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| Section | Objectives |
|---|---|
| Topic 1: Threat Detection and Incident Response | - Security incident investigation
|
| Topic 2: Security Operations in Google Cloud | - Security monitoring and logging (Cloud Logging / Cloud Monitoring)
|
| Topic 3: Identity and Access Security | - IAM security monitoring
|
| Topic 4: Cloud Security Posture and Compliance | - Security configuration assessment
|
| Topic 5: Automation and Response | - Security orchestration and response
|
| Topic 6: Google Security Operations Platform | - Chronicle / Google SecOps SIEM usage
|
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質問 # 37
You are responsible for identifying suspicious activity and security events in your organization's environment.
You discover that some detection rules are generating false positives when the principal.ip field contains one or more IP addresses in the 192.168.2.0/24 subnet. You want to improve these detection rules using the principal.ip repeated field. What should you add to the YARA-L detection rules?
正解：B
解説：
Comprehensive and Detailed Explanation
The correct solution is Option D. The goal is to exclude events (i.e., stop false positives) when the principal.
ip field contains any IP from the trusted 192.168.2.0/24 subnet.
The principal.ip field in UDM is a repeated field, meaning it can hold an array of values (e.g., ["1.2.3.4",
"192.168.2.5"]). YARA-L provides the any and all quantifiers to handle repeated fields.9
* any $e.principal.ip: This checks if at least one IP in the array meets the condition.
* all $e.principal.ip: This checks if every IP in the array meets the condition.
The function net.ip_in_range_cidr(...) returns true if an IP is in the specified range.
Therefore, the logic we need is: "do not trigger this rule if any of the IPs in the principal.ip field are in the
192.168.2.0/24 range."
This translates directly to the YARA-L syntax: not net.ip_in_range_cidr(any $e.principal.ip, "192.168.2.0/24")
* Option B would only find events from that subnet.
* Option A would only find events where all associated IPs are in that subnet.
* Option C is the logical inverse of A and would incorrectly filter out events that might be malicious (e.
g., ["1.2.3.4", "192.168.2.5"] would not be excluded because all IPs are not in the range).
Exact Extract from Google Security Operations Documents:
YARA-L 2.0 language syntax > Repeated fields and boolean expressions: When a boolean expression, such as a function call, is applied to a repeated field, you can use the any or all keywords to specify how the expression should be evaluated.10
* any <repeated_field>: The expression evaluates to true if it is true for at least one of the values in the repeated field.
* all <repeated_field>: The expression evaluates to true only if it is true for all of the values in the repeated field.
Functions > net.ip_in_range_cidr: The net.ip_in_range_cidr function is useful to bind rules to specific parts of the network.11 To exclude all private netblocks as defined in RFC1918, you can add a not to the start of the criteria:
and not (net.ip_in_range_cidr(any $e.principal.ip, "10.0.0.0/8") or net.ip_in_range_cidr(any $e.principal.ip,
"172.16.0.0/12") or net.ip_in_range_cidr(any $e.principal.ip, "192.168.0.0/16")) References:
Google Cloud Documentation: Google Security Operations > Documentation > Detections > YARA-L 2.0 language syntax Google Cloud Documentation: Google Security Operations > Documentation > Detections > YARA-L 2.0 functions > net.ip_in_range_cidr
質問 # 38
Your organization has mission-critical production Compute Engine VMS that you monitor daily.
While performing a UDM search in Google Security Operations (SecOps), you discover several outbound network connections from one of the production VMs to an unfamiliar external IP address occurring over the last 48 hours. You need to use Google SecOps to quickly gather more context and assess the reputation of the external IP address. What should you do?
正解：D
解説：
The fastest way to gather context and assess the reputation of the unfamiliar external IP is to search for the IP in the Alerts & IOCs page in Google SecOps. This page integrates with Google Threat Intelligence and enrichment data, allowing you to quickly evaluate whether the IP is malicious and see any related alerts or indicators in your environment.
質問 # 39
You have been tasked with developing a new response process in a playbook to contain an endpoint. The new process should take the following actions:
* Send an email to users who do not have a Google Security Operations (SecOps) account to request approval for endpoint containment.
* Automatically continue executing its logic after the user responds.
You plan to implement this process in the playbook by using the Gmail integration. You want to minimize the effort required by the SOC analyst. What should you do?
正解：C
解説：
This scenario describes an automated external approval, which is a key feature of Google Security Operations (SecOps) SOAR. The solution that "minimizes the effort required by the SOC analyst" is one that is fully automated and does not require the analyst to wait for an email and then manually resume the playbook.
The correct method (Option D) is to use the platform's built-in capabilities (often part of the "Flow" or
"Siemplify" integration) to generate a unique approval link (or "Approve" / "Deny" links). These links are tokenized and tied to the specific playbook's execution. This link is then inserted as a placeholder into the email that is sent to the non-SecOps user via the "Send Email" (Gmail integration) action.
The playbook is then configured with conditional logic (e.g., a "Wait for Condition") to pause execution until one of the links is clicked. When the external user clicks the "Approve" or "Deny" link in their email, it sends a secure signal back to the SOAR platform. The playbook automatically detects this response and continues down the appropriate conditional path (e.g., "if approved, execute endpoint containment"). This process is fully automated and requires zero analyst intervention, perfectly meeting the requirements.
Options A, B, and C all require manual analyst action, which violates the core requirement of minimizing analyst effort.
(Reference: Google Cloud documentation, "Google SecOps SOAR Playbooks overview"; "Gmail integration documentation"; "Flow integration - Wait for Approval")
質問 # 40
You are ingesting and parsing logs from an SSO provider and an on-premises appliance using Google Security Operations (SecOps). Users are tagged as "restricted" by an internal process.
Restrictions last five days from the most recent flagging time. You need to create a rule to detect when restricted users log into the appliance. Your solution must be quickly implemented and easily maintained. What should you do?
正解：B
解説：
The best solution is to ingest the user flags as custom enrichment data using a feed and then use a multi-event detection rule to detect logins from users flagged in the entity graph. This approach is quick to implement, integrates cleanly with Google SecOps, and ensures that restricted user flags are dynamically correlated without constant manual updates or complex rule rebuilding.
質問 # 41
You are a SOC analyst at an organization that uses Google Security Operations (SecOps). You are investigating suspicious activity in your organization's environment. Alerts in Google SecOps indicate repeated PowerShell activity on a set of endpoints. Outbound connections are made to a domain that does not appear in your threat intelligence feeds. The activity occurs across multiple systems and user accounts. You need to search across impacted systems and user identities to identify the malicious user and understand the scope of the compromise. What should you do?
正解：B
解説：
The most effective approach is to perform a YARA-L 2.0 search that correlates activity across impacted systems and user identities. YARA-L rules can link PowerShell execution events, outbound connections, and user activity, enabling you to identify the malicious user and the scope of the compromise efficiently, rather than relying on manual log searches or only analyzing authentication trends.
質問 # 42
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