Can Generative AI Be FedRAMP-Approved? Breaking Down the Requirements

How can businesses using GenAI tools meet federal security standards? As more organizations adopt Generative AI for tasks like automation and eDiscovery, concerns around compliance and risk grow, especially when working with federal agencies.

Meeting FedRAMP requirements is essential for any cloud-based service handling government data. Let's look into what it means for Generative AI to be FedRAMP-approved, and what businesses need to understand to stay compliant.

What Is FedRAMP and Why It Matters

FedRAMP is the federal government's security program for approving cloud-based services. If a company wants to offer cloud tools to federal agencies, it has to meet FedRAMP standards.

There are three primary reasons FedRAMP matters for any service provider working with Generative AI:

• It standardizes how cloud services are reviewed for security
• It's required for any cloud provider working with federal data
• It creates a shared trust between agencies and vendors

Cloud Services

FedRAMP gives all cloud systems the same rules to follow. Instead of each agency having its own process, FedRAMP makes things consistent. It makes it easier to assess risk, review vendor security controls, and compare systems across agencies.

Federal Data

If a service handles data for a federal agency, it has to meet FedRAMP requirements. That includes Generative AI systems, even if the AI is just one part of a larger platform. Without FedRAMP, those services can't be used by government clients.

Agencies and Vendors

FedRAMP allows agencies to trust services that another agency has already reviewed. Once a company gets approval, it can work with multiple agencies without repeating the full review. It saves time, reduces costs, and supports long-term compliance across different use cases.

Generative AI Standards and Its Unique Risks

Generative AI is built to create new content. It learns from large sets of data and produces text, images, code, or other results based on patterns it finds.

But GenAI also comes with risks that make it harder to fit into strict security frameworks like FedRAMP. There are three main issues to consider:

• The way GenAI generates content can't always be predicted
• It can introduce bias or errors from the data it was trained on
• Tracking or explaining how it reaches a result isn't easy

The Way GenAI Generates Content Can't Always Be Predicted

Unlike a traditional tool that follows set steps, GenAI reacts based on patterns in its training. That means it might produce slightly different answers even when given the same input. This unpredictability makes it hard to measure the safety and accuracy of its output.

It Can Introduce Bias or Errors From the Data It Was Trained On

GenAI systems reflect the data they've learned from. If that data includes outdated or biased content, the results might also be biased or wrong. It can create problems in legal, medical, or government use where accuracy matters.

Tracking or Explaining How It Reaches a Result Isn't Easy

With GenAI, there's often no clear path from question to answer. That makes it tough to explain how or why a certain response was given. For agencies that need full traceability, this lack of transparency can be a major concern.

Can GenAI Meet FedRAMP's Security Requirements?

Meeting FedRAMP's standards isn't easy for any cloud service, but tools like the ones used in Generative AI eDiscovery face even more pressure. These platforms raise concerns around transparency, control, and output monitoring that don't always align with traditional security reviews.

There are three key areas where GenAI may have trouble meeting FedRAMP expectations:

• Explaining how the model works and makes decisions
• Proving that outputs are consistent, secure, and well-managed
• Showing that the cloud infrastructure is properly controlled

How the Model Works and Makes Decisions

FedRAMP reviews often ask for detailed documentation about how a system works. For GenAI, this becomes difficult. These models operate based on deep layers of learned patterns, which aren't easy to explain in plain terms. The lack of clear rules or steps makes it harder for reviewers to understand the model's behavior.

Proving Outputs Are Consistent, Secure, and Well-Managed

GenAI doesn't always give the same answer twice. This raises questions about how results are reviewed and whether unsafe or incorrect outputs can be filtered. FedRAMP looks for consistency and reliability, which can be hard to prove when a model behaves differently each time it's used.

Showing the Cloud Infrastructure Is Properly Controlled

Even if the GenAI model itself is secure, the FedRAMP AI approval process also depends on the cloud system that supports it. It includes things like user access, encryption, logging, and how data flows through the system. The vendor must show that all these parts are secure and meet federal guidelines.

Compliance Challenges With GenAI Integration

Even when a platform offers useful tools, that doesn't mean it's ready to meet federal standards. Many GenAI tools rely on external models or outside vendors. These outside services often change without warning and aren't always designed with compliance in mind.

That makes it harder to guarantee that everything meets FedRAMP standards. Vendors must explain how they manage and secure these connections.

GenAI often works with fresh or real-time data. That might include user content, uploaded files, or shared prompts. If that data contains sensitive or personal information, it can raise security flags.

FedRAMP looks for clear controls over how data is handled, which GenAI systems don't always have.

One of the hardest parts of GenAI integration is logging. If a user gets a result from the model, reviewers need to know where the data came from, how it was processed, and how the model reached its response.

GenAI tools aren't always built to track that kind of detail. This creates a gap in reporting, which makes compliance harder to prove.

Legal Counsel for FedRAMP Compliance

FedRAMP approval for GenAI isn't impossible, but it does require serious planning, clear documentation, and strong legal support.

At Reveal, we help legal teams move faster with AI-powered eDiscovery software built for speed and accuracy. Our all-in-one platform handles everything from data processing and review to supervised learning and visual analytics. With support for hundreds of file types and advanced search tools, we make even complex reviews simple, efficient, and scalable.

Get in touch today to find out how we can help with your GenAI legal needs.