It is no surprise that Enterprise AI adoption is accelerating. What is becoming noticeably clear is that security and governance programs are not keeping up. Organizations are rolling out copilots, AI assistants and even custom agents, but they typically do not know where AI is being used and what data AI systems can access. Additionally, they are recognizing they need to be able to stop risky behavior before it turns into exposure.

These are key gaps that Varonis Atlas addresses. Atlas is an end-to-end AI Security Platform that helps organizations find AI risks, fix them proactively and alert on dangerous behavior at runtime. Atlas is built around a simple idea: in enterprise environments, AI risk is inseparable from data risk. The biggest questions are not just which models exist, but what data they can reach, which identities they inherit and what they can do at runtime.

The Atlas model is organized around the full AI security lifecycle, as shown in Figure 1 below: visibility and posture, protection and governance. That structure mirrors the use cases in this post, moving from AI inventory and AI-SPM to guardrails, monitoring, detection and response, compliance and third-party AI risk.

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Figure 1 – The Atlas Approach

A useful way to understand Atlas is through practical security use cases that map to how organizations deploy and govern AI. While there are many use cases, I have picked several of the most common to cover in this blog. In a subsequent post, I will cover additional use cases and dive deeper into key aspects of Atlas, but for now, let us start at the beginning.

Discovery

For most organizations, the first security problem with AI is visibility. Security teams might know which tools are approved, but they may lack a complete view of all the AI systems in use, the agents under development or the unsanctioned AI activity happening across the business.

Atlas starts with AI inventory by continuously discovering AI assets, projects and systems (including shadow AI) and provides a single, authoritative inventory of AI usage (see Figure 2 below as an example). This includes visibility into the models, agents and tools in use, as well as the data each AI asset can access. These dashboards provide additional context simply by clicking within each component. For example, having the ability to understand unapproved AI resources or even misconfigurations and remediating these items is critical for organizations as they discover their AI assets.

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Figure 2 – AI Inventory Dashboard

Figure 3 below provides further context around misconfigurations that are unresolved within this resource. From there, you can continue to delve deeper into understanding and correcting the issue, all within the platform, as shown.

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Figure 3 – Misconfigurations and Remediation

Remember, continuously discovering AI assets and having an authoritative inventory of AI usage is crucial because an organization cannot govern AI they do not know about or have not found. Inventory is the first step in separating approved use from unmanaged use and in identifying where deeper controls are needed.

Significance:

• Find the shadow AI before it creates unmanaged data risk
• Establish a single inventory of approved and unapproved AI usage
• Create a practical starting point for AI governance

AI Accesses Sensitive Data

Once an organization has an inventory of its AI systems and usage, the next question is more important: what is AI touching? Atlas is designed to connect AI visibility with data context so teams can understand what AI systems exist, what sensitive data they can access and where exposure may exist. Varonis is now at the center of the AI-SPM conversation. Atlas helps teams understand which AI exists, what data it can access and where risk needs to be reduced. Atlas can also produce a comprehensive report of its findings.

Let us look at Figure 4 below. Atlas allows organizations to perform investigations into CVE vulnerabilities, misconfigurations, model scan vulnerabilities and even sensitive data and agentic findings, all from a single pane of glass. In this example, we can see CVEs. From there, we can get an overview of the issues, assess and even remediate the issues. Remediation within the platform allows for automatically creating pull requests to update libraries on the fly.

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Figure 4 – CVE Vulnerability Findings

This is especially important in environments where AI connects to enterprise systems such as Microsoft 365, Salesforce, Jira, Slack and cloud data platforms. In those environments, the risk is real.

This use case makes Atlas easier to understand because it ties AI risk directly to a business problem companies already understand: Protecting sensitive data and reducing overexposure.

Significance:

• Map AI usage to actual enterprise data exposure
• Prioritize remediation based on sensitive data access
• Focus conversations on data risk

Guardrails – Stop Risky AI Behavior

Inventory and visibility alone are critical starting points. However, organizations need controls to prevent unsafe behavior before it becomes an incident. Atlas also provides runtime guardrails that enforce policy across AI activity in real time.

In practice, that means helping customers prevent sensitive data leakage, blocking malicious or non-compliant usage and constraining risky AI behavior before the result reaches an end user or causes downstream changes in the environment.

For example, see Figure 5 below for a policy that can detect whether text was human- or AI-generated and protect it. Or imagine you want to configure a policy to remove personally identifiable information (PII) from a prompt. All of this is possible with Atlas.

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Figure 5 – Setting Guardrails

For many buyers, this is where Atlas becomes especially compelling. Monitoring can tell you that something bad happened. Guardrails are about reducing the chances that it happens at all.

Significance:

• Prevent sensitive data from flowing into the wrong AI systems
• Block or redact non-compliant or risky behavior in real time
• Move from passive visibility to active control

AI Activity Monitoring

As AI adoption scales, teams need an operational view of how AI is being used. Atlas provides end-to-end activity monitoring across AI interactions, including LLM calls, data access and guardrail events.

This is not only a security capability. It is also a governance capability. Monitoring helps organizations understand which AI systems are being used, whether sanctioned tools are being used in approved ways and where usage patterns are drifting outside policy.

For customers who have moved beyond small pilots, this use case becomes critical because it provides the feedback loop needed to manage AI usage day-to-day.

Significance:

• Create an audit trail of AI interactions
• See risky or unusual AI activity earlier
• Support governance with real operational evidence

Respond!

No matter how mature an organization becomes, it still needs a way to detect and respond to AI-related issues as they become real incidents. Atlas is more than a discovery tool; it is also part of a broader detection-and-response model for AI usage.

This is important because AI incidents do not always look like classic cyber events. The trigger may be a risky data flow, a policy violation, an unsafe agent action or a vulnerable AI component. Detection and response are the layers that turn visibility into action. As a quick example, look at Figure 6 below to see a vulnerability Atlas has identified.

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Figure 6 - CVE-generated Incident

This use case ties the Atlas story together: inventory tells you what exists, data context tells you what is at risk, guardrails reduce unsafe behavior, monitoring shows what is happening and response helps contain what still gets through.

Significance:

• Real-time alerts on risky AI behavior
• Investigate AI-related incidents with audit evidence
• Close the loop between AI visibility and security operations

Connecting Use Cases

This use-case structure shows how AI risk truly unfolds. Organizations do not start with random or abstract conversations about maturity. They start with real concerns and questions. Atlas is easiest and most accurately understood when it is tied to those.

Have you found yourself or your organization asking any of the questions below? 

• Do we know where AI is being used?
• Do we know what sensitive data it can reach?
• Can we stop risky behavior within AI platforms?
• Can we see suspicious activity within the context of AI usage?
• Can we respond with evidence if something breaks?

Conclusion

I have covered several key use cases for Varonis Atlas and AI security in this post. As mentioned, stay tuned for future posts focused specifically on key areas within these common use cases and more. Remember, Varonis Atlas is best understood as a comprehensive platform designed to help organizations inventory AI, understand data exposure, apply runtime controls, monitor usage and respond to incidents in one connected model. Most enterprises are adopting copilots, agents and custom AI systems. There is a distinct difference between knowing where AI is in use and being able to govern it safely.

Further, AI magnifies data security concerns like never before. Enterprises have always needed data security; now they need it more than ever. They need AI security that understands the data, identities and controls underneath it. Varonis understands this clearly, and Atlas is putting this understanding into practice in the AI era.

We can help! Reach out to your Optiv Client Manager for a complimentary AI risk assessment with Atlas.