As enterprises deploy AI across cloud environments, concerns around securing data, models, and infrastructure are drawing increased attention. Palo Alto Networks and Google Cloud have expanded their partnership to address AI and cloud security requirements, combining infrastructure, AI services, and security platforms to support enterprise adoption at scale.
The two companies announced a landmark agreement designed to tightly integrate Palo Alto Networks’ AI security platforms with Google Cloud’s AI, networking, and infrastructure services. The goal is to give enterprises a secure foundation for building, deploying, and operating AI-driven applications across hybrid and multicloud environments, while reducing the operational friction that often slows innovation.
The timing of the announcement is not accidental. According to Palo Alto Networks’ most recent State of Cloud Security Report, published in December 2025, enterprises are expanding cloud infrastructure at unprecedented speed to support AI workloads. At the same time, nearly all surveyed organizations – 99 percent – reported at least one successful attack on AI-related infrastructure in the past year. The data underscores a growing reality: AI systems are becoming high-value targets, and traditional cloud security approaches are struggling to keep pace.
The expanded collaboration aims to embed security directly into every phase of AI development and deployment on Google Cloud. At the center of the effort is Prisma AIRS, Palo Alto Networks’ AI security platform, which will now provide protection for AI workloads running on Google Cloud services such as Vertex AI and Agent Engine. The platform is designed to address a wide range of risks, including model vulnerabilities, runtime threats, misconfigurations, and attacks on autonomous AI agents.
Beyond protecting deployed models, the partnership extends into the development lifecycle itself. By integrating Prisma AIRS with Google Cloud’s AI developer tools, including the Agent Development Kit, the companies are positioning security as a built-in component rather than an afterthought. This approach reflects a broader industry shift toward “secure-by-design” AI systems, where visibility, monitoring, and testing are continuous rather than reactive.
The agreement also deepens technical integration between Palo Alto Networks’ cloud firewall technologies and Google Cloud’s infrastructure. The VM-Series software firewalls, which provide threat prevention and deep packet inspection across virtualized and cloud environments, will be more tightly aligned with Google Cloud services. This allows enterprises to apply consistent security policies while accelerating cloud migrations and AI deployments without redesigning their security architecture.
Pre-Engineering and Validating Joint Solutions
On the networking side, Palo Alto Networks’ Prisma SASE platform will further integrate with Google Cloud’s global network. Prisma Access, which runs on Google’s infrastructure, is designed to improve performance for users accessing AI and cloud applications while maintaining consistent security controls. Integration with Google Cloud Interconnect also allows enterprises to extend secure networking across multiple clouds and on-premises environments, a growing requirement as AI workloads span diverse infrastructure.
A key theme of the partnership is simplification. Large enterprises often manage dozens of security tools across cloud, network, and endpoint environments, leading to fragmented visibility and slower incident response. By pre-engineering and validating joint solutions, Palo Alto Networks and Google Cloud aim to reduce integration complexity, accelerate deployment, and give security teams a unified view of risk across hybrid multicloud estates.
Executives from both companies framed the agreement as a response to board-level concerns about AI risk. BJ Jenkins, president of Palo Alto Networks, said organizations are under pressure to harness AI’s potential without introducing new vulnerabilities. He emphasized that the partnership is designed to remove the traditional tension between development speed and security by making advanced protection a native part of the Google Cloud platform.
From Google Cloud’s perspective, the expansion reflects growing demand from customers that want enterprise-grade security tightly aligned with cloud-native AI services. Matt Renner, president and chief revenue officer of Google Cloud, noted that joint customers are increasingly standardizing on both platforms to secure applications, data, and AI infrastructure in a seamless way.
The agreement builds on an already substantial relationship between the two companies. Palo Alto Networks and Google Cloud have completed more than 75 joint integrations and generated over $2 billion in sales through the Google Cloud Marketplace. As part of the new deal, Palo Alto Networks is also deepening its own reliance on Google Cloud by migrating key internal workloads to Google’s infrastructure in a multibillion-dollar commitment. In addition, Palo Alto Networks is using Google Cloud’s Vertex AI platform and Gemini large language models to power its own AI copilots, further aligning the companies’ engineering roadmaps.
For enterprises navigating increasingly complex regulatory, security, and operational environments, the partnership highlights a broader industry trend: AI innovation is no longer just about faster models or larger datasets. It is about building trusted systems that can scale securely, comply with regulations, and withstand a rapidly evolving threat landscape.
Executive Insights FAQ
Why is AI security becoming a top concern for cloud customers?
AI systems process sensitive data, automate decisions, and expose new attack surfaces, making them high-value targets for cybercriminals.
What does embedding security “from code to cloud” mean in practice?
It means protecting AI applications during development, deployment, and runtime, rather than adding security controls only after systems go live.
How does this partnership address hybrid and multicloud complexity?
By integrating security controls across networking, workloads, and endpoints, enabling consistent policies across clouds and on-premises environments.
Why is latency important for AI security services?
High latency can degrade user experience and slow threat detection, especially for real-time AI inference and security inspections.
What does this deal signal about the future of enterprise AI adoption?
It suggests AI growth will increasingly depend on tightly integrated security platforms that scale alongside cloud and infrastructure services.
