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| Section | Objectives |
|---|---|
| Topic 1: Network Security Platform Architecture | - Next-Generation Firewall Deployment
|
| Topic 2: Cloud and Hybrid Security Architecture | - Prisma Browser and Device-ID
|
| Topic 3: IoT and Endpoint Security Architecture | - IoT Security
|
| Topic 4: Third-Party Integration and Automation | - Security Automation
|
| Topic 5: Log Collection and Monitoring Architecture | - Monitoring and Troubleshooting
|
| Topic 6: Zero Trust Network Security Design | - SASE vs Traditional Firewall Edge Solutions
|
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NEW QUESTION # 64
A global organization is in the process of securing critical applications during a cloud-based migration while migrating to a cloud-first design, and it is currently performing a brownfield migration of its most critical applications - such as CRM and product intellectual property / design systems - into Azure Cloud. The organization already has an active/passive high availability (HA) NGFW deployed at its data center with multiple zones and has replicated that design into its existing Azure HA deployment.
The organization recognizes the need to modernize its security posture as critical workloads move out of the data center and users connect from anywhere. Its security model is defined by a traditional "hard shell, soft center" approach:
Zero Trust Gaps
- Current network segmentation is perimeter-based. The organization wants to expand Zero Trust principles across cloud and on-premises environments.
- The network relies heavily on VLANs and IP address-based Access Control Lists (ACLs) segmented primarily by office location and broad departmental groups.
- Once employees are on the corporate network (i.e., inside the "perimeter"), they have relatively wide access.
- If attackers compromise a single endpoint (e.g., via a phishing email), they can easily move laterally and scan for high-value targets.
Cloud Blind Spots
- The organization uses Azure for its production environments and hosts applications that contain sensitive customer data.
- Security controls in the cloud are often managed independently of the on-premises network.
Access is frequently granted with overly permissive identity and access management (IAM) roles and keys based on the resource rather than the user's real-time context or application health.
Remote User Access
- Many remote users are still hairpinning into the corporate data center just to reach internet or SaaS resources, creating latency and inefficiency.
- Traditional VPN is used for remote employees.
- The VPN grants access to the entire internal network segment making the remote endpoint the new, weaker perimeter. There is no continuous check on the user's device health after the initial connection.
Visibility and Logging
- Logs are primarily stored on-premises, then forwarded to a local Security Information and Event Management (SIEM) solution. As applications move to Azure, visibility into cloud traffic and user behavior becomes fragmented.
Data Security Concern
- Sensitive data, including product design files, will now live in SaaS and cloud environments. The organization needs data security to prevent leakage and enforce compliance.
Ingress Security
- Third-party partners and suppliers require access into the data center and cloud applications, introducing risk at ingress points.
The organization needs to ensure data security and prevent the leakage of sensitive product design files since it is migrating to SaaS and cloud environments.
How would implementing a Next-Generation CASB (CASB-X) capability address the concerns in the scenario?
Answer: C
Explanation:
Next-Generation CASB (CASB-X) provides integrated data protection by applying DLP controls to both data-at-rest and data-in-transit within sanctioned SaaS and cloud applications. This enables the organization to identify, monitor, and prevent leakage of sensitive product design files as they move to cloud and SaaS environments, directly addressing the data security concern.
NEW QUESTION # 65
A global manufacturing organization with 50,000 employees spanning 35 countries designs advanced industrial equipment and owns significant intellectual property. The organization operates in a highly competitive market where protecting trade secrets is critical to maintaining market advantage.
Over the past 18 months, the CISO discovered that employees across the organization have adopted hundreds of GenAI applications to improve productivity. Engineers use AI coding assistants to accelerate product development sales teams use AI tools to generate proposals, and customer service representatives use chatbots to draft responses. While this adoption has driven innovation, it has also created significant security risks.
A security audit reveals sensitive CAD files uploaded to image-generation services, proprietary source code shared with public coding assistants, and confidential customer information used in prompts. The audit identifies over 300 different GenAI applications in use, most of which had not been formally reviewed or approved.
The customer service department has also been developing internal AI applications, including a customer service copilot built on a cloud large language model (LLM) platform, an internal knowledge management assistant, and a code review tool. These internal applications access sensitive databases, customer records and internal APIs - creating additional security concerns about exploitation or misuse.
The organization has a distributed workforce in which 60% of employees work remotely or in hybrid arrangements, accessing corporate resources and AI applications from various locations using managed and unmanaged devices. Existing network security infrastructure lacks AI-specific security capabilities.
Organization leadership wants to enable AI-driven innovation while implementing comprehensive security controls. The CISO has been tasked with developing an organization-wide GenAI governance program that protects sensitive assets without hindering productivity. The program must address both external AI applications employees are using and internal AI applications being developed by IT.
Which enforcement solution can the CISO recommend to control GenAI data exfiltration?
Answer: B
Explanation:
AI Access Security is designed to control and govern user interactions with external GenAI applications, including inspecting prompts and responses and applying DLP policies to prevent sensitive data exfiltration. It provides inline enforcement for SaaS-based AI usage across distributed users, which directly addresses the risk of confidential data being exposed through third-party GenAI tools.
NEW QUESTION # 66
You need to ensure consistent threat prevention across all applications. Which approach should you use?
Answer: B
Explanation:
Security Profile Groups allow consistent application of multiple security profiles across policies.
This ensures standardized protection and simplifies management compared to applying profiles individually.
NEW QUESTION # 67
A global manufacturing organization has a strategic plan for rapid growth through mergers and acquisitions Several components the organization has purchased are deemed large deployments with existing IP address schemas and allocations that conflict with the parent organization. The manufacturing organization needs access to the resources before a re-IP initiative can be completed.
All of the deployments include a variety of IoT devices Leadership requires protection of vulnerable assets and identification of any known CVEs associated with the IoT devices. The governance, risk and compliance (GRC) team requires comprehensive non-repudiable logs to identify all IoT devices reporting "Critical (9 0+) CVE scores" for mandatory remediation.
Throughput needs to exceed the current 1 Gbps trending rate, and with expected growth will soon scale to 5 Gbps.
Segmentation is a mandatory requirement with enclaves based on region, device type, and function.
Which architectural component ensures the IoT storage, integrity, and non-repudiation of this granular risk data for auditing purposes?
Answer: C
Explanation:
Strata Logging Service provides centralized, cloud-based log storage with integrity and non- repudiation guarantees, ensuring that IoT telemetry and security logs are preserved for auditing.
It scales to handle high throughput environments and supports long-term retention and analysis, which is required for tracking devices with critical CVE scores across large, distributed deployments.
NEW QUESTION # 68
A retail organization wants to sanction the use of a particular third-party SaaS-based AI application for inventory management. This application will need network layer data access to the organization's internal supply chain database with confidential information highly secured in its own DMZ. The implementation is delayed because the CISO is concerned that the sanctioned third-party AI application could get compromised and then used to exfiltrate customer PH from the internal database. Which solution will address the CISO's concern?
Answer: D
Explanation:
Enterprise DLP integrated with AI Access Security inspects traffic to and from the SaaS application and can detect sensitive data such as customer PII. It enforces policies to prevent exfiltration even if the application is compromised, allowing the organization to safely sanction the AI application while protecting confidential data.
NEW QUESTION # 69
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