The Hidden Risk Within
Every CISO and Security Operations leader knows the familiar call from Legal or HR: “Can you tell us what this employee did five months ago?” Unfortunately, the answer is difficult to gather – there are indicators such as someone uploading files to a public file sharing site or transferring data to a removable media device. We know that breaches originate from different attack vectors, however, the more damaging breaches may often come from inside the organization.
Insider threats are a common threat that every organization encounters at some point. As cybersecurity professionals, we need the ability to quickly detect insider threats before it escalates into a business threat such as corporate espionage. Cybersecurity is more than reviewing proxy, DNS, endpoint, etc. logs. It’s about understanding behavior—context and intent are key to understand motive.
From Action to Intent: The Evolution of Insider Threat Monitoring
In the past, cybersecurity analysts could tell you how data left the network, but not why or what. For example, proxy logs can provide insights into which websites were accessed, but it cannot tell us the content of the data that was sent.
Security Operations have relied on tools like DLP and SIEM to detect insider threats. But these tools rarely provide a complete picture. A visit to ChatGPT or Gemini might be benign—or it could be the prelude to intellectual property theft. The difference lies in context, which traditional systems fail to capture.
AI Conversations: A Window Into Employee Wellbeing and Risk
With the rise of enterprise AI platforms—think ChatGPT, Gemini, Microsoft Copilot, etc.—we have a new data point to identify potential insider threats. These systems are where employees vent, ask questions, and even express frustration. The way people interact with public LLMs is conversational.
By using an AI observability solution, organizations can now detect early signs of burnout, frustration, or disengagement. When an employee starts exhibiting patterns consistent with potential resignation, fatigue, or hostility, AI systems can potentially flag on that behavior. If paired with traditional security alerts (like large file transfers), the result is higher fidelity.
Turning Data Into Defense
Here’s a real-world scenario: An AI observability monitoring tool detects that an employee has made a series of increasingly negative statements—“How do I tell my manager I am leaving in two weeks?,” or “Create a smooth transition plan before quitting”—flagging them as a potential flight risk. Days later, that same employee triggers a DLP alert for copying files to a USB drive. Separately, these two signals are not enough to start an investigation. However, correlating these two alerts provides a higher fidelity detection for a potential insider threat.
By forwarding these AI-based alerts to a SIEM, a Security Operations Center can gain the ability to correlate human sentiment with technical activity in real-time. This allows SecOps to become proactive in identifying this behavior before it becomes a larger issue.
Centralized Intelligence: The Missing Link
However, there’s a catch. For this to work, AI conversation insights need to be retained in a centralized, queryable format. The AI observability tool must be able to integrate with existing SIEMs. Without centralized retention and integration, valuable context is lost.
Too often, by the time HR or Legal asks for a retrospective review, those AI conversations are either deleted or siloed in systems that don’t talk to each other. By retaining AI-driven behavioral data securely, companies can unlock long-term visibility—answering the “what happened five months ago?” question with clarity, not guesswork.
Building the Future Insider Threat Stack
To enable your organization, consider building an insider threat detection stack that integrates:
- AI Observability Solution that inspects traffic via the network
- Centralized logging for AI conversations, fully integrated with your SIEM
- Automated correlation alerts that link behavioral and technical security alerts
- Partnership with HR to act on early warnings before they escalate—create a playbook
Understanding Behavioral Patterns
Over the course of multiple AI interactions with matching intents, an AI observability tool can score a disengagement sentiment. This score aggregates patterns—rising exit-intent queries, shifts in sentiment, intensified venting. As new conversations occur, the score updates in real time, giving teams a predictive view of emerging disengagement long before a formal resignation is announced. While disengagement is just one aspect to it, this can be applied to other behaviors of organizational interest.
Conclusion: Seeing the Full Picture Before It’s Too Late
By leveraging an AI observability solution like WitnessAI to analyze employee sentiment, organizations gain a new layer of intelligence. It’s not just about knowing that someone moved data—it’s about understanding why they did it.
Early detection of disengagement, burnout, or potential exits can help prevent data loss and protect intellectual property. When behavioral alerts are correlated with other security alerts in real time, they tell a story with substance. And in cybersecurity, the ability to understand the series of events before it becomes a larger issue can make a difference.
Related resource: 5 AI Insider Threat Signals You Can’t Ignore