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How Traffic, State, and Organizational Data Help Fortify Your Network

Traffic data is the lifeblood of network security, representing the raw, unfiltered truth of what is happening on the network.

Secure Network Perimeter

In the realm of cybersecurity, understanding the various data types within an infrastructure is essential for effective defense and management. These data types serve as the foundation for identifying, analyzing, and responding to potential threats. Let’s delve into the four critical data types: traffic data, state data, event data, statistical data, and organizational data, to understand their significance and application in security.

Traffic Data: The Ground Truth

Traffic data is the lifeblood of network security, representing the raw, unfiltered truth of what is happening on the network. Historically, this has been captured through raw packet captures, but the scope has broadened to include flow logs and traffic-based logs from DNS, HTTP, VPN, and ZTNA. This data type is invaluable for investigating threats, detecting compromises, and identifying anomalies, as it provides a direct look at the bits on the wire and the actual communications taking place.

State Data: The Infrastructure’s Pulse

State data offers insights into the current status of the network and its devices. It answers critical questions such as which devices are active or offline, leveraging technologies like SNMP and streaming telemetry for device state, and synthetic monitoring tools like ThousandEyes, Kentik, and Catchpoint for network state. This data is crucial for identifying changes within the network, whether they are intentional or not, thus aiding in the management and security of the infrastructure.

Event Data: Interpreting Activity

Event data is generated when network and security tools analyze traffic to identify threats and behavioral anomalies. Tools such as EDR, DLP, IDS/IPS, and NDR play a pivotal role here, interpreting traffic to provide meaningful insights into network activity. The challenge with event data lies in its volume; not all events are equally important, and distinguishing critical alerts from noise is essential. The goal is to extract meaningful information that answers the who, what, when, where, and why of network activities.

Statistical Data: Understanding Scale and Behavior

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Statistical data about traffic and devices helps contextualize the amount of specific activities occurring within the network. It’s particularly useful in security for gauging the scale of certain behaviors, such as the number of attempts to access malicious domains. This data can elevate an event from being merely curious to critically important, providing a quantitative basis for assessing threats.

Organizational Data: Context and Ownership

Organizational data provides crucial context to the network’s activities, detailing information about users, devices, responsible groups, operating systems, and applicable security policies. It plays a pivotal role in defining what is considered normal behavior and assists in prioritizing security efforts based on the risk profiles and policies specific to each device.

Traditionally, organizational data might be consulted as the final step in security analysis, primarily to determine the ownership of a host after identifying an issue. However, integrating this data earlier in the analytical process can significantly empower security analysts. By enriching traffic and event data with organizational insights, analysts are equipped with a more nuanced understanding of the network, enhancing their ability to safeguard it effectively. This proactive use of organizational data not only accelerates response times but also improves the precision of security measures, ensuring that resources are focused where they are most needed.

The Challenge of Integration

No single platform can consolidate all these data types effectively. The key to a robust security posture is selecting an ecosystem of complementary platforms, each adding value individually and in combination with others. Interoperability among these platforms enables a more cohesive security approach, enhancing the ability to share data and insights, which in turn, reduces response times in a data-rich environment.

In conclusion, the complex landscape of network security demands a multifaceted approach to data analysis. By understanding and leveraging the unique strengths of traffic data, state data, event data, statistical data, and organizational data, organizations can enhance their security measures, making informed decisions to protect their infrastructure. The integration of these data types through a complementary ecosystem of security tools represents the best strategy for navigating the ever-evolving threat landscape, ensuring a proactive and responsive security posture.

Written By

Over his 25+ year career, Matt has held senior technology leadership positions across numerous industries including Netography, Neustar, Verisign, and Prolexic Technologies. With a rich background in innovation and go-to-market strategies, Matt has been a critical leader in helping many companies conceptualize solutions from the customer lens and drive them to market with significant impact.

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