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Detecting AI Activity: How to Meet the New Demands of Network Security and Cyber Defense in Your Solutions

The explosion in generative AI (GenAI)-powered applications has reshaped the digital enterprise landscape — bringing extraordinary new capabilities but also introducing serious governance and security challenges for IT and security teams. These challenges include:

  • Data leakage due to users unknowingly uploading confidential data into external AI tools.
  • The use of unauthorized (or ‘shadow’) AI tools that violate security policies and create network blind spots.
  • The use of compromised or flawed AI Agents that may gain unauthorized access to sensitive systems or make poor decisions.
  • The use of deepfakes or other AI-generated content for fraud or social engineering attacks.

Mitigating risks like these requires continuous global visibility into AI network activity—both real-time and historical—so organizations can detect and respond to suspicious or noncompliant behavior before damage occurs.

Integrating AI Observability into Cybersecurity Solutions

Organizations now expect their cybersecurity software vendors to deliver AI observability alongside broader network visibility and risk management capabilities. Meeting this demand requires combining knowledge of AI systems and tools with deep expertise in traffic identification, protocol recognition, and network observability.

Accordingly, effective cybersecurity solutions for AI risks must:

  • Recognize the protocols and APIs used by the latest AI tools.
  • Capture AI-related activity via transactional metadata such as file uploads, downloads, and API requests.
  • Provide real-time and forensic data for monitoring AI agent behavior.
  • Perform granular network flow analysis to uncover hidden or emergent AI activity.
  • Maintain provenance tracking to detect manipulation and authenticate content origins.

This granular visibility is not only key to monitoring AI traffic but also to enforcing trust boundaries. In zero-trust environments, multifactor authentication remains essential — and the number and type of authentication factors are expected to grow significantly in the AI era.

AI observability can provide this enhanced contextual authentication data while also supporting continuous trust evaluation by monitoring AI activities for signs of anomalous behavior, and supporting adaptive access control, such as rerouting or blocking traffic, if a potential breach is detected.

The Role of Enea’s Qosmos ixEngine in AI Observability

Enea’s Qosmos ixEngine® delivers the embedded Deep Packet Inspection (DPI) and traffic intelligence that is essential for the global AI visibility and zero trust network protection demanded of network security systems today. As an embedded software component, Qosmos ixEngine integrates tightly into network security solutions, offering comprehensive insights into both general network behavior and AI-specific activity.

Key capabilities include:

  • Extensive protocol coverage for modern AI tools and services, such as:
    • LLM APIs (e.g., Anthropic, DeepSeek)
    • AI chatbots (e.g., Grok, ChatGPT)
    • Agentic AI frameworks (e.g., Model Context Protocol (MCP))
    • Personal assistants (e.g., Pi, Alexa)
    • Coding tools (e.g., Claude, Amazon CodeWhisperer)
    • Image generators (e.g., DALL·E, Midjourney)
    • Writing assistants (e.g., Writesonic, Copy.ai)
    • Email management (e.g. SaneBox, Mailbutler)
    • Multi-chatbot aggregator platforms (e.g., Poe)
    • Voice and video tools (e.g., RunwayML, Descript)
    • Productivity apps (e.g., Microsoft Copilot, Notion)
    • Evaluation tools for AI models (e.g. SWE-Bench, LMArena) 
  • Metadata for AI: including metadata for popular suites like ChatGPT and MS Copilot, and cross-protocol transactional metadata like file transfers and API call patterns.
  • Holistic network flow and transactional data capture, enabling both real-time and forensic analysis.
  • Provenance indicators that support authentication, deepfake detection, and AI deception prevention.

In addition, because Qosmos ixEngine captures metadata rather than full packets, forensic storage requirements are reduced by up to 150x, allowing for efficient long-term monitoring and analysis without compromising insight.

Replying to Enterprise Demands for AI-Threat Protection

As the use of AI agents, chatbots, assistants, and automated decision systems proliferate across corporate environments, traditional network visibility is no longer sufficient—AI observability is now a prerequisite for true network security.

By integrating Enea’s Qosmos ixEngine into network security solutions, vendors gain the deep, continuous insights demanded by security teams as they strive to ensure full protection of their systems, data, and reputations from the next generation of AI-driven threats.

Further Information:

  • To find out more about Enea’s DPI technology and network traffic identification expertise, click here.
  • To see the full list of protocols and applications recognized by Qosmos ixEngine, click here.
  • To find out more about Enea’s DPI technology and AI, click here.
  • To see a demo of Enea’s Embedded DPI technology in action:
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