Feb 28, 2026 Reliable Study Materials for XSIAM-Engineer Exam Success For Sure [Q213-Q237]

NO.213 An XSIAM Engineer is debugging a sophisticated parsing issue for cloud audit logs ingested via a custom API integration. The logs are JSON, but certain ‘details’ fields contain nested JSON strings that are not being correctly parsed as objects, but rather as raw strings. The goal is for these nested JSON strings to be parsed into actual JSON objects within XSIAM’s schema’. Given a raw log snippet like this:

The ‘event_data’ field is currently ingested as a string. How can the XSIAM parsing rule be modified to parse “event_data’ as a nested JSON object?

Use a regex in the parsing rule to extract the entire ‘event_data’ field as a string, then manually write a custom post-processing script to convert it to JSON. This is inefficient.

Within the XSIAM parsing rule for this data source, define the ‘event_data’ field as type ‘JSON’ (if supported) or use a ‘JSON Extractor’ processor specifically on the ‘event_data’ field to recursively parse its content. This involves specifying ‘json_extract: event_data’ or similar.

Change the source API integration to send the ‘event_data’ field as a pre-parsed JSON object, not a string. This requires source-side modification, which may not be feasible.

Apply a ‘mutate’ filter in the XSIAM ingestion pipeline to convert the ‘event_data’ string to a JSON object. This is typically done for simple type conversions, not complex nested parsing.

The XSIAM schema definition for ‘event_data’ needs to be changed from string to object. This alone won’t parse the string content.

NO.214 An XSIAM engineer is performing content optimization on indicator rules. They notice that a rule designed to detect ‘suspicious process injections’ is generating an alarmingly high number of alerts, primarily from legitimate debugging tools and application updates. The current rule uses a broad XQL query:

To reduce false positives without compromising the detection of malicious injections, which of the following modifications or considerations would be most effective? (Select all that apply)

Add a filter for to exclude injections originating from known legitimate processes like Visual Studio or trusted update services.

Implement a ‘risk_score’ threshold for the rule, only generating alerts if the aggregated risk score of the host or user exceeds a certain value.

Refine the XQL query to include additional conditions such as ‘target_process_integrity_level = ‘System” or ‘injection_type = ‘remote” if the data is available, as these are often indicators of malicious activity.

Create a pre-filtering rule with higher precedence to explicitly suppress alerts for processes with valid digital signatures and known clean hashes.

Adjust the rule’s ‘time window’ for correlation to a shorter duration, assuming malicious injections are instantaneous.

NO.215 As an XSIAM engineer, you are tasked with implementing a highly granular content optimization strategy using scoring rules. The requirement is that alerts from certain detection rules should have their scores influenced by a user’s department (e.g., ‘Finance’, ‘Engineering’) and, additionally, by the time of day (e.g., ‘business_hours’, ‘non_business_hours’). This means a ‘Suspicious Login’ from a ‘Finance’ user during ‘non_business_hours’ should have the highest score. Which XSIAM capabilities and best practices are crucial for achieving this complex scoring logic effectively and maintainably?

Define time-based lookup lists (e.g., ‘business_hours_ips’) and use a single scoring rule with a complex XQL join across alert data, user data, and time data to calculate the final score using a ‘case’ statement.

Utilize XSIAM’s built-in ‘Time Zones’ and ‘Business Hours’ configurations and create multiple, chained scoring rules. Each rule focuses on a specific condition (e.g., one for ‘Finance’ users, another for ‘non_business_hours’), with later rules applying additive or multiplicative changes based on combined context.

Create user-group-specific detection rules (e.g., ‘Suspicious Login – Finance’, ‘Suspicious Login – Engineering’) and manually assign different ‘rule_weight’ values based on department and time, duplicating detection logic.

Develop a custom Python script that periodically fetches all ‘Suspicious Login’ alerts via the XSIAM API, performs external calculations based on department and time, and then uses the API to update each alert’s score.

Integrate XSIAM with an external SOAR platform that receives all alerts, enriches them with department and time data, and then pushes back a calculated severity score to XSIAM.

NO.216 A large-scale XSIAM deployment is experiencing significant delays (hours) in log visibility from geographically dispersed Palo Alto Networks NGFWs, despite network connectivity being verified and NGFWs showing active log forwarding. The and metrics on the XSIAM Collectors indicate high activity, but is significantly lower. This suggests a bottleneck. Which of the following is the most effective immediate action to identify the specific bottleneck within the XSIAM data ingestion pipeline?

