How Palo Alto Networks builds gen-AI solutions

Operational and business challenges 

Operationalizing gen AI can introduce complex challenges across multiple functions, especially for compliance, legal, and information security. Evaluating ROI for gen AI solutions also requires new metrics. To address these challenges, Palo Alto Networks implemented the following techniques and processes:

• Data residency and regional ML processing: Since many Palo Alto Networks customers need a regional approach for ML processing capabilities, we prioritized regional machine learning processing to help enable customer compliance with data residency needs and regional regulations, if applicable.

  Where Google does not offer an AI data center that matched Prisma Cloud data center locations, customers were able to choose having their data processed in the U.S. before gaining access to the Prisma Cloud Co-pilot. We implemented strict data governance policies and used Google Cloud’s secure infrastructure to help safeguard sensitive information and uphold user privacy.

• Deciding KPIs and measuring success for gen AI apps: The dynamic and nuanced nature of gen AI applications demands a bespoke set of metrics tailored to capture its specific characteristics and comprehensively evaluate its efficacy. There are no standard metrics that work for all use cases. The Prisma Cloud AI Co-pilot team relied on technical and business metrics to measure how well the system was operating. 

• Technical metrics, such as recall, helped to measure how thoroughly the system fetches relevant URLs when answering questions from documents, and to help increase the accuracy of prompt responses and provide source information for users. 

• Customer experience metrics, such as measuring helpfulness, relied on explicit feedback and telemetry data analysis. This provided deeper insights into user experience that resulted in increased productivity and cost savings.

• Collaborating with security and legal teams: Palo Alto Networks brought in legal, information security, and other critical stakeholders early in the process to identify risks and create guardrails for issues including, but not limited to: information security requirements, elimination of bias in the dataset, appropriate functionality of the tool, and data usage in compliance with applicable law and contractual obligations. 

Given customer concerns, enterprises must prioritize clear communication around data usage, storage, and protection.  By collaborating  with legal and information security teams early on to create transparency in marketing and product communications, Palo Alto Networks was able to build customer trust and help ensure they have a clear understanding of how and when  their data is being used. 

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