Classify and analyze data by using entities

Microsoft 365 Defender provides a unified pre- and post-breach enterprise defense suite that natively coordinates detection, prevention, investigation, and response across endpoints, identities, email, and applications to provide integrated protection against sophisticated attacks. Within this suite, entities play a critical role. Here are some common entity types:

• User Entities: Represent individual user accounts. These entities contain information about user activities, sign-ins, and associated alerts.
• Host Entities: Include devices like computers and servers. Data concerning host configurations, installed software, and related alerts are aggregated under host entities.
• IP Address Entities: Refer to network addresses. These entities can be analyzed to identify suspicious activities or connections to known malicious IPs.
• File Entities: Represent files detected across the network, including their hash values, paths, and any associated threat intelligence.
• URL/URI Entities: Include web addresses that could be associated with phishing attempts or malware distribution.

Classification and Analysis of Entity Data

Step 1: Data Collection and Normalization

The first step in classifying and analyzing entity data is to collect and normalize the data. Microsoft security solutions, such as Microsoft Defender for Endpoint, collate data related to entities into a structured format that can be easily analyzed.

Step 2: Entity Behavior Analytics

Behavior analytics involves the creation of a baseline of normal behavior for each entity. Anomalies are then identified by comparing current data with the baseline.

For example, a user entity typically accesses the company resources during specific hours. If there is a login attempt during an unusual hour or from a geographically improbable location, this flagged event will be an anomaly that requires further investigation.

Step 3: Threat Detection and Response

Using the entity data, threat detection algorithms hunt for signs of known attack patterns, such as lateral movements, credential dumping, or data exfiltration attempts. For instance, multiple failed login attempts from a single IP address entity may indicate a brute force attack.

Security analysts can use tools provided by Microsoft, such as Microsoft Sentinel. Sentinel allows for setting up custom alert rules based on entity behaviors, correlating entity data with threat intelligence feeds, and creating automated responses to detected threats.

Step 4: Investigation and Remediation

Once suspicious activity is identified, the entity data can be used for detailed investigations. Analysts can visualize entity relationships and interactions using tools like the Microsoft 365 security center, which provides in-depth analysis and detailed timelines of entity-related activities.

For instance, a file entity suspected of being malware can be investigated by examining its creation, modification, the process that created the file, and any network connections it attempted to establish.

Step 5: Continuous Improvement and Adaptation

The final step is continuously improving the classification and analysis of entities by incorporating feedback from investigations and updates to threat intelligence. This ensures that entity behavior analytics remain accurate and that threat detection methods evolve to keep pace with the changing threat landscape.

Conclusion

For SC-200 Microsoft Security Operations Analyst exam candidates, understanding how to classify and analyze data by using entities is paramount. It enhances the ability to detect, investigate, and mitigate threats effectively. Real-world examples of entity analysis not only reinforce the concepts but also provide practical insights into the daily responsibilities of a Security Operations Analyst. By mastering entity classification and analysis, candidates will be well-equipped to add value to their organizations’ security operations centers.