Google Security-Operations-Engineer - Google Cloud Certified - Professional Security Operations Engineer (PSOE) Exam

Comprehensive and Detailed Explanation

The correct answer is Option C. The question asks for the immediate action to remediate the existing compliance drift, which is the VM that already has an external IP address.

Option C (Remediate): Reconfiguring the VM's network interface to remove the external IP directly fixes the identified misconfiguration. This action brings the resource back into compliance, which will cause the Security Command Center finding to be automatically set to INACTIVE on its next scan.2

Option A (Prevent): Applying the organization policy constraints/compute.vmExternalIpAccess is a preventative control.3 It will stop new VMs from being created with external IPs, but it is not retroactive and does not remove the external IP from the already existing VM. Therefore, it does not remediate the current finding.

Option B (Mask): Removing the tag simply hides the resource from the posture scan. This is a violation of compliance auditing; it masks the problem instead of fixing it.

Option D (Ignore): Marking a finding as fixed without actually fixing the underlying issue is incorrect and will not resolve the compliance drift. The finding will reappear as ACTIVE on the next scan.

Exact Extract from Google Security Operations Documents:

Finding deactivation after remediation: After you remediate a vulnerability or misconfiguration finding, the Security Command Center service that detected the finding automatically sets the state of the finding to INACTIVE the next time the detection service scans for the finding.4 How long Security Command Center takes to set a remediated finding to INACTIVE depends on the schedule of the scan that detects the findin5g.

Organization policy constraints: If enforced, the constraint constraints/compute.vmExternalIpAccess will deny the creation or update of VM instances with IPv4 external IP addresses.6 This constraint is not retroactive and will not restrict the usage of external IPs on existing VM instances. To remediate an existing VM, you must modify the instance's network interface settings and remove the external IP.

Comprehensive and Detailed Explanation

The goal is to automate a decision-making process within a SOAR playbook based on data from an integration. This requires two steps: getting the specific data point (Option E) and then using it in a logical operator (Option A).

Get the Data Point (Option E): The VirusTotal integration returns a detailed JSON object. The most critical data point for determining maliciousness is the number of detections (i.e., how many scanning engines flagged the URL). The playbook must parse this specific value from the JSON output.

Use the Data in Logic (Option A): Once the playbook has the number of detections, it must use a conditional statement (an "If/Then" block) to act on it. This logic is how the playbook makes a recommendation and sets the severity. For example: IF number_of_detections > 3, THEN set severity to CRITICAL and add a comment URL is suspicious. ELSE, set severity to LOW and add a comment URL appears benign.

Option C is incorrect as it describes a manual process, which defeats the purpose of automation. Option D is incorrect as widgets are for displaying data in the case UI, not for executing logic within a playbook.

Exact Extract from Google Security Operations Documents:

Playbook logic and conditional actions: SOAR playbooks execute a series of actions to automate incident response. A core component of this automation is the conditional statement. After an enrichment action (like querying VirusTotal) runs, the playbook can use a conditional block to evaluate the results.

The playbook can parse the JSON output from the integration to extract key values, such as the number of positive detections. This value can then be used in the conditional (e.g., IF detections > 0) to determine the next step, such as setting the alert's severity, escalating to an analyst, or automatically determining if an indicator should be treated as suspicious or benign.

Comprehensive and Detailed Explanation

The correct solution is Option B. This is a common false positive tuning scenario.

The "high priority network indicators" rule set triggers when it sees a connection to or from a known-malicious IP or domain. The problem states the false positives are coming from the on-premises proxy servers.

This implies that the proxy server itself is initiating traffic that matches these indicators. This is often benign, legitimate behavior, such as:

Resolving a user-requested malicious domain via DNS to check its category.

Performing an HTTP HEAD request to a malicious URL to scan it.

Fetching its own threat intelligence or filter updates.

In all these cases, the source of the network connection is the proxy server. In the Unified Data Model (UDM), the source IP of an event is stored in the principal.ip field.

