'iam' 검색 결과

Inspect 3MF files for 3D printing, detect cylindrical holes, and resize them to a chosen diameter. Repair mesh defects and compare files while preserving BambuStudio and PrusaSlicer metadata during file repacking.

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• Finding cylindrical holes and checking their diameters
• Resizing selected holes to a specified diameter
• Repairing mesh defects before 3D printing

Configure Amazon S3 storage with encryption, access restrictions, and version history. Set rules for moving or deleting older files, replicate data across regions, create temporary access links, and investigate permission or policy problems.

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• Create buckets with encryption and public access blocked
• Set rules for archiving or deleting older files
• Replicate data to another region

Detect unauthorized cryptocurrency mining in cloud workloads by combining billing changes, CPU usage, mining pool connections, and process alerts. Follow workflows to preserve evidence, isolate affected resources, and trace how attackers gained access.

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• Investigating sudden cloud billing increases
• Responding to GuardDuty cryptocurrency alerts
• Checking containers for unauthorized mining processes

Configure AWS Verified Access for VPN-free access to internal applications. Combine user identity and device security checks, write Cedar access policies, and use Terraform and AWS RAM to deploy and share access controls across AWS accounts.

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• Replacing VPN access to internal applications
• Writing policies based on users and device security
• Deploying Verified Access with Terraform

Set up decoy credentials, storage, and secrets across AWS, Azure, and GCP to detect suspicious access. Connect audit logs to security alerts, verify detection, and document permissions, resource inventories, and maintenance plans.

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• Detecting use of leaked cloud credentials
• Monitoring suspicious access to decoy storage and secrets
• Extending on-premises deception coverage to cloud environments

Respond to security incidents across AWS, Azure, and GCP by investigating audit logs, containing compromised identities, isolating resources, and preserving evidence. Organize recovery and follow-up security measures for credential leaks, unauthorized permission changes, and attacks spanning cloud services.

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• Responding to leaked cloud credentials
• Investigating unauthorized permission or resource changes
• Preserving audit logs and disk snapshots

Plan and conduct authorized security tests of AWS, Azure, and GCP environments. Check access permissions, credential exposure, cross-account access, and monitoring, then organize findings into a report with severity, business impact, and remediation guidance.

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• Assessing cloud security before production deployment
• Checking access controls after a cloud migration
• Testing monitoring and incident response readiness

Review Google Cloud workloads against the security principles of the Well-Architected Framework. Identify gaps in access control, network protection, data handling, and security operations, then receive prioritized recommendations linked to relevant Google Cloud products.

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• Reviewing security during Google Cloud architecture design
• Checking access permissions and network defenses
• Identifying gaps in data protection and security monitoring

Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.

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• Moving a Gemini application to Google Cloud
• Using Google Cloud credits for Gemini API calls
• Unifying agent authentication and billing

Create and administer AlloyDB for PostgreSQL databases, including clusters, instances, backups, secure connections, and access permissions. It also covers command-line operations, infrastructure configuration, and MCP tools that let agents automate database tasks.

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• Create an AlloyDB cluster and primary instance
• Manage backups and plan recovery
• Configure secure connections and access permissions

Configure Cloud Logging within one Google Cloud project. Set storage locations, retention periods, routing rules, and viewing permissions; create metrics from logs; and adjust stored volume through exclusions or sampling, which retains only a portion of entries.

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• Set regional log storage and retention periods
• Restrict access to sensitive logs
• Count specific errors with logs-based metrics

Configure Django file storage on AWS S3 using django-storages and boto3. Separate public and private files, create temporary download or upload links, configure CloudFront delivery, and test storage behavior without accessing real S3 buckets.

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• Moving Django static files and uploads to S3
• Separating public files from private documents
• Creating temporary download and direct upload links

Assess AWS, Azure, and GCP environments by identifying identities, permissions, and paths to broader access or data exposure. Covers cloud storage, secrets, cross-account trust, and CI/CD, the automated systems used to build and deploy software.

