#Paloalto

[sullibeaar]

I am her

[yourfavcoquette]

I like to romanticise life by pretending im in a coming of age film

[stakethroughyou]

[khiphop-discussions]

"I'm thirsty because this talent pool is a desert." - "My Team" Paloalto

[russmorris]

Red wagon.

Through the Viewfinder

Pentax *ist D, Sigma 28-135mm ƒ/5.6 Macro + Kodak Duaflex II

[skillcertexams]

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[sullibeaar]

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[govindhtech]

Prisma Cloud Palo Alto: Build AI-Powered Security Solutions

What is Palo Alto Prisma Cloud?

Security and compliance for workloads, applications, and infrastructure in public, private, hybrid, and multi-cloud environments is provided by Prisma Cloud Palo Alto, a full CNAPP. It helps firms reduce risk, improve compliance and security, and collaborate with Dev-Sec.

Google Cloud speeds up generative AI innovation by connecting prototypes to production. Palo Alto Networks and Google Cloud created a cutting-edge security posture control solution that helps users remediate, provides comprehensive risk insights with a few clicks, and provides smart “how-to” answers on demand.

Palo Alto Networks can build and implement new AI-powered solutions leveraging managed Retrieval Augmented Generation (RAG) services like Google Cloud's Vertex AI Search and sophisticated AI services like Google's Gemini models.

The result was Palo Alto Prisma Cloud Co-pilot, a new AI solution. Its user-friendly UI and AI-driven threat detection and reduction make cloud security administration easier.

Technical issues and surprises

The 2023-24 Prisma Cloud Palo Alto Co-pilot project began in October 2024. Palo Alto Networks watched Google's AI models progress swiftly from Text Bison (PaLM) to Gemini Flash 1.5. Due to the speed of innovation, every iteration added new capabilities, hence a development technique that could adapt quickly was needed.

Palo Alto Networks developed proven techniques to navigate the ever-changing landscape of generating AI models:

Quick engineering and management

Through Vertex AI, Palo Alto Networks managed prompt templates and generated a diverse prompt library to deliver various responses. To assess each new model's capabilities, restrictions, and performance across a variety of tasks, the Palo Alto Networks and Google Cloud team painstakingly generated and updated prompts for each submodule. The time-consuming trial-and-error process of prompt engineering was reduced by Vertex AI's Prompt Optimiser.

Recognition of intent

Palo Alto Networks' Gemini Flash 1.5-based intent recognition module guided user queries to the right co-pilot component. This technology offers users a lightweight user interface with many functions.

Guardrails input

Guardrails from Palo Alto Networks protect the chatbot against unexpected, hostile, or incorrect enquiries that might compromise its operation and user experience. These guardrails protect chatbot functionality by restricting its scope and eliminating prompt injection vulnerabilities like system command evasion.

Guardrails were created to limit user queries to generic cloud security, hazards, and vulnerabilities to prevent unintentional use. The chatbot ignored subjects beyond this range. The chatbot was developed to generate proprietary code for Palo Alto Networks systems to query internal systems, therefore requests for general-purpose code were not fulfilled.

Evaluation data curation

A robust and representative evaluation dataset is needed to accurately and quickly evaluate AI models. Palo Alto Networks kept evaluation data current by selecting high-quality data and updated it with representative questions and expert-validated replies. For evaluating dataset quality and reliability, Palo Alto Networks subject matter experts were contacted directly.

Evaluation automation

Palo Alto Systems and Google Cloud automate gen AI assessment with Vertex AI. Palo Alto Networks used this pipeline to expand their review of many generation AI models and benchmark them using unique evaluation criteria, focussing on performance metrics including response consistency, accuracy, and latency.

Red teaming and human evaluator training

Palo Alto Networks trained its human review personnel to identify loss trends and provide extensive replies on a variety of rubrics. This allowed them to identify regions where a model's reactivity was insufficient and provide insightful feedback on model performance, which guided model selection and development.

Load testing

Palo Alto Networks examines current algorithms per Gemini model QPM and latency for real-time performance. They modelled user traffic scenarios using supplied throughput to find the greatest balance between scalability and responsiveness, delivering a flawless user experience even during peak usage.

Business and operational challenges

Information security, legal, and compliance teams may struggle with current AI implementation. New metrics are needed to evaluate evolving AI systems' ROI. Palo Alto Networks addressed these problems using these methods:

Regional ML processing and data residency

Many Palo Alto Networks clients need regional ML processing, thus it prioritised it. This helps clients meet data residency and regional laws.

If Google did not supply an AI data centre that matched Prisma Cloud data centre locations, customers might have their data processed in the US before accessing the Prisma Cloud Co-pilot. They implemented strict data governance rules and used Google Cloud's secure architecture to protect sensitive data and user privacy.

Setting KPIs and assessing gen AI app success

Gen AI applications are dynamic and complex, requiring distinct metrics to analyse their success and capture their unique properties. No single measurement set fits all use scenarios. The Prisma Cloud AI Co-pilot team evaluated system performance using technical and business KPIs.

Technical criteria like recall were used to assess how well the system finds relevant URLs when responding to document requests to enhance speedy responses and provide customers with source information.

Customer helpfulness was measured via telemetry data analysis and explicit feedback. This increased production and decreased costs by improving user experience comprehension.

Working with security and legal

Palo Alto Networks included legal, information security, and other key stakeholders early in the process to identify risks and establish safeguards for information security requirements, dataset bias elimination, tool functionality, and data usage in compliance with law and contract.

Given customer concerns, businesses must prioritise honest data usage, storage, and protection communication. Palo Alto Networks worked with the legal and information security teams early on to give transparent marketing and product communications to earn customer confidence and ensure they understood how and when their data was utilised.

Ready to start Vertex AI?

With careful design and implementation, organisations can maximise the promise of generative AI. Use Vertex AI's pilots to assess your company's AI needs and obtain Google Cloud Consulting's guidance.

[aktieportfoljen]

Bottenpluggen ur på Wall Street, Spiltan och Europeiskt konsumentrelaterat i topp

Efter milen i löparskorna är det skönt att komma hem och sätta sig och summera börsen. Ikväll är Nasdaq på väg mot den sämsta börsdagen sedan 2022 och är i detta nu ned -4,15%.Men först vill jag citera Dagens Industris Mikael Vilenius om röran i USA: -”Det är en ny sheriff i stan under Donald Trump” deklarerade USA:s vice president JD Vance i München den 14 februari. De senaste veckorna har det…