# cloud-ai-security
10 articlestagged with “cloud-ai-security”
AWS Bedrock Security Deep Dive
Advanced security assessment of AWS Bedrock covering model invocation controls, guardrails bypass testing, VPC configurations, and red team methodologies for foundation model APIs.
Hardening Azure OpenAI Service
Comprehensive hardening guide for Azure OpenAI Service covering network isolation, content filtering, managed identity configuration, and threat detection for GPT and DALL-E deployments.
Cost Security and Budget Controls for Cloud AI
Protecting cloud AI deployments from cost-based attacks including denial-of-wallet, token exhaustion, and auto-scaling abuse with budget controls across AWS, Azure, and GCP.
Data Residency and Sovereignty for Cloud AI
Managing data residency, sovereignty, and cross-border transfer requirements for cloud AI services including GDPR, AI Act, and regional model deployment strategies.
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.
Logging and Monitoring for Cloud AI Services
Implementing comprehensive logging and monitoring for cloud AI services including prompt/response capture, anomaly detection, and security-focused observability across AWS, Azure, and GCP.
Security Risks of Cloud AI Model Marketplaces
Assessing security risks in cloud AI model marketplaces including AWS Bedrock Model Garden, Azure AI Model Catalog, GCP Vertex AI Model Garden, and Hugging Face Hub, covering supply chain attacks, trojan models, and verification gaps.
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.
Multi-Cloud AI Security Strategy
Designing and implementing a unified security strategy for organizations using AI services across AWS, Azure, and GCP, covering policy normalization, centralized monitoring, and cross-cloud incident response.
Private Endpoint Configuration for AI Services
Configuring and validating private endpoints for cloud AI services across AWS, Azure, and GCP to eliminate public internet exposure and enforce network-level access controls.