An increasing number of people are using AI for personal, semi-professional, and business needs using generic tools like ChatGPT, Copilot, or Gemini. But what about using AI with your organization’s data?
For any business, the next step is to start building a company-specific AI tool connected to trusted enterprise data. Axway can help in several ways to provision and use this company-specific AI platform, in a well-managed and secure manner. Let’s explore what it takes to achieve this.
See also: Secure and Scalable Agentic AI: A Guide for Enterprise Leaders
Setting up an enterprise AI tool
Incorporating your enterprise’s data into AI tooling can add valuable context to responses. As my colleague Jeroen Delbarre writes, it’s the difference between:
“Your spending limit depends on your bank’s policies.” (Vague and unhelpful)
Vs.
“Based on your latest transactions, you have spent $1,200 this month. Your remaining spending limit is $800 as per your bank’s updated policy on March 1, 2025.” (Precise and contextual)
Many large organizations are now deploying a private instance of AI tooling to gain control over their data and how it’s used. Such a setup has two fundamental advantages:
- The company data that is pushed into the AI private instance stays within that instance only, which means it is not shared in any way with the “public” AI tool or the AI tool vendor.
- Using a private instance, it is possible to add management and security into usage and replies of the AI tool.
While it may be helpful for an employee to ask your company’s AI assistant “how much PTO do I have left?”, you might not want them to be able to get the answer to “which employees in my department are on a PIP?”
This is why IT leaders need to lay several foundational elements first when setting up a company-specific AI tool:
- First order of business is determining how to include proprietary company data so the AI does not only know “generic” information, but also relevant company-specific details in the appropriate context.
- Secondly, as soon as the AI tool contains company-specific data, you need to be able to manage & secure access to the data.
- AI is very good at sharing information, but LLMs cannot execute actions. So, you need to integrate action (or “agentic”) capabilities into the chatbot, allowing AI insights to trigger real outcomes across systems. Or, in other words, you’re adding an “action button” to the chatbot/AI tool.
- Finally, as you are creating these (integration) assets, governance will be key.
How to safely incorporate company data into AI tools
As soon as the private AI instance is up and running, the first step is to add company-specific data to that instance, so the AI is aware of as many company-specific details as possible and all responses contain those specifics.
AI is extremely data-hungry: in order to generate relevant responses, it must be fed with large volumes of accurate, up-to-date information.
Many companies opt for a hybrid approach, where they feed data into the system using technologies like MCP to allow the LLM to interact with external tools, APIs, and data sources.
See also: How Model Context Protocol (MCP) Enables LLMs to Take Action
In this article, let’s focus on the incorporation of data into an AI instance. There are two main ways to do so, each offering different technical behaviors but fulfilling similar integration needs:
- Large Language Model Finetuning: Company data is given to the AI platform, so it learns from the company-specific data as it did from all the other data it was offered since its creation.
- RAG (Retrieval-Augmented Generation): Company data is also offered to the AI platform, but it is used to add details to the prompt (question raised by the user through a Chatbot) before the prompt is offered to the AI platform.
Dive deeper: How to use Amplify Fusion for retrieval-augmented generation (RAG)
Continuous data delivery with enterprise-grade MFT
From a data integration standpoint, both approaches require continuous, secure delivery of large data volumes – not just once, but on an ongoing basis (involving a constant flow of data).
With the growing importance of agentic AI (specialized AI engines to support specific use cases), and given global organizations concerns around sovereignty and locations where data is stored, it is highly likely it is not one AI engine that needs to be given specific data, but rather a large number of AI engines that each need to access a specific subset of the company data.
AI Data Governance: Liquid Intelligence – From Pools to Pipelines
To be able to transfer all the data from its origin to all AI engines, an intelligent, enterprise-grade Managed File Transfer (MFT) platform is essential for maintaining reliable, long-term data delivery.
A key challenge is ensuring the transfers are done in a secure and managed way, so alerting is in place if file transfers fail for whatever reason.
Transferring a large set of files one time is quite easy, but implementing a constant stream of file transfers that continue to run for many years is a different story and requires the right tool, right setup/processes and the right monitoring. If the company AI tool is not given up-to-date company data, the business value of the platform will go down slowly but steadily.
Axway offers a leading intelligent MFT platform, built on 40 years’ experience, that has proven to support the most demanding, highly-regulated organizations around the globe.
