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Looking to integrate enterprise AI in your organization, but confused whether to go with private or public models? Or do you want to understand the security risks that come with deploying AI models? Or what are the compliance challenges that an enterprise faces while deploying AI models? All of your questions are answered in this one detailed guide. Let’s explore.
What is Private AI Model?
Private AI models are Artificial Intelligence systems that are privately owned by an enterprise. They are designed in such a way that they only run within the infrastructural limits of organization.
With Private AI models you have full control over the way your organizational AI will be developed, deployed, and used.
By integrating Private AI, you guarantee one thing and that is that your data never goes outside your organization’s governance scope neither in training nor in inference.
These Private models leverage privacy-preserving techniques like end-to-end encryption and federated learning. This ensures that no one other than authorized users can access the data during processing, neither externally nor internally.
You can also train Private AI models to work in a way that they match the objectives of the industry or domain. It increases data relevance and minimizes risks related with vendor lock-in, non-compliance, and data breaches.
Private AI has high implementation costs, however, they do offer a high return on investment in the long-run.
What is Public AI Model?
Artificial Intelligence systems developed and trained using large-scale public datasets are known as Public AI models. They are made for widespread use.
Unlike Private AI, Public AI operates externally on the provider’s infrastructure.
You can easily find them online and access them. Microsoft Copilot, ChatGPT, Gemini, Claude, etc are common examples of AI models that belong to the Public AI category.
As Public AIs require minimal setup, it becomes very easy and quick to adopt them.
With Public AIs, you get a wide range of functionality such as content generation, customer service, productivity tools, and so much more.
However, along with those functionality, you also face limited privacy, customization, and control with these models.
The reason? Because, all your data is processed externally and the providers also retain your inputs to train their model.
Even with limited control and less security, Public AI is a good choice for businesses that prioritize scalability, speed, and easy accessibility over strict data control.
What are the Security Risks of Integrating AI Models?
Integrating AI models comes with security risks, if you don’t understand and solve them, you will eventually end up losing your reputation, client, and users. We have mentioned some key security risks that you must know.
Unintentional Data Leakage
The most often disregarded vulnerability is this one.
Utilizing public AI products includes many individuals entering inappropriate or sensitive data that should not be accessed from outside the company, even with an enterprise license.
For instance, here are a few types of data that should remain with the company:
- Company-created code and algorithm developments.
- Confidential Customer Communications.
- Contract language/terms & conditions/pricing/M&A confidentiality agreements, etc.
- Confidential R&D documents and financial statements.
Even though a vendor’s enterprise policy states that they will not use company-provided data for training purposes, you still have no control over your data once it leaves your facility.
Other than what you would have if it remained on your site, and you also have no control over the transit or storage of that data and/or how the vendor will abuse that data after they receive it.
You should also be aware that any Free and Plus subscribers will have an indefinite retention period for chat histories.
Shadow AI and the Unmanaged Agent Crisis
Nowadays, the AI tools that shadow employees (non-official enterprise personnel) use have become one of the major sources of data loss and theft.
If these shadow employees with shadow AI agents have direct access to the internal databases of your enterprise then the risk increases.
Apart from that, the data theft increases when these shadow AI tools are integrated with CRM and ERP, which are official systems that operate outside of authorized integration.
The continuous lack in centralized monitoring of such independent AI agents has led to an increase in various data theft and loss cases.
Adversarial Attacks and Prompt Injection
Public AI systems generally have more infrastructures compared to most private AI systems and are hence prone to attacks more than private AIs.
A prompt injection attack is hard to detect in a public AI since it is complex and challenging to identify where they fit into the infrastructure of the public AI.
By 2026, such attack vectors were employed by businesses/small companies.
Businesses/ small companies are obliged to create methods through which they can prevent the use of prompt injection attacks in their private AI.
They use intent-monitoring and AI gateways to achieve this whereas public AIs lack such an advantage.
Even though prompt injection attacks pose a threat to private AIs, there are benefits to having such capabilities.
This is because guardrails and intent-monitoring can be utilized in private enterprise AI networks but not in public AIs.
Compliance Challenges of Integrating AI Models
Integrating AI models comes with a lot of compliance challenges for business, especially the ones from regulated industries like healthcare, finance, and more. However, here we have provided all the compliance challenges, so let’s check them out.
Compliance of Third Party Vendors and Infrastructures
When using publicly available AI tools, enterprise organizations must ensure that their vendors are compliant in terms of certifications, compliance and contracts.
Cloud service providers must comply with applicable guidelines, such as ISO 27001 and SOC 2.
By assessing a vendor’s compliance, the enterprise can mitigate potential threats resulting from a data breach due to unauthorized processing or outages within the vendor’s infrastructure.
AI Governance and Access Control Policy Development
Enterprises should create an AI governance framework that outlines how AI can be used by employees, what rights employees have to use AI, and how AI will be monitored while being used.
Enterprises should also implement access controls to help prevent unauthorized usage or accidental exposure of sensitive data.
An AI Governance Framework will also assure that AI will meet all legal, ethical, and regulatory compliance obligations.
Compliance with Data Privacy Laws and Protection
All AI applications used within an organization must comply with applicable data privacy laws such as GDPR or HIPPA, and any other applicable jurisdictional laws governing data protection.
In addition, an organization must have the ability to access, secure, collect, process and store personal or sensitive data to be compliant with laws regarding personal data.
An organization using private AI models will have greater control of all organization data, and will have reduced risk of a data breach due to unauthorized access or use.
Guidelines That Govern Data Storage And AI Technology Utilization
Developing and implementing an organization’s retention and use policies for data and AI technologies is critical to the full understanding of how to properly manage the organization’s data within its systems.
As we are already aware, if an organization is retaining user interactions with commercial/publicly available AI technologies they risk creating a compliance issue.
These types of technologies also frequently collect and retain user prompts/conversations for the purpose of auditing their technology’s model performance.
Thus, it is vitally important for organizations to establish a policy for retaining and securely disposing of their confidential business data.
This retention policy will outline the circumstances under which an organization may request that its confidential business data be reused and required.
An organization obtains the organization’s written authorization before reusing any of the organization’s confidential business data.
Requirements for Auditing and Transparency
There is a need for an AI system to have a clear mechanism for decision-making and keep comprehensive audit logs.
The organization needs to understand how the data is being processed, by whom, and how the results of such processing were obtained.
The private AI environment is expected to provide more auditing opportunities.
It makes compliance with regulations easier.
Which Will Suit Your Enterprise the Best?
Now, the question arises which will suit your enterprise the best.
As already mentioned earlier, Private AI models are best suited for highly regulated industries where data is everything.
You should choose Private AI models if:
- You handle strict compliances like HIPAA, GDPR, and financial records that should not be shared outside your organization’s controlled environment.
- You want to train or fine-tune your AI models with unique customer insights, proprietary codebases, and patented processes, without any risk of data leakage.
- You want long term efficiency in your organization.
Public AI models are most suitable for marketing agencies, departments wanting rapid experimentation, startups, and general productivity.
You should choose Public AI Models if:
- You want to quickly launch your solution.
- You prefer a “pay-as-you-go” (OPEX) model over high upfront infrastructure costs (CAPEX).
- You draft emails, write generic codes, brainstorm, and summarize public research using AI.
Final Thoughts
With the blog coming to an end, now you know about both Private and Public AI and which one will choose you the best. However, in both the cases you’ll need to hire AI experts with experience of deploying both models in enterprises.
