ExtraHop Delivers AI Asset Inventory and Real-Time Observability for Advanced Threat Detection

ExtraHop, a leader in modern network detection and response (NDR), is introducing a comprehensive approach to de-risking AI innovation by providing enterprises with the definitive visibility and oversight required to manage their AI and agentic infrastructure.

According to the company, to move forward on their agentic journeys, enterprises must establish a foundation of total AI transparency, monitoring what is connected to the network, what it is doing, and how it is interacting with other systems, while ensuring that autonomous workflows remain within defined safety parameters and security boundaries.

ExtraHop is helping organizations address this need, ensuring operational integrity for the agentic enterprise by delivering deep insights that transform the network into a source of truth for AI observability, threat investigation and response, and governance.

Organizations can baseline their approved AI tools and immediately flag unsanctioned entities the moment they spin up on the network by automatically discovering and mapping:

• LLM usage across cloud and on-prem environments to identify unauthorized or insecure model access.
• MCP servers, APIs, and tool endpoints, which often serve as high-privilege gateways to sensitive internal data.
• Agents and their communication patterns to reveal hidden paths between agents and the core network.

As AI use expands across the enterprise, visibility becomes non-negotiable. ExtraHop closes this gap by monitoring, decrypting, and analyzing AI traffic in real time to surface both operational and behavioral insights, the company said.

Correlating actions across the AI stack with the devices and identities behind it provides the context necessary to detect unauthorized data movement, privilege escalation, and anomalous agent behavior. This granular visibility covers:

• LLM request/response behavior to identify anomalies in intent, prompt structure, or data output that signal a compromised interaction.
• MCP-mediated tool usage, surfacing exactly which internal systems an agent is calling and the specific actions it is performing within them.
• Identity propagation, tracking how credentials and permissions move across multi-step workflows.

When agentic infrastructure is manipulated by threat actors, they can bypass security boundaries, abuse privileges, or leak sensitive information.

ExtraHop addresses these risks by recognizing the specific network patterns of an AI-based compromise in real time. By monitoring for deviations from established behavioral baselines, the platform identifies critical AI-specific threats, including:

• Prompt injection attempts, detecting anomalous prompt structures designed to bypass safety filters.
• Suspicious data flows that indicate potential exfiltration or unintended agent behavior.
• Unexpected or risky agent actions that deviate from established behavioral baselines.

“AI is the ultimate competitive advantage, yet it quickly becomes a disadvantage if deployed without transparency and control,” said Kanaiya Vasani, chief product officer, ExtraHop. “To scale safely, enterprises must establish definitive oversight of every agent and autonomous workflow on their network. By harnessing deep network insights, we are giving leaders the real-time visibility and context they need to move fast and innovate boldly, ensuring their AI remains a powerful engine for growth rather than an unmanaged risk.”

For more information about this news, visit www.extrahop.com.