Professional Cloud Security Engineer

Securing AI Workloads

Implementing Security and Privacy Controls for AI/ML Systems

When building AI and machine learning systems, teams must protect both the data that feeds the models and the models themselves from accidental or intentional misuse. Training data often contains sensitive information, so controls like data anonymization, differential privacy, and strict access controls prevent the model from revealing private details about the people or organizations in the training set. Input validation protects the running model from adversarial inputs designed to trick it into making mistakes, while output filtering prevents the model from sharing harmful or confidential information. The key relationships are: data enters the system, the model processes it, and output leaves the system—each step needs its own security layer.

Determining Security Requirements for IaaS-Hosted and PaaS-Hosted Training Models

Where the training happens changes who manages which security controls. With IaaS (Infrastructure as a Service), the team rents computing resources like virtual machines and storage but must configure the operating system, network, and access management themselves—meaning they are responsible for patching, firewall rules, and identity management. With PaaS (Platform as a Service), the cloud provider manages the underlying infrastructure, and the team focuses on securing their model code, data, and access policies while relying on the provider for foundational security. The choice affects who owns the trust boundary: in IaaS the team draws that line themselves, while in PaaS the provider defines the secure defaults. Both paths require encryption of training data at rest and in transit, but IaaS gives more control over where that encryption happens.

Implementing Security Controls for Gemini Enterprise Agent Platform

The Gemini Enterprise Agent Platform is a managed AI service that lets organizations deploy AI agents for business tasks, and it brings its own set of security considerations. Access to the platform uses Identity and Access Management (IAM) to control who can create, modify, or use agents, and organizations should follow the principle of least privilege—granting only the permissions each user or service needs. Data processed by the platform may flow through Google's infrastructure, so teams must configure data residency options and understand what data Google processes versus what stays under customer control. The platform also integrates with other Google Cloud services, so security depends on properly configuring those connections—each integration point is a boundary that needs its own access controls and monitoring.