Overview
AI risk management for agentic workflows comes down to a few concrete questions: who is acting, what system or agent is involved, what data is being touched, and what the action appears to be trying to do. DTEX AI Risk Management brings insider risk strategy into AI-driven workflows by treating AI systems with human-level access as insiders.
That matters because prompt-level or API-only controls miss too much of the work. DTEX AIRM connects AI activity with human behavior, inferred intent, prompt and data lineage, continuous risk intelligence, and triage workflows so teams can understand, prioritize, and stop risk from AI use and agentic workflows across the enterprise.
AIRM looks across users, agents, and workflows. It captures what an agent was asked to do, records what happened across systems, and gives endpoint visibility beyond the browser. It also correlates input and interaction signals over time with process, file, and network activity, helping teams separate human-driven activity from autonomous execution.
What You'll Learn
- How DTEX treats AI systems with human-level access as insiders in AI-driven workflows.
- Why thin, event-only SOC telemetry can strip away context and cause agents to fill gaps.
- How AIRM correlates input and interaction signals with process, file, and network activity over time.
- How AIRM supports shadow AI discovery and AI agent oversight across browsers, IDEs, applications, and non-browser utilities.
Frequently Asked Questions
What is AI risk management for agentic workflows?
AI risk management for agentic workflows focuses on how humans and AI systems interact, what data they access, and the intent behind their actions. DTEX AI Risk Management applies insider risk strategies to AI-driven workflows and treats AI systems with human-level access as insiders.
How does DTEX AIRM support an AI risk management framework?
DTEX AIRM supports an AI risk management framework by connecting AI activity, human behavioral insights, inferred intent, prompt and data lineage, continuous risk intelligence, and triage workflows. This helps organizations understand, prioritize, and stop risk created by AI usage and agentic workflows across the enterprise.
What makes DTEX AIRM different from AI security tools focused on prompts or APIs?
DTEX AIRM analyzes actions across users, agents, and workflows, rather than limiting review to prompts or APIs. It captures what agents were asked to do, records what occurred across systems, and provides endpoint visibility beyond the browser.
How does AIRM improve AI risk assessment for autonomous agents?
AIRM improves AI risk assessment by correlating input and interaction signals with process, file, and network activity over time. It also uses AI-specific risk intelligence to distinguish human-driven activity from autonomous execution.
How does DTEX help with shadow AI discovery and AI agent oversight?
DTEX supports shadow AI discovery and AI agent oversight across browsers, IDEs, applications, and non-browser utilities. This visibility helps organizations monitor AI usage and agentic workflows across the enterprise.
Why does context matter in AI risk mitigation for agentic workflows?
Thin, event-only SOC telemetry can remove context and lead agents to fill gaps. DTEX AIRM correlates activity across users, agents, workflows, systems, and endpoint signals so teams can understand and prioritize AI risk.
Ready to Learn More?
See how the DTEX Platform helps teams detect and mitigate insider risk. Request a demo.