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A SOC analyst is investigating a complex attack involving a custom malware variant. The EDR flagged several suspicious process injections and network connections, but failed to provide full context on the malware's origin, the user account involved, or its lateral movement across the network. The analyst needs to perform a deep forensic analysis and then rapidly contain the threat. Consider the following KQL query an EDR might provide:
Which of the following capabilities of Cortex XDR, beyond this EDR-level query, would significantly aid the SOC analyst in this investigation and response? (Select all that apply)
A. Real-time, signature-based antivirus scanning for every file downloaded to an endpoint.
B. Native Network Traffic Analysis: Cortex XDR's network sensors (e.g., from a Firewall or dedicated NTA) provide detailed network session logs, allowing the analyst to trace lateral movement and C2 communication that an EDR agent might not see.
C. Automated Remediation Playbooks: The ability to trigger automated response actions across multiple security layers (e.g., isolate endpoint, block IP on firewall, disable user account) directly from the Cortex XDR console.
D. Automated Incident Creation and Storyline Correlation: Cortex XDR automatically stitches together related alerts from endpoints, network, cloud, and identity into a single 'incident' with a graphical attack storyline, revealing the full kill chain.
E. Integrated User Behavioral Analytics (UBA): Detection of anomalous user behavior, such as a user account logging in from an unusual location or accessing atypical resources, even if their credentials were stolen.
Answer: B,C,D,E
Explanation:
This question specifically targets the 'X' in XDR and the integrated nature of Cortex XDR. While the EDR query provides endpoint context, it's fragmented. A: Cortex XDR's incident storyline is a core benefit, providing a holistic view of the attack, which an EDR alone cannot achieve. B: Native network traffic analysis is crucial for understanding lateral movement and C2, areas where EDRs have limited visibility. Cortex XDR leverages data from Network Firewalls or dedicated NTA. C: UBA is vital for detecting compromised accounts and insider threats, going beyond just endpoint process analysis. D: Automated remediation across multiple security domains is a key XDR capability for rapid response, whereas EDRs typically offer endpoint-specific isolation. E: While Cortex XDR includes advanced endpoint protection, real-time signature-based AV scanning is a fundamental EDR/EPP function and doesn't represent the 'beyond EDR' capabilities for this complex investigation.
NEW QUESTION # 60
A Security Operations Center (SOC) is onboarding Cortex XSIAM. During the initial sensor deployment phase for a large enterprise network, the team encounters issues with data ingestion from a geographically dispersed set of Windows Server 2019 instances, specifically regarding DNS query logs and process execution details. The network topology includes multiple firewalls, proxies, and a central SIEM that will eventually receive enriched data from XSIAM. Which of the following Cortex XSIAM sensor types are primarily responsible for collecting this type of detailed host-level telemetry, and what common configuration challenges might lead to data ingestion failures in this scenario?
A. Cloud Sensors (e.g., AWS CloudTrail, Azure Activity Logs) are essential for this data, and common challenges include misconfigured IAM roles/service principals or lacking API permissions to access log streams.
B. Network Sensors (e.g., Network Packets, NetFlow) would be the primary choice, and common challenges include firewall port blocking (UDP/4739 for NetFlow) and incorrect NetFlow export configurations.
C. Identity Sensors (e.g., Active Directory, Okta) are responsible, and common challenges include LDAP/SCIM connectivity issues or insufficient service account privileges for directory synchronization.
D. Orchestration Sensors (e.g., SOAR Playbooks) are used for data collection, and common challenges involve incorrectAPl key rotations or misconfigured webhook endpoints preventing automated data pulls.
E. Host Sensors (e.g., Endpoint Agents) are crucial for this data, and common challenges involve Group Policy Objects (GPOs) preventing agent installation, Antivirus/EDR conflicts, or insufficient network connectivity to the Cortex XSIAM Broker.
Answer: E
Explanation:
Host Sensors, specifically the Endpoint Agent (e.g., Cortex XDR agent), are designed to collect detailed host-level telemetry like DNS query logs, process execution details, file activity, and network connections directly from endpoints and servers. Common challenges in their deployment and data ingestion often stem from enterprise-level configurations like GPOs blocking installations, conflicts with existing security software (Antivirus/EDR), or network connectivity issues preventing the agent from reaching the XSIAM Broker or directly to the XSIAM cloud. Options A, C, D, and E describe different sensor types or irrelevant challenges for the specified data collection scenario.
NEW QUESTION # 61
A zero-day vulnerability in a widely used web application is actively being exploited, leading to immediate concern for your organization's internet-facing servers. While vendor patches are not yet available, your Palo Alto Networks NGFW is deployed. Which temporary compensating control, leveraging NGFW capabilities, would offer the best immediate protection against this zero-day exploit without disrupting legitimate traffic or requiring custom signatures?
A. Configure a custom 'Threat Prevention' profile with a 'Vulnerability Protection' rule using a signature specific to the zero-day CVE (if available from threat intelligence), applied to the relevant security policy.
B. Enable 'Strict' application-level security policies using App-lD to only allow known legitimate application traffic to the web server, blocking anything else.
C. Utilize Palo Alto Networks GlobalProtect to enforce host information profile (HIP) checks, ensuring only patched clients can access the web application.