Increase the CPU and memory allocated to the XSIAM Collectors. This is a potential solution, but not an immediate identification of the specific bottleneck.

Review the XSIAM Collector’s ‘collector.log’ and ‘pipeline.log’ for errors or warnings related to parsing failures, unhandled events, or persistent backlogs in specific processing stages. Look for repeated messages indicating a slow parser or a problematic data source.

Temporarily disable all custom parsing rules and normalization rules for the affected data sources to see if performance improves. This helps isolate if custom logic is the bottleneck, but is disruptive.

Check the XSIAM Data Lake’s disk I/O performance and free space. While important, the metrics provided being low while is high) point to a pre-storage processing bottleneck.

Deploy additional XSIAM Collectors to distribute the load. This is a scaling solution, not an immediate troubleshooting step to identify the bottleneck.

NO.217 An XSIAM tenant is ingesting logs from a highly virtualized environment. Due to the ephemeral nature of some short-lived containers, the ‘Container Image Drift Detected’ rule generates frequent, legitimate alerts as containers are spun up and down with minor, expected variations. The security team wants to ignore these specific ‘drift’ alerts for containers that run for less than 5 minutes. Given that XSIAM’s exclusion logic primarily relies on event field values, how can this time-based condition be effectively managed to prevent alert generation?

Modify the ‘Container Image Drift Detected’ rule’s KQL query to include a time-based aggregation that only flags drift if the container has been active for more than 5 minutes.

Implement an XSIAM ‘Exclusion’ for the ‘Container Image Drift Detected’ rule, but this exclusion would need to reference a dynamic list of ‘short-lived’ container IDs. This list would be populated by a custom script parsing container lifecycle events outside XSIAM and then pushed to an XSIAM External Dynamic List (EDL).

Create a ‘Behavioral Baseline’ for container activity and only alert on deviations from this baseline, which implicitly handles short-lived containers.

Set up a Cortex XSOAR playbook that receives ‘Container Image Drift Detected’ alerts. For each alert, the playbook queries XSIAM for the container’s creation timestamp and, if the alert timestamp is within 5 minutes of creation, the playbook closes the incident and archives the alert.

XSIAM’s current exclusion framework does not natively support time-duration-based exclusions tied to arbitrary event fields like container lifespan; this scenario typically requires either rule modification or post-alert automation.

NO.218 Consider a large enterprise with a complex Cortex XSIAM deployment involving multiple on-prem collectors and integrations, and numerous custom playbooks. The security operations center (SOC) reports that for the past week, the XSIAM dashboard’s ‘Attacker Focus’ widget is consistently showing ‘No Data Available’ or outdated information, even though new incidents are being generated and observed in the ‘All Incidents’ view. Basic checks confirm collectors are online and ingesting data’. Which of the following is the most advanced and holistic troubleshooting approach to resolve this issue?

Check the XSIAM incident schema for any recent custom field additions or modifications that might conflict with the ‘Attacker Focus’ data model.

Review the health and performance metrics of the XSIAM backend services responsible for data aggregation and analytics, typically visible in the XSIAM ‘System Health’ dashboard (if available to administrators).

Verify that the XSIAM roles assigned to SOC analysts include permissions to view ‘Attacker Focus’ data.

Examine the ‘Data Source’ logs in XSIAM to identify any errors specific to the parsing or normalization of threat-related indicators.

Create a new custom dashboard with the same widgets to see if the issue persists on a fresh configuration.

NO.219 An XSIAM administrator is troubleshooting an issue where a specific set of XDR Agents are failing to connect to the XSIAM cloud after a Broker VM firmware update. Other agents are connecting successfully. The Broker VM’s status appears healthy in the XSIAM console, and network connectivity from the affected agents to the Broker VM is confirmed. Which of the following is the MOST likely cause and the first area to investigate on the Broker VM itself?

The Broker VM’s IP address changed after the update, and agents have not updated their configuration. Check the Broker VM’s network settings.

The Broker VM’s internal certificates expired or were corrupted during the firmware update. Review the Broker VM’s certificate status and logs for TLS/SSL errors.

