{"course":{"productid":35952,"modality":1,"active":true,"language":"en","title":"Vertex AI and Generative AI Security","productcode":"VAIGAS","vendorcode":"GO","vendorname":"Google","fullproductcode":"GO-VAIGAS","courseware":{"has_ekit":false,"has_printkit":true,"language":""},"url":"https:\/\/portal.flane.ch\/course\/google-vaigas","objective":"<ul>\n<li>Establish foundational knowledge of Vertex AI and its security challenges.<\/li><li>Implement identity and access control measures to restrict access to Vertex AI resources.<\/li><li>Configure encryption strategies and protect sensitive information.<\/li><li>Enable logging, monitoring, and alerting for real-time security oversight of Vertex AI operations.<\/li><li>Identify and mitigate unique security threats associated with generative AI.<\/li><li>Apply testing techniques to validate and secure generative AI model responses.<\/li><li>Implement best practices for securing data sources and responses within Retrieval-Augmented Generation (RAG) systems.<\/li><li>Establish foundational knowledge of AI Safety.<\/li><\/ul>","essentials":"<p>Fundamental knowledge of machine learning, in particular generative AI, and basic understanding of security on Google Cloud.<\/p>","audience":"<p>AI practitioners, security professionals, and cloud architects<\/p>","outline":"<h4>Module 01 - Introduction to Vertex AI Security Principles<\/h4><p>\n<strong>Topics<\/strong>\n<\/p>\n<ul>\n<li>Google Cloud Security<\/li><li>Vertex AI components<\/li><li>Vertex AI Security concerns<\/li><\/ul><p>\n<strong>Objectives<\/strong>\n<\/p>\n<ul>\n<li>Review Google Cloud Security fundamentals.<\/li><li>Establish a foundational understanding of Vertex AI.<\/li><li>Enumerate the security concerns related to Vertex AI features and components.<\/li><\/ul><p>\n<strong>Activities<\/strong>\n<\/p>\n<ul>\n<li>Lab: Vertex AI: Training and Serving a Custom Model<\/li><\/ul>\n<h4>Module 02 - Identity and Access Management (IAM) in Vertex AI<\/h4><p>\n<strong>Topics<\/strong>\n<\/p>\n<ul>\n<li>Overview of IAM in Google Cloud<\/li><\/ul><p>\n<strong>Objectives<\/strong>\n<\/p>\n<ul>\n<li>Control access with Identity Access Management.<\/li><li>Simplify permission using organization hierarchies and policies.<\/li><li>Use service accounts for least privileged access.<\/li><\/ul><p>\n<strong>Activities<\/strong>\n<\/p>\n<ul>\n<li>Lab: Service Accounts and Roles: Fundamentals<\/li><\/ul>\n<h4>Module 03 - Data Security and Privacy<\/h4><p>\n<strong>Topics<\/strong>\n<\/p>\n<ul>\n<li>Data encryption<\/li><li>Protecting Sensitive Data<\/li><li>VPC Service Controls<\/li><li>Disaster recovery planning<\/li><\/ul><p>\n<strong>Objectives<\/strong>\n<\/p>\n<ul>\n<li>Configure encryption at rest and in-transit.<\/li><li>Encrypt data using customer-managed encryption keys.<\/li><li>Protect sensitive data using the Data Loss Prevention service.<\/li><li>Prevent exfiltration of data using VPC Service Controls.<\/li><li>Architect systems with disaster recovery in mind.<\/li><\/ul><p>\n<strong>Activities<\/strong>\n<\/p>\n<ul>\n<li>Lab: Getting Started with Cloud KMS<\/li><li>Lab: Creating a De-identified Copy of Data in Cloud Storage<\/li><\/ul><h4>Module 04 - Securing Vertex AI Endpoints and model deployment<\/h4><p>\n<strong>Topics<\/strong>\n<\/p>\n<ul>\n<li>Network security<\/li><li>Securing model endpoints<\/li><\/ul><p>\n<strong>Objectives<\/strong>\n<\/p>\n<ul>\n<li>Deploy ML models using model endpoints.<\/li><li>Secure model endpoints.<\/li><\/ul><p>\n<strong>Activities<\/strong>\n<\/p>\n<ul>\n<li>Lab: Configuring Private Google Access and Cloud NAT<\/li><\/ul><h4>Module 05 - Monitoring and logging in Vertex AI<\/h4><p>\n<strong>Topics<\/strong>\n<\/p>\n<ul>\n<li>Logging<\/li><li>Monitoring<\/li><\/ul><p>\n<strong>Objectives<\/strong>\n<\/p>\n<ul>\n<li>Write to and analyze logs.<\/li><li>Set up monitoring and alerting.<\/li><\/ul><h4>Module 06 - Security risks in generative AI applications<\/h4><p>\n<strong>Topics<\/strong>\n<\/p>\n<ul>\n<li>Overview of gen AI security risks<\/li><li>Overview of AI Safety<\/li><li>Prompt security<\/li><li>LLM safeguards<\/li><\/ul><p>\n<strong>Objectives<\/strong>\n<\/p>\n<ul>\n<li>Identify security risks specific to LLMs and gen AI applications.