See also: How agentic AI will change MFT and everything else around it
Manage & secure AI access to enterprise data
As soon as the company AI engine has been populated with all kinds of company-specific data, the next challenge is to manage & secure access to the AI engine for all possible consumers.
AI platforms are not designed with enterprise-grade governance. They will always provide an answer but won’t control what’s being exposed – unless you add a governance layer. So, if you want to manage or secure the information shared by the AI platform, you need to add a separate component.
The good news is that all communication between the AI application and the chatbot used by any consumer is based on APIs. So, it is perfectly possible to take an AI gateway and intercept all traffic passing by to look at the traffic and see if it meets all rules (policies) that you want to be checked.
Some examples of possible rules are:
- Limit exposure: A limit is applied to the amount of information that is being shared (for example you can only get 10 customers instead of the full customer database with thousands of customers)
- Data masking: Specific information is replaced, for example the first 12-digits of credit card numbers are replaced by stars if credit card numbers are being shared
- Role-based access: Specific information can only be shared with people with specific roles in the organization, for others it is not visible
- Usage control: To keep the cost of using AI under control, the number of questions that can be raised and/or the size of the question is limited
Based on over 25 years of experience in API management, Axway created a dedicated AI gateway that comes with a large set of relevant policies for managing & securing company AI platforms. It is a proven, scalable enterprise AI gateway that meets all requirements large organizations have when looking at managing and securing their AI platforms.
Learn more about Amplify AI Gateway
Agentic AI: adding the “action button” to AI capabilities
While AI is excellent at generating content, most organizations need more than just information – they need outcomes. They want to sell, they want to have a support case logged, they want you to plan a meeting, etc.
To bridge this gap, AI systems must be able to call on enterprise systems via secure, standardized interfaces. In the past, every single AI platform provider had its own, proprietary way of connecting its AI platform to applications.
At the end of 2024, Anthropic published an open standard called MCP (Model Context Protocol) to standardize how AI systems interact with external tools and data sources.
“[MCP] acts as a universal translator, enabling seamless communication between AI models and various systems. This allows AI applications, like programmatic agents and coding assistants, to connect to external services using a common interface.”
For any AI platform used by an organization, MCP is a key addition to being able to really add business value. That is why Axway added an MCP to its Amplify portfolio, so AI tools can be integrated in any API orchestration like any other application that offers APIs.
Dive deeper: Step-by-Step Guide to Setting up an MCP Server with Amplify Fusion
Ensuring data governance in AI use cases
MCP promises to offer to wrap existing APIs and other digital assets and expose their functionalities in a standardized way, bringing some order to the chaos of AI adoption and normalizing access for LLMs. But it doesn’t solve the chaos itself.
Several of Axway’s large enterprise customers that are at the forefront of AI innovation have shared with us that adding MCP to AI platforms is probably the easiest part of the MCP story.
What they saw happening is that all kinds of departments and groups of developers launched their own AI platforms including an MCP server, resulting in a significant amount of unmanaged MCP servers running in the organization. On the horizon, they see agentic AI growing extremely fast.
Axway’s Bas Van Den Berg reiterates this sentiment in a recent article: if anything, MCP can add to the sprawl, with more digital assets from different parties layered on top of your API landscape.
Agentic AI introduces small, task-specific AI agents that collaborate through MCP, which can result in dozens – even hundreds – of MCP servers across a single organization.
To bring control and efficiency, organizations need a governance layer, similar to what they’ve already built for APIs: promoting reuse of existing MCP servers over creating new ones (again, the same reuse story that organizations are seeing with APIs).
Axway’s Amplify Engage, the multigateway API marketplace that Axway has now offered for several years, is extended to include MCP servers as yet another integration asset. This means that Engage will contain a full overview of not only integration assets (APIs), but also MCP servers.
And it means that all internal & external developers can see, test, and subscribe to any asset, whether it is an integration service or an MCP server, which will:
- Increase re-use significantly (re-use vs. building again)
- Reuse will result in a lower TCO of your IT platforms
- Reuse will increase your asset’s business value
Learn more about API marketplaces with Amplify Engage.
Ready to transform your enterprise with AI?
The journey from generic AI tools to enterprise-grade AI solutions requires more than just deploying a chatbot—it demands a comprehensive approach to data integration, security, and governance. As organizations increasingly recognize that their competitive advantage lies in how effectively they can combine AI capabilities with their unique enterprise data, the need for robust, scalable infrastructure becomes critical.