The Broker VM’s inbound firewall rules were inadvertently reset during the firmware update, blocking agent connections. Verify the Broker VM’s firewall configuration.

The Broker VM’s internal proxy settings were cleared, preventing it from reaching the XSIAM cloud. Reconfigure proxy settings on the Broker VM.

The XDR Agent version on the affected endpoints is incompatible with the updated Broker VM firmware. Downgrade the Broker VM or upgrade the agents.

NO.220 An XSIAM deployment utilizes a custom data source for legacy security appliances that export logs in a unique, multi-line JSON format. A newly introduced log type from these appliances is failing ingestion, resulting in fragmented or truncated events in XSIAM. The custom XSIAM parsing rule is defined to handle multi-line events. Given the following snippet of a problematic log:

Which of the following is the most likely cause for the ingestion failure, and how should an XSIAM Engineer approach the fix?

The XSIAM Collector’s buffer is too small to handle large multi-line JSON events. Increase the collector’s ingestion buffer size via configuration files.

The multi-line log processing logic in XSIAM is not correctly identifying the end of an event. The presence of escaped newline characters (‘In’) within the ‘message’ field is confusing the parser, causing it to prematurely terminate the event. The XSIAM parsing rule needs a more robust ‘multiline_regex’ that explicitly identifies the start of a new JSON object (‘A(S) or end of an event CAY).

The JSON data contains invalid Unicode characters that XSIAM cannot parse. Convert the source logs to UTF-8 before sending them to the Collector.

The source appliance is sending events faster than the XSIAM Collector can process them, leading to dropped or truncated events. Implement flow control or reduce the sending rate on the source.

The custom data source mapping in XSIAM is attempting to parse the ‘details.message’ field as a single-line string, causing truncation. Modify the schema to handle multi-line strings or CLOB data types if available.

NO.221 Consider an organization deploying Palo Alto Networks XSIAM across multiple geographical regions. Region A is the primary data center with on-premises infrastructure, while Region B utilizes a public cloud provider (AWS). The XSIAM deployment in Region A is expected to handle 70% of the total data ingestion and 80% of query volume, with Region B serving as a disaster recovery site and handling the remaining load. Data must be replicated bidirectionally between regions with low latency. Which of the following hardware considerations are critical for ensuring data consistency and performance across this hybrid multi-region XSIAM deployment?

Implementing a hardware-based WAN optimization solution between Region A and Region B to accelerate data replication and reduce network latency.

Ensuring the on-premises storage in Region A is compatible with AWS S3 for seamless data tiering and archiving to the cloud.

Provisioning dedicated high-performance network links (e.g., AWS Direct Connect or equivalent) between Region A and the AWS region for Region B to minimize inter-region latency and maximize bandwidth.

Deploying identical server hardware specifications (CPU, RAM, storage) in both Region A and Region B to maintain consistent performance profiles.

Utilizing specialized network appliances for real-time data deduplication and compression before inter-region transfer.

NO.222 As part of XSIAM’s planning phase, an organization is assessing its existing data governance policies. They have strict data retention periods for different log types (e.g., 90 days for network flows, 1 year for endpoint activity, 7 years for audit logs). Additionally, certain data types are subject to anonymization requirements before being stored in a cloud platform. How can these requirements be reconciled with XSIAM’s unified data lake architecture, and what XSIAM features or best practices should be leveraged?

XSIAM’s unified data lake has a fixed, unconfigurable retention policy, so the organization must adjust its internal policies to match XSIAM. Anonymization requires manual pre-processing before ingestion.

XSIAM allows for configurable data retention policies based on data source or type, enabling different retention periods within the platform. For anonymization, XSIAM’s data transformation capabilities (e.g., during ingestion via Data Collectors or through specific mapping rules) can be used to mask sensitive fields before storage. Data governance should include proper role-based access control (RBAC) within XSIAM.

All data ingested into XSIAM is automatically anonymized and retained for 7 years by default, simplifying compliance. No further configuration is needed.

The organization should continue using their on-premise SIEM for long-term retention and anonymization, and only forward real-time, un-anonymized data to XSIAM for immediate threat detection.

XSIAM’s architecture is not suitable for organizations with complex data retention or anonymization requirements; they should consider an on-premise solution.

