Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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Configure microsegmentation policies to enforce least-privilege workload-to-workload access using tools like VMware NSX, Illumio, and Calico, preventing lateral movement in zero trust architectures.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Microsegmentation divides a network into granular security zones, enforcing least-privilege access between workloads at the application layer rather than relying on traditional VLAN-based segmentation. In a zero trust architecture, microsegmentation eliminates implicit trust between workloads within the same network segment, preventing lateral movement even after an attacker gains initial access.
This skill covers designing microsegmentation policies using workload identity, implementing host-based and network-based enforcement, and validating segmentation effectiveness with tools like Illumio Core and VMware NSX.
Traditional Segmentation Microsegmentation
┌─────────────────┐ ┌──────────────────────┐
│ VLAN 10 │ │ Workload A ←policy→ │
│ ┌───┐ ┌───┐ │ │ Workload B ←policy→ │
│ │ A │ │ B │ │ │ Workload C ←policy→ │
│ └───┘ └───┘ │ │ Workload D ←policy→ │
│ (trust each │ │ (zero trust between │
│ other) │ │ every pair) │
└─────────────────┘ └──────────────────────┘
Before creating segmentation policies, discover actual communication flows between workloads using traffic telemetry. Tools like Illumio, Guardicore, and AppDynamics provide application dependency maps showing which workloads communicate, over which ports, and how frequently.
Draft policies in monitor/visibility mode before enforcement. This allows validation that proposed rules will not break legitimate traffic while identifying unnecessary or risky communication paths.
Modern microsegmentation uses labels (role, application, environment, location) instead of IP-based rules. Label-based policies are portable across environments and survive IP changes during migrations.
Isolate critical applications (PCI cardholder data environment, SWIFT financial systems, healthcare PHI) with strict allow-list policies that deny all traffic not explicitly permitted.
Deploy Visibility Agents
Build Application Dependency Map
Assign Labels
Define Segmentation Zones
Create Allow-List Policies
Model Policies in Test Mode
Enforce Incrementally
Validate Segmentation
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Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
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Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
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Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
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Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
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Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
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