Browse past weeks of engineering reads.
Controlling and restricting access to Google Cloud resources while maintaining security through the Principle of Least Privilege.
Protecting proprietary AI models and applications deployed at enterprise scale on Kubernetes while defending against novel threats like prompt injection and maintaining regulatory compliance without impeding developer velocity.
Network administrators needed deep, programmable control over how BGP routes are evaluated and propagated without requiring expensive third-party virtual appliances.
How to safely execute AI-generated code or untrusted binaries in production without risking host application security, data integrity, or cloud credential exposure.
Developers needed a secure way to connect external AI agents built in tools like Antigravity CLI to resources within their Google Cloud environment without managing infrastructure.
Organizations need a secure, centralized way to manage Claude API access across multiple developers without distributing individual cloud credentials.
Developers needed a quick way to deploy AI prototype applications to production without managing complex cloud infrastructure configuration.
Securing generative AI models and deployments in production environments while maintaining usability for enterprise customers in regulated industries like telecommunications.
Enterprises need to integrate unstructured data from Google Cloud Storage into AI agent systems while maintaining security, standardization, and efficient context retrieval at scale.
Organizations need to securely build, deploy, and govern autonomous AI agents at enterprise scale as the industry transitions from experimental LLMs to production agentic AI systems.
Deploying and managing AI agents at scale in production requires infrastructure for state management, security governance, and complex workflow orchestration that goes beyond demo implementations.
Building safe, reliable, and autonomous agents that can act independently across multiple enterprise systems while maintaining security, governance, and reliability guardrails.
Organizations need to secure their AI systems and infrastructure against emerging AI-era threats while maintaining the ability to leverage AI's potential at scale.
Developers using Google's AI APIs (Gemini and Google APIs) are exposing their API keys to unauthorized access, leading to account compromise, token theft, and service abuse.
Developers needed a unified, secure way to build AI agents locally and deploy them to Google Cloud with standardized protocols and tooling.
Enabling seamless connectivity, governance, and security across multi-agent AI systems and core applications distributed globally at planet scale.