NO.223 A threat actor has gained initial access to an endpoint via a phishing email and is attempting to establish persistence. The XSIAM agent on the endpoint observes the following sequence of events:

Which of the following XSIAM BIOC rules would be most effective in detecting this specific persistence mechanism, prior to the ‘Registry.Key’ modification being observed, assuming the goal is to catch the initial malicious execution chain?

NO.224 An organization is deploying a new web application and wants to ensure robust detection of common web-based attacks using XSIAM.
They have observed several attempts of SQL Injection and Cross-Site Scripting (XSS) during pre-production testing. To optimize their detection content, which of the following XSIAM content management strategies would be most effective for creating high-fidelity detection rules for these attack types, leveraging both IOCs and BIOCs?

Rely solely on out-of-the-box XSIAM rules for web attacks, as they are generally comprehensive.

Create custom IOC rules based on known malicious IP addresses and URLs found in threat intelligence feeds related to web attacks.

Develop BIOC rules that analyze web server logs for unusual HTTP request parameters, abnormal response codes, and sequences of requests indicative of SQLi or XSS payloads, while also incorporating IOCs for known attack patterns.

Implement a simple keyword-based search in XSIAM for common SQLi keywords like ‘SELECT FROM’ and XSS keywords like ‘<script>’.

Configure network-based firewalls to block all traffic containing ‘SQL’ or ‘XSS’ in the payload.

NO.225 A critical zero-day vulnerability has been disclosed, and the XSIAM team needs to rapidly deploy a new detection rule. Due to the high potential impact, all alerts generated by this rule must immediately be prioritized and assigned the highest possible score, regardless of other contextual factors. Which XSIAM scoring rule configuration element is explicitly designed to achieve this immediate, overriding effect?

Setting the ‘Condition’ of the scoring rule to ‘always true’ and the ‘Score Modification Type’ to ‘Additive’ with a high value.

Utilizing the ‘Set Total Score’ action in a scoring rule, ensuring it’s evaluated with a high ‘Order’ and the target score is the maximum allowed (e.g., 100).

Applying a ‘Multiplicative’ score modification with a factor of 10 to any alert from this rule.

Configuring the ‘Rule Weight’ within the detection rule itself to its maximum value.

Disabling all other scoring rules that might affect alerts generated by this new rule.

NO.226 An XSIAM deployment is experiencing high ingestion rates, leading to increased costs and slower query performance. Analysis reveals that a significant portion of ingested logs, while voluminous, contributes little to high-fidelity detections for critical security use cases. The security team wants to optimize content ingestion to focus on high-value dat a. Which XSIAM content optimization strategy should be prioritized?

Increase the data retention period for all ingested logs to ensure historical analysis is always possible.

Implement data filtering at the ingestion point (e.g., via Cortex Data Lake or brokers) to only forward specific event types and fields required by existing detection rules and critical analytics.

Purchase additional XSIAM compute resources to handle the increased data volume more efficiently.

Disable all correlation rules and rely solely on raw log searches for incident investigation.

Migrate all security logs to a separate, cheaper storage solution and only send alerts to XSIAM.

NO.227 A systems engineer overseeing the integration of data from various sources through data pipelines into Cortex XSIAM notices modifications occurring during the ingestion process, and these modifications reduce the accuracy of threat detection and response. The engineer needs to assess the risks associated with the pre- ingestion data modifications and develop effective solutions for data integrity and system efficacy.
Which set of steps must be followed to meet these goals?

Develop an advanced monitoring system to track and log all changes made to data during ingestion, and use analytics to compare pre- and post-ingestion states based on XDM to identify and mitigate discrepancies.

Design a hybrid approach for critical data fields to be safeguarded against modifications during ingestion, while less critical data fields undergo allowable modifications that are rectified post-ingestion by using XDM to balance performance with data integrity.

Implement a pre-ingestion data validation process that aligns with the post-ingestion standards set by XDM, ensuring data consistency and integrity before it enters Cortex XSIAM.

Establish a process to minimize data modifications during ingestion, prioritizing raw data capture and using XDM post-ingestion for necessary transformations and integrity checks.

NO.228 An XSIAM administrator is configuring a dashboard for endpoint security posture. A key metric is the ‘Percentage of Endpoints with Outdated Antivirus Signatures’. The raw data in XSIAM’s endpoint_status_logs includes a boolean field is_signature_current. Which XQL snippet would accurately represent this metric in a percentage format for a dashboard widget?