To eliminate these false positives, you must create a rule exclusion (or add a not condition to the rule) that tells the detection engine to ignore any events where the principal.ip is the IP address of your trusted proxy servers. This will not affect the rule's ability to catch a workstation behind the proxy (whose IP would be the principal.ip) connecting through the proxy to a malicious target.ip.

Exact Extract from Google Security Operations Documents:

Curated detection exclusions: Curated detections can be tuned by creating exclusions to reduce false positives from known-benign activity. You can create exclusions based on any UDM field.

Tuning Network Detections: A common source of false positives for network indicator rules is trusted network infrastructure, such as proxies or DNS servers. This equipment may generate traffic to malicious domains or IPs as part of its normal operation (e.g., DNS resolution, content filtering lookups). In this scenario, the traffic originates from the infrastructure device itself. To filter this noise, create an exclusion where the principal.ip field matches the IP address (or IP range) of the trusted proxy server. This prevents the rule from firing on the proxy's administrative traffic while preserving its ability to detect threats from end-user systems.

Comprehensive and Detailed 150 to 250 words of Explanation From Exact Extract Google Security Operations Engineer documents:

To reduce false positives in Threat Intelligence (TI) matching rules, the standard practice is to filter based on the confidence or severity of the threat indicator. Threat feeds often contain "noisy" indicators (like IP addresses associated with low-risk spam or scanning).

In Google Security Operations, entity context (such as TI data) is accessed in YARA-L using the graph variable. The UDM (Unified Data Model) field structure for threat severity within an entity is metadata.threat.severity.

By modifying the rule to include a condition checking graph.metadata.threat.severity, you ensure the rule only triggers on indicators that the intelligence source has deemed "High" or "Critical," thereby filtering out low-fidelity noise.

Option B correctly applies this logic: $graph.metadata.threat.severity = "CRITICAL" or $graph.metadata.threat.severity = "HIGH".

Option D (Adjusting the match window) only changes the frequency of grouping, not the criteria for what constitutes a threat, so the false positive IP would still trigger an alert (just once per day).

Option C refers to risk_score, which is often a calculated aggregate, whereas threat.severity is the direct attribute from the TI feed (MISP) needed for this specific tuning.

Google Security Operations (SecOps) SOAR is designed to natively measure and report on key SOC performance metrics, including MTTR. This calculation is automatically derived from playbook case stages.

As a case is ingested and processed by a SOAR playbook, it moves through distinct, customizable stages (e.g., "Triage," "Investigation," "Remediation," "Closed"). The SOAR platform automatically records a timestamp for each of these stage transitions. The time deltas between these stages (e.g., the time from when a case entered "Triage" to when it entered "Remediation") are the raw data used to calculate MTTR and other KPIs.

This data is then aggregated and visualized in the built-in SecOps SOAR reporting and dashboarding features. This is the standard, out-of-the-box method for capturing these metrics. Option C describes a manual, redundant process of what case stages do automatically. Option D describes where the data might be viewed (Looker), but Option B describes the underlying mechanism for how the MTTR data is captured in the first place, which is the core of the question.

(Reference: Google Cloud documentation, "Google SecOps SOAR overview"; "Manage playbooks"; "Get insights from dashboards and reports")

The most efficient and native solution is to use the Google Cloud operations suite. Google Security Operations (SecOps) automatically exports its own ingestion health metrics to Cloud Monitoring. These metrics provide detailed information about the logs being ingested, including log counts, parser errors, and event counts, and can be filtered by dimensions such as hostname.

To solve this, an engineer would navigate to Cloud Monitoring and create a new alert policy. This policy would be configured to monitor the chronicle.googleapis.com/ingestion/log_entry_count metric, filtering it for the specific hostname of the critical Windows server.

Crucially, Cloud Monitoring alerting policies have a built-in condition type for "metric absence." The engineer would configure this condition to trigger if no data points are received for the specified metric (logs from that server) for a duration of 30 minutes. When this condition is met, the policy will automatically send a notification to the desired channels (e.g., email, PagerDuty). This is the standard, out-of-the-box method for monitoring log pipeline health and requires no custom rules (Option B) or custom heartbeat configurations (Option C).

