS tools where the hosting cost is borne by the software company itself, for private deployment of any application, the hosting cost has to be borne by the organization using it.
But there are some positives here too. The subscription cost is relatively lesser as the client is just paying only for the application and not for infra cost. Infra cost can be optimized if it's under company's control. And the infra cost doesn't scale directly with the number of users unlike the subscription cost. More control equals more clarity around costs.
There’s one more major consideration when it comes to costs. And that’s the intangible price that is paid in the event of a data breach, which is, the loss of customer trust and brand reputation. These developments and others are adding up to significant cost savings for on-prem users.
Reduce your team's dependency to manage the system
AI models and systems are complex, and it often takes a specific set of skills to both set them up and keep them running. Further customization as per your requirements requires integrating customer’s knowledge in the AI infrastructure and AI models.
You need the right partners with a strong AI team, with a ready to deploy, no-code private deployment solution. Businesses can get their on-prem solution up and running in a matter of days and a fully customized solution in a couple of weeks. This is where ActionSync excels, our complete focus is to provide customized on-premise deployment to the clients because data security and trust is paramount for us.
