Overview
AI agents can access sensitive data and take action across trusted systems, but many security tools only capture a narrow view of what happens. Prompts and API activity don’t always reveal who initiated an action, what an agent did next, or where data moved.
DTEX AI Risk Management (AIRM) brings insider risk methods to AI-driven workflows. It connects AI activity with behavioral context and activity lineage, so organizations can distinguish human actions from autonomous execution to assess risk.
The solution brief explains how AIRM extends visibility beyond the browser to uncover shadow AI and trace agent activity, identifying behavior that warrants investigation.
What You'll Learn
- Why AI systems with human-level access should be evaluated as digital insiders.
- How behavioral context reveals risk that prompt and API monitoring can miss.
- How AIRM distinguishes human activity from autonomous agent execution
- How security teams can uncover shadow AI and trace agent behavior across the enterprise
Frequently Asked Questions
What is AI risk management?
AI risk management is the process of identifying and addressing risks created by AI use. This includes how people use AI tools, how autonomous agents behave, what data AI systems access, and what actions occur across connected workflows.
What is DTEX AI Risk Management?
DTEX AI Risk Management, or AIRM, helps organizations understand and prioritize risk from enterprise AI use and agentic workflows. It uses behavioral context and activity lineage to evaluate actions across humans, AI, and the data they access.
How does AIRM identify shadow AI?
AIRM provides visibility into unsanctioned AI tools and unapproved workflows used through browsers, applications, or other endpoint utilities. This helps organizations find AI activity that may fall outside approved channels.
How does AIRM support AI agent oversight?
AIRM captures what an agent was instructed to do and traces the resulting activity across systems. It helps teams distinguish autonomous execution from human-driven activity while identifying anomalous or risky agent behavior.
Why isn't prompt monitoring enough to manage AI risk?
Prompt content doesn’t show every action an AI system takes, and it may not be available for some agent workflows. AIRM supplements available prompt data with behavioral and data movement signals to provide a broader view of activity and risk.
Ready to Learn More?
Learn how AI Risk Management connects human behavior, AI activity, and data exposure to uncover risk.