D. Block all inbound HTTP/HTTPS traffic to the affected web application server.
E. Deploy a 'Denial-of-Service (DoS) Protection' policy to rate-limit connections to the web server.
Answer: B
Explanation:
The challenge is a zero-day with no available patches or specific signatures. Blocking all HTTP/HTTPS (A) disrupts legitimate traffic. While custom signatures (C) are ideal, they aren't available for a zero-day without external intelligence quickly providing one. GlobalProtect (D) is for client access, not server protection. DoS protection (E) mitigates DoS, not exploits. The most effective immediate compensating control is App- ID (B). By strictly defining and allowing only the legitimate application traffic (e.g., 'web-browsing' and specific sub-applications) and blocking anything else, the NGFW can often prevent the execution of malicious code or unusual protocols that the zero-day exploit might leverage, even without a specific vulnerability signature. This is a powerful feature for 'positive security model' enforcement.
NEW QUESTION # 62
An internal application developer inadvertently embeds hardcoded credentials within a file (SHA256: f8d7c2e1a9bOc3d4e5f6a7bgc9doe1f2a3b4c5d6e7f8a9bc1d2e3f4a5b6c7d8) that is then committed to a public GitHub repository. This file also contains a URL (https://internal-api.example.com/sensitive_data) pointing to a highly confidential internal API. The security team needs to leverage Cortex products to identify if this file has been processed or accessed internally, prevent external access to the sensitive URL, and ensure the file's exposure is contained. Which specific combination of Cortex capabilities would achieve this with the highest fidelity and automation, considering both file and URL indicator types?
A. Manually create an XDR 'Custom Indicator' for the file hash, then conduct a 'Live Terminal' session on developer machines to search for the file. For the URL, configure a new 'URL Filtering Profile' on the NGFW to block the full URL, and manually distribute this policy.
B. Upload the file to WildFire for analysis. If identified as sensitive, WildFire will automatically block its execution on endpoints. For the URL, rely on the NGFW's 'Data Filtering' profile to prevent exfiltration if the sensitive data passes through the firewall.
C. Create a 'Behavioral Threat Protection' rule in Cortex XDR to detect processes accessing URLs matching the pattern 'internal-api.example.com'. For the file, conduct an 'Investigation' in Cortex XDR starting from the file hash.
D. Configure a 'File Blocking Profile' on the NGFW to prevent the transfer of files with the specific hash over the network. For the URL, instruct the network team to manually configure a 'Deny' rule on the firewall for traffic destined to internal-api.example.com.
E.
Answer: E
Explanation:
Option B provides the most comprehensive, automated, and high-fidelity solution by effectively combining Cortex XSOAR for orchestration with Cortex XDR for endpoint visibility and NGFWs for network control, utilizing both file and URL indicator types. 1. XQL Query for Detection: The XQL query efficiently searches Cortex Data Lake (XDRs backend) for historical and real-time instances of the specific file hash and connections to the exact sensitive URL. This addresses the need to 'identify if this file has been processed or accessed internally'. 2. NGFW URL Blocking: Cortex XSOAR can programmatically interact with the NGFW to add the sensitive URL to a block list (e.g., a custom URL category or an EDL used by a URL Filtering Profile). This immediately 'prevents external access to the sensitive URL' at the network perimeter. 3. XDR File Prevention: XSOAR can update Cortex XDR's prevention policies to block the execution or processing of the specific file hash on endpoints. This ensures 'the file's exposure is contained' at the endpoint level, preventing further internal propagation or execution of the sensitive file. 4. Automated Alerting/lncident Creation: If the XQL query finds matches, XSOAR can automatically create an incident, streamlining the incident response process. Option A is too manual. Option C (WildFire) is for malware analysis and blocking, not typically for sensitive data exposure unless the file is also malicious, and 'Data Filtering' might be reactive. Option D is partly correct for network file blocking but is too manual for the URL and lacks endpoint detection. Option E is more focused on detection and doesn't offer the immediate, programmatic prevention capabilities that B does.
NEW QUESTION # 63
A DevOps team is developing a custom application that utilizes highly unusual but legitimate system calls and network protocols. When deployed, Cortex XDR sensors on the development machines generate numerous high-severity alerts related to 'Suspicious API Usage' and 'Unusual Network Traffic'. The security team needs to fine-tune the sensor's detection logic to allow this legitimate application's behavior while maintaining high fidelity for actual threats. Which of the following Cortex XDR sensor policy adjustments are most appropriate to address this specific challenge?
A. Utilize Behavior Exceptions within the Behavioral Threat Protection policy to define specific allowed behaviors (e.g., specific process, parent process, API calls, network destinations/ports) for the legitimate application, and create Network Allow Rules for the custom protocols, ensuring these exceptions are granular and target only the legitimate application's unique actions.
B. Create a new profile with a lower severity threshold for all BTP and Network Protection detections, then assign it to the development machines.
C. Submit the application's binaries to WildFire for a 'safe' verdict, which will automatically suppress all related alerts.
D. Disable the entire Behavioral Threat Protection (BTP) module and Network Protection module for the development machines.
E. Exclusively whitelist the application's executable hash in the 'Known Good Hashes' list.
Answer: A
Explanation:
This scenario requires nuanced policy tuning. Simply whitelisting hashes (A) won't address the behavioral alerts. Disabling modules (B) is a dangerous oversimplification and removes critical protection. Lowering severity thresholds (C) is a blunt instrument that could mask real threats. Submitting to WildFire (E) is for malware analysis, not for fine-tuning legitimate application behavior. The most appropriate and granular solution is to use Behavior Exceptions within BTP and Network Allow Rules. Behavior Exceptions allow you to define specific allowed patterns of behavior for a given process, preventing alerts for its legitimate actions (e.g., specific API calls it makes that might otherwise be flagged as suspicious). Similarly, Network Allow Rules can be configured for specific custom protocols or destinations used by the application. This ensures that the legitimate, unusual behavior is allowed without broadly compromising the security posture or generating excessive false positives, while still detecting true threats.
NEW QUESTION # 64
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