(Reference: Google Cloud documentation, "Google SecOps ingestion metrics and monitoring"; "Cloud Monitoring - Alerting on metric absence")

Comprehensive and Detailed Explanation

The correct solution is Option D. The problem is that a known, trusted principal (the backup tool's service account) is performing a legitimate action (storage.objects.list) that happens to look like the suspicious behavior the rule is designed to catch.

The most precise and effective way to reduce these false positives without weakening the rule's ability to catch malicious actors is to create an exception for the trusted principal.

By adding principal.user.email != "backup-bot@fcobaa.com" (or the equivalent principal.user.userid) to the events or condition section of the YARA-L rule, the rule will now only evaluate events where the actor is not the known-good backup bot.

Option A is incorrect because it just lowers the priority of the false positive; it doesn't stop it from being generated.

Option B is incorrect because the legitimate tool might also perform repeated calls, leading to the same false positive.

Option C is incorrect because api.service_name = "storage.googleapis.com" is less specific than api.operation = "storage.objects.list" and would likely increase the number of false positives by triggering on any storage API call.

Exact Extract from Google Security Operations Documents:

Reduce false positives: When a detection rule generates false positives due to known-benign activity (e.g., from an administrative script or automation tool), the best practice is to add a not condition to the rule to exclude the trusted entity.8

You can filter on UDM fields to create exceptions. For example, to prevent a rule from firing on activity from a specific service account, you can add a condition to the events section such as:

and $e.principal.user.userid != "trusted-service-account@project.iam.gserviceaccount.com"

This technique, often called "allow-listing" or "suppression," improves the rule's accuracy by focusing only on unknown or untrusted principals.

The core of this problem is the unreliable data quality for the file hash. A robust detection strategy cannot depend on an unreliable data point. Options B and C are weak because they create a dependency on the SHA256 hash, which the prompt states is "not reliably captured." This would lead to missed detections. Option A is far too broad and would generate massive noise.

The best detection engineering practice is to use the reliable IoCs in a flexible and high-performance manner. The domain is a reliable IoC (from DNS logs), and the hash is still a valuable IoC, even if it's only intermittently available.

The standard Google SecOps method for this is to create a List (referred to here as a "data table") containing both static IoCs: the hash and the domain. An engineer can then write a single, efficient YARA-L rule that references this list. This rule would trigger if either a PROCESS_LAUNCH event is seen with a hash in the list or a NETWORK_DNS event is seen with a domain in the list (e.g., (event.principal.process.file.sha256 in %ioc_list) or (event.network.dns.question.name in %ioc_list)). This creates a resilient detection mechanism that provides two opportunities to identify the threat, successfully working around the unreliable data problem.

(Reference: Google Cloud documentation, "YARA-L 2.0 language syntax"; "Using Lists in rules"; "Detection engineering overview")

The requirement to detect activity that is *unusual* compared to a *user's established baseline* is the precise definition of **User and Endpoint Behavioral Analytics (UEBA)**. This is a core capability of Google Security Operations Enterprise designed to solve this exact problem with **minimal effort**.

Instead of requiring analysts to write and tune custom rules with static thresholds (like in Option A) or configure external metrics (Option B), the UEBA engine automatically models the behavior of every user and entity. By simply **enabling the curated UEBA detection rulesets**, the platform begins building these dynamic baselines from historical log data.

When a user's activity, such as data download volume, significantly deviates from their *own* normal, established baseline, a UEBA detection (e.g., `Anomalous Data Download`) is automatically generated. These anomalous findings and other risky behaviors are aggregated into a risk score for the user. Analysts can then use the **Risk Analytics dashboard** to proactively identify the highest-risk users and investigate the specific anomalous activities that contributed to their risk score. This built-in, automated approach is far superior and requires less effort than maintaining static, noisy thresholds.

*(Reference: Google Cloud documentation, "User and Endpoint Behavioral Analytics (UEBA) overview"; "UEBA curated detections list"; "Using the Risk Analytics dashboard")*