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• Planning an AWS, Azure, or GCP security assessment
• Tracing privilege escalation from current permissions
• Reviewing CI/CD credentials and OIDC trust settings

Emulated AWS cloud services (S3, SQS, Cognito, IAM, STS) for local development and testing. Use when the user needs to interact with AWS API endpoints locally, test S3 bucket and object operations, emulate SQS queues and messages, test Cognito sign-up, sign-in, JWTs, or Identity Pools, manage IAM users/roles/access keys, test STS assume role, or work without hitting real AWS APIs. Triggers include "AWS emulator", "emulate AWS", "mock S3", "local SQS", "test IAM", "emulate S3", "AWS locally", "STS assume role", or any task requiring local AWS service emulation.

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Provides AWS CloudFormation patterns for IAM roles, policies, managed policies, permission boundaries, and trust relationships. Use when modeling least-privilege access, cross-account assumptions, service roles, or reusable IAM stacks that other CloudFormation templates consume.

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Analyze magnetic levitation systems by applying Earnshaw's theorem to determine whether passive static levitation is possible, then identifying the appropriate circumvention mechanism (diamagnetic, superconducting, active feedback, or spin-stabilized). Use when evaluating maglev transport, magnetic bearings, superconducting levitation, diamagnetic suspension, or Levitron-type devices. Covers force balance calculations, stability analysis in all spatial and tilting modes, and Meissner effect versus flux pinning distinctions.

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Manages the end-to-end lifecycle of on-demand, temporary access using Privileged Access Manager (PAM). Use when a user asks to create, read, update, or delete PAM entitlements, request temporary access, or approve/deny pending PAM grants. Do NOT use for permanent IAM policy bindings, troubleshooting IAM permission errors, or general Google Cloud resource provisioning.

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Diagnoses, remediates, and manages Google Cloud Identity and Access Management (IAM) access issues. Supports two distinct operational modes: (1) Requester Flow for developers encountering access denials (capturing error context, self-service PAM JIT activations, elevated developer self-remediation, or logging structured tickets), and (2) Resolver Flow for privileged administrators (authoritative Policy Troubleshooter analysis, deny policy exemptions, least-privilege role discovery, and PAM/IAM provisioning).

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Plans, configures, and hardens platform-level Google Kubernetes Engine (GKE) cluster security. Covers cluster add-ons (Secret Manager enablement), RBAC hardening (disabling insecure bindings, audit tools), Binary Authorization, enabling Shielded Nodes, GKE Sandbox cluster enablement, GKE IAM roles, and cross-service authentication IAM patterns. Use when securing cluster control planes, hardening GKE RBAC, enabling Shielded Nodes, enabling GKE Sandbox runtime, enabling cluster-wide security add-ons, or managing GKE IAM roles. Don't use for workload-level security (Workload Identity, SecretProviderClass, PSS, NetPol, gVisor pod runtimeClassName; use gke-workload-security instead).

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Diagnoses Workload Identity Federation for GKE authentication failures for Pods (403 "iam.serviceAccounts.getAccessToken" / permission denied, "could not find default credentials", or GKE metadata server unreachable) by verifying cluster and node-pool Workload Identity configuration, the Kubernetes ServiceAccount (KSA) to IAM binding (direct principal binding and legacy Google ServiceAccount impersonation), target-resource IAM roles, and gke-metadata-server health. Use when a Pod cannot authenticate to Google Cloud APIs even though Workload Identity is expected to be in effect. Don't use for in-cluster Kubernetes RBAC errors (API-server authorization), general workload crashes (use gke-workload-troubleshooting), or Workload Identity setup and hardening (use gke-workload-security).

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IAM policy review, hardening, and least privilege implementation

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IAM policy review, hardening, and least privilege implementation

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Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.

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Detect AWS IAM privilege escalation paths using boto3 and Cloudsplaining policy analysis to identify overly permissive policies, dangerous permission combinations, and least-privilege violations

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