NO.229 You are managing XSIAM XDR Collector updates for a large number of distributed collectors running on various Linux distributions. To ensure consistency and enable quick rollback if issues arise, you’ve decided to manage collector updates via configuration management tools (e.g., Ansible, Puppet) rather than relying solely on manual updates or in-place upgrades. Which of the following approaches is the MOST robust and recommended for managing XDR Collector updates using configuration management?

Use the configuration management tool to directly execute the collector’s built-in update script (e.g., ‘collector_update.sh’) on each server sequentially.

The configuration management tool should download the new collector installer, uninstall the old collector, then install the new one, verifying service status after each step.

Configure the XDR Collector to automatically fetch updates from Palo Alto Networks servers and use the configuration management tool only to monitor the collector’s status.

Maintain distinct configuration management playbooks/manifests for each XDR Collector version. To update, re-apply the playbook for the target version, ensuring idempotency and handling dependency updates (e.g., Python dependencies, libraries). Include pre-flight checks for prerequisites and post-update validation of data ingestion.

Push a Docker image update to a centralized Docker registry, and have the configuration management tool trigger a container restart on each host, pulling the new image.

NO.230 An XSIAM engineer needs to create an indicator rule that identifies attempts to disable security products. Specifically, the rule should look for command-line executions that attempt to stop or delete services related to Endpoint Detection and Response (EDR) agents or antivirus software, using common Windows commands like ‘sc’ or ‘taskkill’ combined with service names or process names. The challenge is to make this rule resilient to obfuscation and common legitimate administrative tasks. Which of the following XQL patterns best addresses this requirement for a high-fidelity indicator rule?

NO.231 An XSIAM engineer needs to implement a scoring rule that dynamically adjusts alert severity based on the ‘asset_criticality’ field, which is populated via an external CMDB integration. Alerts associated with assets marked ‘High’ criticality should receive a significant score boost, while ‘Low’ criticality assets should see a reduction. Which of the following XQL-like logic within a scoring rule’s condition and action configuration best supports this scenario, assuming ‘alert.asset_criticality’ is a field that holds ‘High’, ‘Medium’, or ‘Low’?

Condition: ‘alert.asset_criticality = ‘High” Action: Additive +30; Condition: ‘alert.asset_criticality = ‘Low” Action: Additive -15. Configure as two separate scoring rules with distinct orders.

Condition: ‘alert.asset_criticality in (‘High’, ‘Low’) Action: (alert.asset_criticality = ‘High’) then SetTotalScore(90) else SetTotalScore(30)’.

Condition: ‘alert.asset_criticality = ‘High” Action: Multiplicative x2.0; Condition: ‘alert.asset_criticality = ‘Low” Action: Multiplicative x0.5. Configure as two separate scoring rules.

Condition: ‘alert.asset_criticality = ‘High” Action: Additive +’alert.base_score 0.5; Condition: ‘alert.asset_criticality = ‘Low” Action: Additive ‘-alert.base_score 0.2.

Use a single scoring rule with a complex XQL case statement:

Options A and C are the most practical and effective ways to implement this in XSIAM’s scoring rules. Option A (Separate Additive Rules): This is a standard and clean way. You create one rule to boost ‘High’ criticality alerts and another to reduce ‘Low’ criticality alerts. Additive changes are direct and predictable. Option C (Separate Multiplicative Rules): This is also a very effective method. Multiplying by 2.0 significantly increases the score for ‘High’ assets, and multiplying by 0.5 effectively halves it for ‘Low’ assets. This maintains proportionality based on the initial score, which is often desirable for risk. Option B (‘Set Total Score’ with Conditional Logic): While ‘Set Total Score’ can be powerful, using ‘if/then/else’ directly within the action part like this with XQL is not the primary way XSIAM scoring rules are configured for score modification . ‘Set Total Score’ usually sets an absolute value, and complex conditional logic for modifying is done via separate rules or more advanced methods. This approach would also overwrite all previous scoring, which might not be desired for ‘boosting’ or ‘reducing’ an existing score. Option D (Dynamic Additive based on ‘base_score’): While theoretically possible, XSIAM’s direct scoring rule actions primarily support fixed additive/multiplicative values or ‘Set Total Score’. Performing dynamic calculations like ‘alert.base_score 0.5’ directly in the ‘Additive Score Change’ field is not a standard configuration option within the UI for score actions. Option E (Single rule with ‘case’ statement): XSIAM’s scoring rules are typically evaluated sequentially with simple conditions and actions per rule. Embedding complex ‘case’ statements for score modification directly within a single rule’s ‘Action’ field like this (e.g., modifying ‘alert.score’ within a ‘ SetTotalScore’ operation) is not a supported syntax for how score modifications are defined in the UI for additive/multiplicative/set total. You’d typically use separate rules for different conditions and their associated actions.

