What challenges did Palo Alto Networks face before AI implementation?

Palo Alto Networks needed to boost software developer productivity and code quality while ensuring strict security compliance across its complex codebases. Traditional onboarding and code review processes were time-consuming and resource-intensive, limiting developer efficiency. There was a need for scalable AI-driven tools integrated into the development lifecycle to accelerate software delivery with high quality and security.

Source

GCPProductionEvidence: Medium65/100

Palo Alto Networks Enhances Developer Productivity with Claude Models on Google Cloud Vertex AI

Use case typeCode assistantUpdated Jun 13, 2026

Palo Alto Networks deployed Anthropic's Claude large language models on Google Cloud's Vertex AI to enhance developer productivity and code quality while maintaining security compliance. Claude models are integrated into IDEs to assist with pair programming, code completion, and post-processing code reviews, accelerating feature development and improving unit test generation. The solution enabled faster onboarding of junior developers from months to weeks and helped senior developers debug code and brainstorm architectural solutions. The deployment has scaled up to 3,000 developers with measurable improvements in speed, security, and code quality, and ongoing efforts to leverage AI for automated code enhancement and security vulnerability patches.

Organization: Palo Alto Networks
Industry: Tech & Comms
Location: United States

Reported outcomes

Quality: +10–30%

Quality & accuracy

Catalog median for quality & accuracy deployments: +40% across 55 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Time: 20-30% decrease

Google Cloud Customer StoriesCustomer storyInferred claimMedium evidence strength

Achieved 20% to 30% faster feature development and code implementation.

Normalized claim

Quantified impact: 10-30% increase

Google Cloud Customer StoriesCustomer storyInferred claimMedium evidence strength

Improved unit test generation by 10% to 30%, reducing bugs and enhancing software quality.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Palo Alto Networks
Provider: GCP
Maturity: Production
Linked source: Google Cloud Customer Stories

Palo Alto Networks deployed Anthropic's Claude large language models on Google Cloud's Vertex AI to enhance developer productivity and code quality while maintaining security compliance

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1AI-assisted software development
2Code completion and review
3Developer productivity enhancement

Palo Alto Networks needed to boost software developer productivity and code quality while ensuring strict security compliance across its complex codebases.
Traditional onboarding and code review processes were time-consuming and resource-intensive, limiting developer efficiency.
There was a need for scalable AI-driven tools integrated into the development lifecycle to accelerate software delivery with high quality and security.

Deployed Anthropic Claude models on Google Cloud Vertex AI with secure and performant infrastructure ensuring compliance policies and consistent availability.
Integrated Claude models via IDE plugins provided by Sourcegraph for seamless developer interaction.
Used Claude 3.5 Haiku for code completion and Claude 3.5 Sonnet for pair programming to assist with bug fixes, code improvements, and architectural brainstorming.
Implemented AI-driven post-processing of code including variable renaming, comment generation, unit test creation, and security vulnerability identification and patching.

Technologies

Vertex AI Anthropic Claude models Claude 3.5 Sonnet Claude 3.5 Haiku

Improved developer productivity and accelerated secure software delivery.

Implementation partners1

Anthropic

Sources & evidence1

Evidence: Medium65/100Evidence strength