Enterprise AI: Build or Buy?
Download Guide
Here is the thing about AI security that nobody tells you upfront. Most of the playbooks you have been using for traditional software do not quite apply here. The attack surfaces are different. The data flows are different. And the ways things can go wrong are, frankly, more creative than anything your security team has dealt with before.
That is not meant to scare you off. AI is worth deploying. But secure AI deployment requires thinking through problems that did not exist five years ago. If you're a technical leader trying to get AI into production without giving your CISO a heart attack, this is the guide you need.
With traditional software, you're mostly worried about unauthorized access, data breaches, and code vulnerabilities. You know the drill. Patch your systems, encrypt your data, lock down your endpoints. Those concerns do not disappear with AI, but they get joined by a whole new category of risks.
First, there’s the data exposure problem. AI systems are hungry for data. They need to ingest, process, and learn from your most sensitive information to be useful. A customer service AI needs access to customer records. A contract analysis system needs access to your legal documents. A knowledge search tool needs access to your internal communications. The value of AI comes from connecting it to real enterprise data, which means your most sensitive assets are suddenly flowing through a new system.
Second, there’s the model behavior problem. Unlike traditional software that does exactly what the code says, AI models have emergent behaviors. They can be manipulated through prompt injection. They can hallucinate sensitive information they were trained on. They can be tricked into bypassing their own guardrails. These are not bugs you can patch; they are inherent characteristics of how the technology works.
Third, there’s the vendor trust problem. When you send data to a cloud AI provider, you're trusting their security, their access controls, their employee vetting, and their data handling practices. you're also trusting that your data will not be used to train models that benefit your competitors. That is a lot of trust.
Let me walk you through the security issues that tend to surface after deployment, when they are much harder to fix. These are not hypothetical concerns. They are the problems we actually see organizations scrambling to address once the AI is already in production. Real users touching real data.
Data leakage through prompts and responses. When users interact with AI systems, their prompts often contain sensitive information. Those prompts get logged. They get stored. In cloud deployments, they leave your environment entirely. Multiply that by thousands of queries per day and you have a significant data leakage surface.
Inadequate access controls. Traditional access controls work at the system or document level. AI systems need more granular controls. Just because someone can access the AI does not mean they should be able to ask it about executive compensation or pending litigation. The AI knows everything it was trained on, which means access control needs to happen at the query level, not just the system level.
Missing audit trails. When something goes wrong with traditional software, you can trace exactly what happened. With AI systems, traceability is trickier. What training data influenced this response? Why did the model make this recommendation? If you cannot answer these questions, you cannot investigate incidents, satisfy auditors, or demonstrate compliance.
Shadow AI proliferation. Here is one that catches a lot of security teams off guard. While you're carefully planning your official AI deployment, your employees are already using AI every day. They are pasting customer data into ChatGPT to draft responses. They are uploading contracts to Claude to summarize terms. They are feeding proprietary code into Copilot alternatives to debug issues. None of this is going through your security controls because none of this is happening on systems you manage.
By the time you deploy your secure, sanctioned AI system, you may already have months of sensitive data sitting in third party systems you never authorized. Your secure AI deployment strategy needs to account for the AI that is already happening, not just the AI you're planning.
Now that we have covered what can go wrong, let us talk about what to do about it. Secure AI deployment is not about adding security after the fact. It is about building it into the architecture from day one. The organizations that get AI security right treat it as a design constraint, not a compliance checkbox.
They ask "how do we build this securely" before they ask "how do we build this fast."
Counterintuitively, this approach usually ends up being faster in the long run because they are not constantly backtracking to fix security gaps that block production deployment. Here are the things you need to prioritize in order to ensure your AI deployment is secure.
Data residency controls. Your data is owned by business users who know the most about their data. We pursue a distributed data architecture in which our solutions treat data as a product.
Access management. Implement controls that understand what the user is asking, not just who the user is. Access and governance management is on the user, team, department and organization level.
Comprehensive logging and auditability. Every query, every response, every data access should be logged in a way that supports investigation and compliance. This is not just about storing logs; it is about structuring them so you can actually trace what happened when you need to.
Model governance. Track what models are deployed, what data they were trained on, who approved them, and what guardrails are in place. When a model changes, you need versioning. When something goes wrong, you need rollback capability. Treat models like you treat production code, because that is exactly what they are.
Future stability. Models come and go, but the solution's performance must remain the same. It's important to think of systems in a model agnostic way.
Your data is your most valuable asset. AI makes it more valuable by unlocking insights and automation. But that value evaporates instantly if the data is compromised. Secure AI deployment is not just a technical requirement. It is the foundation that makes everything else possible.
If you're working with an AI vendor rather than building everything yourself (which is the right choice for most organizations), here is what to look for.
Ask where the data goes. Not in marketing language. Specifically. Does data leave your environment? Where is it processed? Where is it stored? For how long? Who has access? A vendor who cannot give you clear answers to these questions is a vendor you should not trust with sensitive data.
Verify the compliance certifications. Look for industry specific certifications relevant to your sector. But do not stop at certifications; they tell you that the vendor had good security practices at the time of the audit. Ask about ongoing security practices and how they handle incidents.
Understand the access control model. Can you implement role based access at the query level? Can you restrict what data different users can access through the AI? Can you integrate with your existing identity provider? If the answer to any of these is no, you will be compromising on security from day one.
Review the audit capabilities. Can you see every query and response? Can you trace how data flowed through the system? Can you export logs for your own analysis and retention? Auditability is not optional for regulated industries, and it should not be optional for anyone serious about security.
Secure AI deployment is not about slowing down. It is about making decisions upfront that avoid costly rework later. The organizations that get this right are the ones deploying AI fastest, because they are not constantly backtracking to fix security gaps they should have anticipated.
If you're evaluating AI deployment options and security is a priority (which it should be), start with a conversation about your specific requirements. What data will the AI access? What regulations apply? What are your internal policies on data residency? The answers to these questions should drive your architecture decisions.
We built Unframe for organizations that cannot compromise on security. Our platform deploys in your environment, your data never leaves, and governance is embedded from day one. If that sounds like what you need, let's talk.
.png)
.avif)
