What is AI data security and how do you protect training data?

AI data security is the set of practices used to protect the data that trains and operates AI and machine learning systems from unauthorized access, tampering, and corruption across the system's entire lifecycle. CISA, the National Security Agency, the FBI, and international partners defined these practices in joint guidance, AI Data Security: Best Practices for Securing Data Used to Train & Operate AI Systems, published May 22, 2025.

What is AI data security?

The guidance treats data security as the ability to protect data resources across their lifecycle so AI outcomes remain accurate and trustworthy.

It frames the datasets behind an AI system as a critical link in the AI supply chain that must itself be protected and secured.

The guidance is aimed at organizations that rely on AI day to day, including defense contractors, federal agencies, and critical infrastructure operators, so they can protect sensitive and mission critical data.

CISA published it together with the NSA, the FBI, and cyber agencies from Australia, the United Kingdom, and New Zealand.

What are the three primary risks to training data?

The guidance organizes risk into three categories: compromise of the data supply chain, deliberately poisoned data, and data drift over time.

On supply chain risk, the guidance warns that organizations cannot assume large web-scraped datasets are clean or free of malicious content.

One named technique is split-view poisoning, where different subsets of a dataset are manipulated to cause specific failures without affecting overall model performance during testing.

Another is frontrunning poisoning, where malicious examples are injected just before crowd-sourced content is collected from a website.

Data drift is described as a natural effect in which the statistical properties of input data change over time, reducing model accuracy and reliability, distinct from deliberate poisoning.

How does data security map onto the AI lifecycle?

The guidance ties its recommendations to the six stages of the NIST AI Risk Management Framework, from initial planning through ongoing operation.

During the Build & Use Model stage, securing data is meant to ensure models are trained on reliably sourced, accurate, and representative information.

At deployment, strict access controls are described as protecting data from unauthorized access, especially in the Deploy & Use phase.

Once a system is live, continuous data risk assessments in the Operate & Monitor phase are presented as necessary to adapt to evolving threats.

What best practices do the agencies recommend?

The guidance lists concrete controls, starting with verifying that data sources are trusted, reliable, and accurate for training and operating AI systems.

It recommends using checksums and cryptographic hashes to verify that data has not been altered or tampered with during storage or transmission.

Digital signatures are presented as a way to help ensure data integrity and prevent tampering by third parties across training and fine-tuning steps.

The guidance also calls for using a trusted computing environment that leverages Zero Trust architecture to isolate sensitive data operations.

Finally, it recommends categorizing data using a classification system based on sensitivity and required protection measures before applying controls.

How does this relate to earlier AI security guidance?

The CSI builds on the agencies' earlier Guidelines for Deploying AI Systems Securely, released in April 2024, and goes deeper specifically into the data layer.

What should security teams prioritize first?

Organizations are told to treat identifying risks and adopting best practices as the way to fortify AI systems and safeguard the sensitive, proprietary, and mission critical data that powers them.

Putting this guidance into practice means mapping which third-party and web-scraped datasets feed each model, applying provenance tracking and hash verification at ingestion, and building the classification and access controls recommended for the Deploy & Use and Operate & Monitor stages before expanding an AI system's footprint.