# gcp
26 articlestagged with “gcp”
Cloud AI Security Practice Exam 1
Practice exam covering AWS Bedrock, Azure OpenAI, and GCP Vertex AI security assessments.
Advanced Cloud AI Security Assessment
15-question advanced assessment covering cloud AI attack surfaces across AWS, Azure, and GCP: guardrail bypass, knowledge base exploitation, managed identity abuse, model customization risks, and multi-cloud attack paths.
Cloud AI Security Assessment
Test your knowledge of AWS, Azure, and GCP AI service security with 15 intermediate-level questions covering cloud-specific attack surfaces and misconfigurations.
Capstone: Cloud AI Security Assessment
Assess AI deployment security across AWS, Azure, and GCP cloud platforms, producing a comprehensive cloud AI security assessment report.
Cloud ML Platform Security (AWS/Azure/GCP)
Security comparison of cloud ML platforms including AWS SageMaker, Azure Machine Learning, and Google Vertex AI. IAM configuration, data security, model serving, and platform-specific attack surfaces.
IAM Best Practices for Cloud AI Services
Cross-cloud IAM best practices for securing AI services on AWS, Azure, and GCP, covering least privilege, service identity management, cross-account access, and policy automation.
Shared Responsibility Model for Cloud AI Security
Understanding the division of security responsibilities between cloud providers and customers for AI/ML workloads across AWS, Azure, and GCP, with specific guidance for LLM deployments.
GCP IAM for AI Services
IAM exploitation patterns for GCP AI services: service account exploitation, Workload Identity abuse, VPC Service Controls for AI, and privilege escalation through Vertex AI permissions.
GCP AI Services Security Overview
Red team methodology for GCP AI services including Vertex AI, Model Garden, and AI Platform: service enumeration, service account exploitation, and attack surface mapping.
Model Garden Risks
Security risks of deploying models from GCP Model Garden: third-party model trust, model provenance verification, deployment from untrusted sources, and supply chain attack vectors.
Vertex AI Attack Surface
Red team methodology for Vertex AI: prediction endpoint abuse, custom training security gaps, feature store poisoning, model monitoring evasion, and pipeline exploitation.
GCP AI Platform Threat Analysis
Threat analysis of GCP AI platform services including AutoML, custom training, and prediction endpoints.
GCP Model Garden Security
Security assessment of GCP Model Garden including model deployment, versioning, and access control.
GCP Vertex AI Agent Builder Security
Security assessment of Google Vertex AI Agent Builder including grounding, tool use, and safety settings.
GCP Vertex AI Security Assessment
Security assessment methodology for GCP Vertex AI covering IAM bindings, VPC Service Controls, Model Garden risks, and detection strategies for Gemini API abuse.
GCP Vertex AI Security Guide
Security guide for GCP Vertex AI including model garden, endpoints, and Gemini API security.
Cloud AI Security
Comprehensive overview of cloud AI security for red teamers: shared responsibility models, attack surfaces across AWS, Azure, and GCP AI services, threat models for model APIs, data pipelines, and inference endpoints.
Security Controls Comparison Matrix
Side-by-side comparison of AWS, Azure, and GCP AI security controls: IAM patterns, content filtering, guardrails, network isolation, logging, and threat detection across cloud providers.
Cloud AI Infrastructure Attacks
Security assessment of cloud-hosted AI/ML platforms including AWS SageMaker, Azure ML, and GCP Vertex AI -- IAM misconfigurations, model theft, and data exposure.
GCP Vertex AI Attack Surface
Security assessment of Google Cloud Vertex AI -- service account exploitation, endpoint security, notebook attacks, and pipeline manipulation.
Cloud AI Security Cheat Sheet
Quick reference comparing AI security controls across AWS, Azure, and GCP -- covering IAM, networking, encryption, monitoring, and AI-specific services.
GCP Vertex AI Security Testing
End-to-end walkthrough for security testing Vertex AI deployments on Google Cloud: endpoint enumeration, IAM policy analysis, model serving exploitation, pipeline assessment, and Cloud Audit Logs review.
Cloud AI Platform Walkthroughs
Hands-on walkthroughs for red teaming AI systems deployed on major cloud platforms: AWS Bedrock, Azure OpenAI, Google Vertex AI, and Hugging Face Hub.
Vertex AI Red Team Walkthrough
End-to-end walkthrough for red teaming Google Cloud Vertex AI: prediction endpoint testing, Model Garden security assessment, Feature Store probing, and Cloud Logging analysis.
Vertex AI Red Team Walkthrough (Platform Walkthrough)
Complete red team walkthrough for Google Vertex AI: testing prediction endpoints, Model Garden assessments, Feature Store probing, and exploiting Vertex AI Agents and Extensions.
Testing GCP Vertex AI Deployments
Red team testing guide for models deployed via GCP Vertex AI including Model Garden and custom endpoints.