Wanting to implement AI within your enterprise and not sure which model to choose – private or public? Or maybe you are interested in the security concerns associated with implementing AI models? Or how does compliance become a challenge for an enterprise when implementing AI models? You will find answers to all these questions in this one comprehensive guide.
What do you mean by Private AI Model?
Private AI models are AI models owned by an enterprise. These models are designed in a manner that they run under infrastructural limits of organizations.
In a Private AI model, you have total control over how your AI would be developed and utilized within the organization.
With the use of Private AI models, there is something that you guarantee and that is no data leaves the governance of your organization either for training purposes or for inference.
Privacy-preserving tools such as end-to-end encryption & federated learning are employed in Private models. This means that no unauthorized individual can access your data during the process of computation. This can occur both externally and internally.
Another important feature of Private AI models is the ability of an enterprise to train its AI models as per the requirements of industries/domains. This leads to the relevance of data and eliminates any risk of vendor lock-in, data breach, and non-compliance.
The implementation of Private AI models may be costly but they provide high returns on investments.
What do you mean by Public AI Model?
AI models created and trained with huge public datasets are termed Public AI. These systems are meant for general use.
In contrast to Private AI, Public AI is run externally by the provider’s infrastructure.
They are easily available and accessible on the internet. For instance, Microsoft Copilot, ChatGPT, Gemini, Claude, and many other popular models fall under the Public AI category.
Since the adoption process for Public AIs is very simple, it’s highly convenient to use these AIs.
Public AI provides you with a myriad of features, including content generation, productivity applications, customer service, and many more.
But besides having lots of functionalities, the downside of Public AIs is the lack of privacy, customization, and control over them.
Why? This is because your data will be processed externally, and even your inputs will be retained by the providers to improve their models.
Regardless of their security and control issues, Public AIs are a great option for companies looking for scale and ease of access than data control.
AI Model Integration – What Are the Security Challenges?
There are security risks associated with AI model integration. If you do not consider and address them, you would ultimately lose your reputation, client, and customers.
Here are some important security challenges that you need to be aware of.
Data Leakage by Accident
This is one risk which is commonly overlooked.
Using any AI service involves a lot of people uploading data which is not intended to be made available outside the organization, even if using an enterprise license.
Some examples of such data include:
- Corporate developed codes and algorithms.
- Private communication between the customer and the company.
- Terms and conditions/contract pricing/M&A confidentiality agreements, etc.
- Private research and development documents & finances.
In spite of the enterprise policy of the vendor that says that they won’t be using company data for training, you still don’t have any control over the usage of your data after you transfer it from your premises.
Aside from what you would have had if it were left in your premises, you do not have any control over the transfer and storing of said data, as well as the abuse that can come about after the vendor receives it.
Note also that chat history retention would be indefinite for all Free and Plus subscribers.
Problem of Shadow AI and Unmanaged Agent Problem
Currently, the use of AI solutions by shadow employees (employees who do not belong to the official employee strength of your enterprise) has become one of the primary sources of data loss and theft.
In case these shadow employees having shadow AI solutions have access to your enterprise’s internal databases then the problem is exacerbated.
Besides this, there is an increased case of data theft when these shadow AI solutions are connected to your enterprise’s CRM & ERP, that are official systems but without any proper integration.
The constant problem of unmonitored activities of such independent AI solutions has contributed to this problem.
Adversarial Attacks and Prompt Injection
Public AI infrastructure usually has more infrastructures as compared to most private AI infrastructures.
As a result, public AI infrastructure becomes vulnerable to attacks when compared to private AI infrastructures.
Detection of a prompt injection attack is quite difficult in the case of a public AI due to its complexity, and it is challenging to understand how they fit into the public AI infrastructure.
Such attack vectors became popular by the year 2026 among businesses/small companies.
Such businesses/ small companies have an obligation to develop a way through which prompt injection attacks can be prevented in their private AIs.
Intent-monitoring and AI gateways are used by them to do so while public AIs do not have this benefit.
Despite the fact that prompt injection attacks are dangerous for private AIs, there are still advantages to having such capabilities.
It is because guardrails and intent monitoring can be implemented in private enterprise AI networks but not in public AIs.
Compliance Challenges in Integrating AI Models
The integration of AI models poses many challenges of compliance for businesses, particularly those from regulated industries such as healthcare, finance, and so forth. Nevertheless, we have presented all the compliance challenges here, so let’s look at them.
Compliance to Infrastructures and Third Party Vendors
Where publicly available AI tools are being utilized, the enterprises need to ensure that the vendors are compliant in respect of certifications, compliance, and contracts.
The cloud service providers need to be compliant with relevant guidelines, for example, ISO 27001 and SOC 2.
Through evaluation of compliance of the vendor, enterprises will be able to manage any risks associated with potential threats arising from a data breach through unauthorized processing and infrastructure outage.
Development of AI Governance & Access Control Policy
The enterprises need to establish an AI governance framework, which will describe the ways of using AI, employees’ rights to use AI and monitoring AI while it is in use.
The enterprises also need to implement access control measures in order to prevent any unauthorized or unintentional use of sensitive information.
The AI Governance Framework will also ensure that AI complies with all legal and regulatory requirements.
Observance of Data Protection and Privacy Laws
Every AI application utilised by a company must abide by any applicable jurisdictional laws controlling data protection as well as any relevant data privacy laws like GDPR or HIPPA.
To comply with rules pertaining to personal data, an organization must also be able to access, safeguard, gather, process, and keep sensitive or personal data.
When a business uses private AI models, it will have more control over all of its data and be less likely to experience a data breach from unauthorized usage or access.
Rules Controlling the Use of AI Technology and Data Storage
To fully comprehend how to handle an organization’s information within its systems, it is essential to develop and execute retention and usage policies for data as well as artificial intelligence technologies.
We already know that in case of retention of user interactions by the organization using commercially available AI systems, there may be a problem with compliance.
Such systems usually capture and retain conversations/prompting of users in order to audit the performance of their models.
Therefore, it becomes extremely critical for the organization to have a policy for retention and disposal of its confidential data.
Retention policy will determine the conditions under which an organization can reuse the confidential business data of the organization.
An organization seeks authorization in writing from the organization for the reuse of the confidential business data of the organization.
Auditing and Transparency Requirements
An AI system should have a decision-making process and an audit trail to log all the information.
The corporation must be aware of the way the data was processed, who processed it, and how this was achieved.
The private AI will offer more possibilities for auditing.
It will simplify regulatory compliance.
What Decision Should an Enterprise Take?
Now, comes the question of which will suit your business better.
As you may have read above, private AI models are ideally suited for highly regulated industries where the data itself is everything.
You must go for private AI models if:
- You follow compliances such as HIPAA, GDPR, and other financial information that shouldn’t be out of the control of your company.
- You wish to train or refine your AI models with unique customer data, proprietary code bases, and patented procedures without any chance of data leaks.
- You wish to achieve efficiency in your organization on a long term basis.
Public AI models are perfect for marketing agencies, departments seeking quick experiments, startups, and general productivity.
You must go for public AI models if:
- You want to get your solution to market quickly.
- You prefer an ‘operational expenditure’ (OPEX) model than capital expenditure (CAPEX).
- You wish to draft emails, code in generic fashion, brainstorm, and summarize public research with the help of AI.
Final Thoughts
Now, since we are at the end of the blog, you know all about Private and Public AI models and which is the right one to choose for you. But in either case, you would have to hire experienced AI experts to implement both.