NO.232

Option A

Option B

Option C

Option D

Option E

NO.233 A company is planning to integrate XSIAM with its highly customized CMDB, which runs on a legacy database system without a modern API. The CMDB contains critical asset metadata (e.g., owner, criticality, patching status) that XSIAM needs for accurate alert context and prioritization. Given the constraints, what is the most effective and maintainable integration strategy?

Develop a custom ETL process that periodically extracts data from the legacy CMDB, transforms it, and loads it into a format ingestible by a XSIAM Data Collector (e.g., JSON, CSV over SFTP).

Implement direct database connectivity from a XSIAM Data Collector to the legacy CMDB, ensuring proper firewall rules and credentials.

Require the CMDB vendor to develop a modern API for XSIAM integration.

Manually update XSIAM lookup lists with CMDB data on a daily basis.

Use a generic syslog forwarder to send raw database logs to XSIAM.

NO.234 A new XSIAM automation workflow is being planned to periodically synchronize user identity information from an external HR system (via SCIM API) with XSIAM’s identity store to ensure accurate user context for investigations. During the planning, it’s identified that the HR system’s SCIM implementation has a rate limit of 100 requests per minute and that XSIAM will be performing frequent updates. What is a critical design consideration to prevent service degradation and ensure successful synchronization?

Configure the XSIAM automation to run once daily, regardless of data volume.

Implement an exponential backoff mechanism and retry logic within the XSIAM playbook’s SCIM actions.

Increase the XSIAM data retention period to store more historical identity data.

Disable XSIAM’s threat detection rules during the synchronization window.

Perform the synchronization manually during off-peak hours.

NO.235 The following string is a value of a key named “Data2” in the context:
{“@admin”:”admin”,”@dirtyld”:”1″,”@loc”:”Lab”,”@name”:”default#1″,”@oldname”:”Test”,”@time”:”2024/08/28 07:45:15″,”alert”:{“@admin”:”admin”,”@dirtyld”:”2″,”@time”:”2024/08/28 07:45:15″,”member”:
{“#text”:”
Based on the image below, what will be displayed in the “Test result” field when the “Test” button is pressed?

1

“1

2

“2

NO.236 A critical infrastructure organization is deploying Palo Alto Networks XSIAM in an air-gapped environment with no internet connectivity. This mandates that all software updates, threat intelligence feeds, and content packs must be delivered offline. From a hardware perspective, what unique requirements arise, and what solution would be most effective?

Provisioning a dedicated, physically isolated server to act as an internal update proxy, which is manually updated via USB drives and distributes content to XSIAM nodes.

Implementing a secure, high-capacity portable storage device (e.g., hardened SSDs) for periodic manual transfer of large update files and threat intelligence to the air-gapped network.

Designing the XSIAM cluster with redundant power supplies and network interfaces, as air-gapped environments are inherently more prone to hardware failures due to limited access.

Utilizing specialized ‘ruggedized’ server hardware designed for harsh environments, as air-gapped data centers often lack standard climate control.

Configuring a one-way data diode to securely transfer update packages from a connected network segment into the air-gapped XSIAM environment.

NO.237 During the XSIAM deployment planning, the security team identifies that their existing identity provider (IdP), Okta, is used for SSO across multiple critical applications. To optimize user context within XSIAM and enable identity-based threat detection, what specific type of integration with Okta should be prioritized?

Integration for user provisioning (SCIM) to automatically create and manage XSIAM user accounts.

SSO integration (SAML/OIDC) to allow security analysts to log into XSIAM using their Okta credentials.

API integration to ingest Okta’s system logs (e.g., authentication attempts, user lifecycle events) into XSIAM.

Integrating Okta’s Universal Directory as an external lookup source for user attributes during alert enrichment.

Only integrate if Okta can forward data via syslog.