AI tools operating outside of the IT department’s line of sight, coupled with their ability to crawl through data quickly, increase risk exponentially. As a result, the gap between security and AI continues to grow.
Sensitive data moves faster and in more unpredictable ways than ever before — traditional defenses weren’t built for this reality. AI, in spite of its productivity benefits, can leave sensitive data exposed to be extracted in seconds.
Embracing AI safely means organizations need to shift their security focus towards where the risk actually lives: the data itself. In this article, we’ll explore why data security is the foundation of AI security and how to secure your AI pipelines before you’re in too deep.
The AI security challenge
Enterprise AI adoption introduced challenges that many organizations are unable to address, including:
- AI agents that access sensitive data without proper governance
- Generative AI solutions that create new sensitive data at a high rate
- Shadow AI tools that are adopted without IT/security sign off
- AI copilots that connect isolated data without adequate guardrails
- Data and model poisoning that threaten model integrity
Traditional security approaches and DLP solutions are unable to keep up with these challenges — they generate too much noise and often lack support for new data stores where AI operates. Modern data security solutions must include identity, cloud, endpoints, and SaaS environments in their coverage.
Why data security is the foundation of AI security
AI systems are inherently data-driven. A large amount of data is needed for AI to train, operate, and learn from. This data creates a bidirectional security concern, where data going into AI systems and data coming out of AI systems need to be monitored and managed effectively.
Organizations need to ensure sensitive information isn’t inadvertently fed into AI models (which could lead to data leakage or compliance violations), and ensure any outputs generated by AI don’t contain sensitive information or create new security vulnerabilities.
On top of this, organizations need to look out for an increased “blast radius” onset by AI. The blast radius refers to the potential scope of damage possible if your AI systems are compromised or misused.
Uncontrolled AI access can turn a small misstep into a catastrophic data breach. By limiting permissions and monitoring configurations, organizations can drastically reduce the scope and impact of any incident.
Securing AI pipelines: a comprehensive approach
Effectively securing your data from AI requires a holistic approach that addresses the entire AI pipeline from data ingestion to output management. Following this strategy ensures their AI is implemented with data security in mind.
Visualize AI’s access to your sensitive data
The first step to managing AI risk is to map the sensitive data repositories that your AI tools and agents can access. For example, understanding that Microsoft 365 Copilot uses the permissions of the user is important if you’re enabling it for your organization. Locking down your permissions and visualizing what AI may be able to access before enabling a tool helps you understand potential data exposure. From there, you’ll be able to assess the risk level associated with each data store, prioritize mitigation strategies, and implement appropriate access controls.
Revoke excessive permissions
AI systems often receive more permissions than necessary, making implementing least privilege access for AI tools and services critical. For example, a sales forecasting AI tool likely doesn’t need access to your HR data to do its job. Agents trained on over-permissioned data can also deliver irrelevant responses, which can heighten the fallout from a potential data breach. Without enforcing least privilege, AI can become a supercharged insider threat capable of exposing terabytes of data in seconds.
Fix risky AI misconfigurations
Regularly auditing AI system configurations helps organizations identify and remediate security gaps, including reviews of prompt constraints, fixing authentication mechanisms, or addressing improper data integration points. Proactively maintaining a regular audit cadence helps organizations adapt to evolving compliance requirements and reinforce a strong AI governance strategy.
Monitor AI-created data
AI systems can generate vast amounts of new data for your organization, some of which is sensitive and requires security oversight. As new data is generated, you should classify whether or not it’s AI-generated, apply sensitivity labels, and monitor for suspicious activity.
When it comes to classifying AI-generated content, the best approach is to automatically identify and categorize content based on sensitivity levels. For example, a customer service AI chatbot’s outputs should be classified according to whether they contain sensitive information like customer details or financial data.
Next, ensure all AI-generated content receives the appropriate sensitivity label to control how it can be accessed, shared, and used. This might include automatically labeling documents generated by AI assistants with “confidential” or “public” designations.
Monitor suspicious AI use
All user interactions with AI should be monitored to identify questionable prompts and potential misuse. If a user is repeatedly attempting to extract customer credit card information from an AI assistant, automatic monitoring can alert your security team and/or lock down that user and prevent a data breach.
Keep sensitive data out of AI systems
One of the most effective strategies for managing AI data risk is to prevent sensitive data from entering AI systems altogether. Achieving this requires a multi-pronged approach.
First, organizations must discover hidden AI workloads, as the rise of shadow AI can unknowingly expand the blast radius and introduce vulnerabilities. Next, it's essential to identify sensitive data flows by mapping how data moves through AI systems and across the organization—this helps pinpoint potential exposure points and enables the implementation of appropriate controls.
Finally, mapping AI service accounts with access to sensitive data is crucial for understanding which accounts can reach specific repositories, allowing for the enforcement of proper access controls.
Establishing a secure AI implementation strategy
Following these practical steps to ensure AI is securely implemented in your organization:
- Identify risk: Conduct an AI security assessment to identify the security gaps in your current AI implementation
- Classify data: Implement comprehensive data discovery and classification to ensure all data is identified and classified
- Establish AI governance policies: Define clear AI governance policies to align users on which data can be used for which purposes and in which tools
- Implement technical controls: Deploy appropriate security measures to enforce access controls, DLP, and monitoring
- Offer internal training: Educate all your users on AI best practices for secure and safe use
- Monitor and improve: AI security is not a one-time project — continuous monitoring and improvement are necessary to keep up with the changing security landscape
Secure your AI pipelines with Varonis
The number of AI tools available to the public is growing, as is the unprecedented risk they bring to organizations. A simple prompt or misconfigured AI agent can expose sensitive data in seconds.
Our latest research uncovered a sobering truth: 99% of organizations have exposed sensitive data that AI can easily surface. After analyzing nearly 10 billion files across real-world environments, our State of Data Security Report revealed widespread vulnerabilities. From shadow AI and missing MFA to ghost users and unlabeled files, the risks are growing daily. The report not only highlights these threats but also offers actionable steps and tools to secure their data in preparation for AI implementation.
Varonis AI Security continuously identifies AI risks, flags active compliance violations, and automatically remediates exposures so your organization can embrace AI safely.
AI is advancing rapidly, but in the grand scheme of things, we’ve only just begun when it comes to enterprise adoption. Organizations that wait to modernize their defenses or stick to traditional security are left with glaring security risks that will only continue to grow.
By embracing a data-first AI security strategy early, they’ll be better positioned to innovate safely and sustainably.
AI security starts with data security: FAQs
Why is data security critical for AI security?
AI systems are data‑driven. They rely on large volumes of sensitive data for training, operations, and outputs. Without robust data security, AI can unintentionally expose or leak that data through prompts, outputs, or misconfigured access.
What is the biggest risk with AI copilots and agents?
The primary risk is overexposure of sensitive data. Copilots can connect previously isolated data sources, granting users or AI agents access to information they should never see, often without anyone realizing until a breach occurs.
What is “shadow AI,” and why is it dangerous?
Shadow AI refers to AI tools or services adopted outside IT or security oversight. These unmonitored systems can interact with sensitive data, create compliance violations, and expand the organization’s attack surface.
How can I keep sensitive data out of LLMs?
First, discover and classify all sensitive data across your environment. Then, control which AI systems and users can access it. Solutions like Varonis AI Security automatically detect sensitive data flows and prevent it from entering AI models or prompts.