<\/li><li>Understand methods for mitigating prompt hacking and injection attacks.<\/li><li>Explore the fundamentals of securing generative AI models and applications.<\/li><li>Introduce fundamentals of AI Safety.<\/li><\/ul><p>\n<strong>Activities<\/strong>\n<\/p>\n<ul>\n<li>Lab: Safeguarding with Vertex AI Gemini API<\/li><li>Lab: Gen AI &amp; LLM Security for Developers<\/li><\/ul><h4>Module 07 - Testing and evaluating generative AI model responses<\/h4><p>\n<strong>Topics<\/strong>\n<\/p>\n<ul>\n<li>Testing generative AI model responses.<\/li><li>Evaluating model responses.<\/li><li>Fine-Tuning LLMs.<\/li><\/ul><p>\n<strong>Objectives<\/strong>\n<\/p>\n<ul>\n<li>Implement best practices for testing model responses.<\/li><li>Apply techniques for improving response security in gen AI applications<\/li><\/ul><p>\n<strong>Activities<\/strong>\n<\/p>\n<ul>\n<li>Lab: Measure Gen AI Performance with the Generative AI Evaluation Service<\/li><li>Lab: Unit Testing Generative AI Applications<\/li><\/ul><h4>Module 08 - Securing Retrieval-Augmented Generation (RAG) systems<\/h4><p>\n<strong>Topics<\/strong>\n<\/p>\n<ul>\n<li>Fundamentals of Retrieval-Augmented Generation<\/li><li>Security in RAG systems<\/li><\/ul><p>\n<strong>Objectives<\/strong>\n<\/p>\n<ul>\n<li>Understand RAG architecture and security implications.<\/li><li>Implement best practices for grounding and securing data sources in RAG systems.<\/li><\/ul><p>\n<strong>Activities<\/strong>\n<\/p>\n<ul>\n<li>Lab: Multimodal Retrieval Augmented Generation (RAG) Using the Vertex AI Gemini API<\/li><li>Lab: Introduction to Function Calling with Gemini<\/li><\/ul>","summary":"<p>This course is designed to empower your organization to fully harness the transformative potential of Google&rsquo;s Vertex AI and generative AI (gen AI) technologies with a strong emphasis on security. Tailored for AI practitioners and security engineers, it provides targeted knowledge and hands-on skills to navigate and adopt AI safely and effectively. Participants will gain practical insights and develop a security-conscious approach, ensuring a secure and responsible integration of gen AI within their organization.<\/p>","objective_plain":"- Establish foundational knowledge of Vertex AI and its security challenges.\n- Implement identity and access control measures to restrict access to Vertex AI resources.\n- Configure encryption strategies and protect sensitive information.\n- Enable logging, monitoring, and alerting for real-time security oversight of Vertex AI operations.\n- Identify and mitigate unique security threats associated with generative AI.\n- Apply testing techniques to validate and secure generative AI model responses.\n- Implement best practices for securing data sources and responses within Retrieval-Augmented Generation (RAG) systems.\n- Establish foundational knowledge of AI Safety.","essentials_plain":"Fundamental knowledge of machine learning, in particular generative AI, and basic understanding of security on Google Cloud.","audience_plain":"AI practitioners, security professionals, and cloud architects","outline_plain":"Module 01 - Introduction to Vertex AI Security Principles\n\n\nTopics\n\n\n\n- Google Cloud Security\n- Vertex AI components\n- Vertex AI Security concerns\n\nObjectives\n\n\n\n- Review Google Cloud Security fundamentals.\n- Establish a foundational understanding of Vertex AI.\n- Enumerate the security concerns related to Vertex AI features and components.\n\nActivities\n\n\n\n- Lab: Vertex AI: Training and Serving a Custom Model\n\nModule 02 - Identity and Access Management (IAM) in Vertex AI\n\n\nTopics\n\n\n\n- Overview of IAM in Google Cloud\n\nObjectives\n\n\n\n- Control access with Identity Access Management.\n- Simplify permission using organization hierarchies and policies.\n- Use service accounts for least privileged access.\n\nActivities\n\n\n\n- Lab: Service Accounts and Roles: Fundamentals\n\nModule 03 - Data Security and Privacy\n\n\nTopics\n\n\n\n- Data encryption\n- Protecting Sensitive Data\n- VPC Service Controls\n- Disaster recovery planning\n\nObjectives\n\n\n\n- Configure encryption at rest and in-transit.