Axway’s integrated platform addresses each pillar of this transformation: secure data delivery through proven MFT capabilities, intelligent access control via AI Gateway, seamless system integration through MCP support via Amplify Fusion, and comprehensive governance with Amplify Engage.
Don’t let your organization fall behind in the AI revolution due to data silos, security concerns, or governance gaps.
Discover how Axway can accelerate your enterprise AI journey.
Frequently Asked Questions
What infrastructure considerations are most critical when scaling enterprise AI beyond a pilot program?
The most critical consideration is establishing mature, governed data pipelines that can handle the complexity of enterprise data sovereignty requirements. You need infrastructure that can centralize, replicate, or federate data flows based on compliance constraints while maintaining security and performance.
Without robust data governance and intelligent file transfer capabilities that respect legal boundaries and privacy regulations, organizations risk creating ungovernable AI sprawl that compromises both security and compliance.
See also: AI Data Governance: Liquid Intelligence – From Pools to Pipelines
What’s the difference between LLM Finetuning vs RAG (Retrieval-Augmented Generation)?
Whereas finetuning adapts a language model to enterprise data by retraining it on proprietary content, RAG enhances responses by dynamically retrieving relevant documents from a knowledge base, without altering the model itself.
See also: Fine-Tuning vs RAG: Key Differences Explained (2025 Guide)
What’s the difference between RAG and MCP?
Retrieval Augmented Generation and Model Context Protocol can both be used to access data from an outside source, however, MCP can also be used by an LLM to perform actions.
RAG is most suited for AI search, where the latest and most relevant information needs to be retrieved, whereas MCP is most suited for agentic AI use cases, where actions need to be initiated.
How long does it typically take to set up a private AI instance with enterprise data integration?
Implementation timelines vary significantly based on your organization’s API maturity and data complexity. Organizations with mature API infrastructure and clear governance frameworks can accelerate this timeline, while those that lack foundational improvements to their integration landscape will need additional time to establish the robust backbone that enterprise AI demands.
See also: Secure and Scalable Agentic AI: A Guide for Enterprise Leaders
How does a private AI instance ensure our sensitive data never leaves our control?
A private AI instance operates entirely within your controlled environment, meaning company data pushed into the AI instance stays within that instance only and is never shared with public AI tools or vendors.
This approach, combined with AI gateway policies for data masking, role-based access controls, and exposure limitations, ensures your sensitive information remains under complete organizational control while still enabling powerful AI capabilities.
What compliance standards does Axway’s AI gateway support?
Axway’s AI solutions are built on 30+ years of experience serving highly regulated organizations globally, with proven capabilities for maintaining compliance across major standards including SOX, GDPR, and HIPAA. The Amplify AI Gateway includes embedded governance templates, comprehensive logging and auditing capabilities, and role-based access controls specifically designed to meet the stringent requirements of regulated industries like financial services, healthcare, and insurance.
What are examples of specific business outcomes enterprises have achieved?
Enterprise AI implementations deliver tangible results across industries, for example:
- Insurance companies are significantly decreasing claim eligibility processing times while improving fraud detection accuracy
- Financial services firms are enhancing underwriting decision support and risk scoring for real-time pricing
- Healthcare organizations are streamlining patient data analysis and treatment recommendations.
Such outcomes demonstrate how connecting AI to enterprise data can transform generic responses into precise, actionable business intelligence.
How do we handle data governance when we have hundreds of different AI agents across departments?
Managing governance at scale requires a centralized approach like Axway’s Amplify Engage, which provides a unified registry for all AI assets including MCP servers, enabling organizations to promote reuse over redundant creation and maintain visibility across the entire AI landscape.
This platform approach prevents the sprawl of unmanaged AI agents by offering a single governance layer to enforce consistent policies, track usage, and manage the lifecycle of AI assets across all departments.
What are some common pitfalls to avoid when it comes to using AI with organizational data?
The biggest pitfall is implementing AI solutions without first having established mature API infrastructure and clear governance frameworks, as this leads to integration failures and security vulnerabilities that can compromise sensitive data.
Organizations also commonly underestimate the non-deterministic nature of AI systems, applying traditional testing and quality assurance methods that are ineffective for probabilistic AI outputs, resulting in unpredictable behaviors and difficult-to-reproduce issues in production environments.
What you need to know: Why Agentic AI protocols aren’t ready for prime time yet