\n- Encrypt data using customer-managed encryption keys.\n- Protect sensitive data using the Data Loss Prevention service.\n- Prevent exfiltration of data using VPC Service Controls.\n- Architect systems with disaster recovery in mind.\n\nActivities\n\n\n\n- Lab: Getting Started with Cloud KMS\n- Lab: Creating a De-identified Copy of Data in Cloud Storage\nModule 04 - Securing Vertex AI Endpoints and model deployment\n\n\nTopics\n\n\n\n- Network security\n- Securing model endpoints\n\nObjectives\n\n\n\n- Deploy ML models using model endpoints.\n- Secure model endpoints.\n\nActivities\n\n\n\n- Lab: Configuring Private Google Access and Cloud NAT\nModule 05 - Monitoring and logging in Vertex AI\n\n\nTopics\n\n\n\n- Logging\n- Monitoring\n\nObjectives\n\n\n\n- Write to and analyze logs.\n- Set up monitoring and alerting.\nModule 06 - Security risks in generative AI applications\n\n\nTopics\n\n\n\n- Overview of gen AI security risks\n- Overview of AI Safety\n- Prompt security\n- LLM safeguards\n\nObjectives\n\n\n\n- Identify security risks specific to LLMs and gen AI applications.\n- Understand methods for mitigating prompt hacking and injection attacks.\n- Explore the fundamentals of securing generative AI models and applications.\n- Introduce fundamentals of AI Safety.\n\nActivities\n\n\n\n- Lab: Safeguarding with Vertex AI Gemini API\n- Lab: Gen AI & LLM Security for Developers\nModule 07 - Testing and evaluating generative AI model responses\n\n\nTopics\n\n\n\n- Testing generative AI model responses.\n- Evaluating model responses.\n- Fine-Tuning LLMs.\n\nObjectives\n\n\n\n- Implement best practices for testing model responses.\n- Apply techniques for improving response security in gen AI applications\n\nActivities\n\n\n\n- Lab: Measure Gen AI Performance with the Generative AI Evaluation Service\n- Lab: Unit Testing Generative AI Applications\nModule 08 - Securing Retrieval-Augmented Generation (RAG) systems\n\n\nTopics\n\n\n\n- Fundamentals of Retrieval-Augmented Generation\n- Security in RAG systems\n\nObjectives\n\n\n\n- Understand RAG architecture and security implications.\n- Implement best practices for grounding and securing data sources in RAG systems.\n\nActivities\n\n\n\n- Lab: Multimodal Retrieval Augmented Generation (RAG) Using the Vertex AI Gemini API\n- Lab: Introduction to Function Calling with Gemini","summary_plain":"This course is designed to empower your organization to fully harness the transformative potential of Google\u2019s Vertex AI and generative AI (gen AI) technologies with a strong emphasis on security. Tailored for AI practitioners and security engineers, it provides targeted knowledge and hands-on skills to navigate and adopt AI safely and effectively. Participants will gain practical insights and develop a security-conscious approach, ensuring a secure and responsible integration of gen AI within their organization.","skill_level":"Expert","version":"1.0","duration":{"unit":"d","value":2,"formatted":"2 days"},"pricelist":{"List Price":{"US":{"country":"US","currency":"USD","taxrate":null,"price":1495},"CA":{"country":"CA","currency":"CAD","taxrate":null,"price":2065},"DE":{"country":"DE","currency":"EUR","taxrate":19,"price":1500},"GB":{"country":"GB","currency":"GBP","taxrate":20,"price":1320},"AT":{"country":"AT","currency":"EUR","taxrate":20,"price":1500},"SE":{"country":"SE","currency":"EUR","taxrate":25,"price":1500},"SI":{"country":"SI","currency":"EUR","taxrate":20,"price":1500},"FR":{"country":"FR","currency":"EUR","taxrate":19.6,"price":1550},"IT":{"country":"IT","currency":"EUR","taxrate":20,"price":1300},"CH":{"country":"CH","currency":"CHF","taxrate":8.1,"price":1500}}},"lastchanged":"2025-09-30T16:01:01+02:00","parenturl":"https:\/\/portal.flane.ch\/swisscom\/en\/json-courses","nexturl_course_schedule":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course-schedule\/35952","source_lang":"en","source":"https:\/\/portal.flane.ch\/swisscom\/en\/json-course\/google-vaigas"}}