AI Protection overview

AI Protection helps you manage the security posture of your AI workloads by detecting threats and helping you to mitigate risks to your AI asset inventory. This document provides a general overview of AI Protection, including its benefits and several key concepts. AI Protection is available with organization-level activations of Security Command Center.

For the Premium and Enterprise service tiers, when Security Command Center is activated at the organization level, AI Protection helps provide a comprehensive view of AI security across your entire Google Cloud environment. The AI Protection dashboard within the Google Cloud console displays a consistent set of widgets and features, with data aggregated from all projects and resources within the organization.

Capabilities of AI Protection

AI Protection helps you manage threats and risks to your AI systems in the following ways:

  • Organization-wide AI asset inventory: Helps you discover, assess, and manage your AI systems and assets across all projects, including models, datasets, endpoints, Vertex AI, Cloud Storage, and BigQuery.
  • Integrated risk and vulnerability management: Helps you identify, analyze, and manage AI-specific vulnerabilities, misconfigurations, threats, and risks to your AI assets with full organizational context.
  • Compliance support: Helps you verify adherence to relevant security standards using the detective controls in the AI Protection framework.
  • Data security visibility: Integrates with services like Data Security Posture Management to provide insights into data sensitivity, lineage, and risks associated with your AI workloads.
  • Unified AI security management: Assists in monitoring and applying AI security policies and best practices consistently across your organization from a single, centralized dashboard. AI Protection also helps you detect and respond to potential threats, vulnerabilities, and misconfigurations.
  • Risk awareness support: Contributes to minimizing the financial, reputational, and legal risks associated with security breaches and regulatory noncompliance in your AI deployments.

Use cases for AI Protection

AI Protection helps organizations enhance their security by identifying and mitigating threats and risks related to AI systems and sensitive data. The following use cases are examples of how AI Protection can be used in different organizations:

  • Financial services institution: customer financial data

    A large financial services institution uses AI models that process sensitive financial data.

    • Challenge: Processing highly sensitive financial data with AI models entails several risks, including the risk of data breaches, data exfiltration during training or inference, and vulnerabilities in the underlying AI infrastructure.
    • Use case: AI Protection continuously monitors AI workflows for suspicious activity, works to detect unauthorized data access and anomalous model behavior, performs sensitive data classification, and aids in improving your compliance with regulations such as PCI DSS and GDPR.

  • Healthcare provider: patient privacy and compliance

    A major healthcare provider manages electronic health records and uses AI for diagnostics and treatment planning, dealing with Protected Health Information (PHI).

    • Challenge: PHI analyzed by AI models is subject to strict regulations like HIPAA. Risks include accidental PHI exposure through misconfigurations or malicious attacks that target AI systems for patient data.
    • Use case: AI Protection identifies and alerts on potential HIPAA violations, detects unauthorized PHI access by models or users, flags vulnerable and potentially misconfigured AI services, and monitors for data leakage.

  • Manufacturing and robotics company: proprietary intellectual property

    A manufacturing company specializing in advanced robotics and automation relies heavily on AI for optimizing production lines and robotic control, with vital intellectual property (IP) embedded within its AI algorithms and manufacturing data.

    • Challenge: Proprietary AI algorithms and sensitive operational data are vulnerable to theft from insider threats or external adversaries, potentially leading to competitive disadvantage or operational disruption.
    • Use case: AI Protection monitors for unauthorized access to AI models and code repositories, detects attempts to exfiltrate trained models and unusual data access patterns, and flags vulnerabilities in AI development environments to prevent IP theft.

Event Threat Detection rules for Vertex AI assets

The following Event Threat Detection rules run detections on Vertex AI assets:

  • Persistence: New AI API Method
  • Persistence: New Geography for AI Service
  • Privilege Escalation: Anomalous Impersonation of Service Account for AI Admin Activity
  • Privilege Escalation: Anomalous Service Account Impersonator for AI Data Access
  • Privilege Escalation: Anomalous Multistep Service Account Delegation for AI Admin Activity
  • Privilege Escalation: Anomalous Multistep Service Account Delegation for AI Data Access
  • Privilege Escalation: Anomalous Service Account Impersonator for AI Admin Activity
  • Initial Access: Dormant Service Account Activity in AI Service

For more information about Event Threat Detection, see Event Threat Detection overview.

AI Protection framework

AI Protection uses a framework that includes specific cloud controls that are deployed automatically in detective mode. Detective mode means that the cloud control is applied to the defined resources for monitoring purposes. Any violations are detected and alerts are generated. You use frameworks and cloud controls to define your AI Protection requirements and apply those requirements to your Google Cloud environment. AI Protection includes the Default framework, which defines recommended baseline controls for AI Protection. When you enable AI Protection, the default framework is automatically applied to the Google Cloud organization in detective mode.

If required, you can make copies of the framework to create custom AI Protection frameworks. You can add the cloud controls to your custom frameworks and apply the custom frameworks to the organization, folders, or projects. For example, you can create custom frameworks that apply specific jurisdictional controls to specific folders to ensure that data within those folders stays within a particular geographical region.

Cloud controls in the default AI Protection framework

For more information about the cloud controls that AI Protection framework uses, see Google Recommended AI Essentials - Vertex AI.

Supported functional areas for AI Protection

This section defines functional areas that AI Protection can help secure.

  • AI workloads: AI application workloads range from internal tools aimed at improving employee productivity to consumer-facing solutions designed to enhance the user experience and drive business. Examples include AI agents, virtual assistants, conversational AI chatbots, and personalized recommendations.
  • AI agents: AI agents are AI systems that can perceive their environment, make decisions, and take actions to achieve specific goals.
  • AI models: AI models are classified into foundation AI models, fine-tuned AI models, standard first-party AI models, and custom AI models. Examples include Gemini, Llama, translation models, and custom models for specific tasks.
  • AI assets: AI assets contribute to machine learning operation pipelines and are used by AI workloads. Types of AI assets include the following:
    • Declarative AI assets: AI lifecycle management tools, such as Vertex AI, track these assets.
    • Inferred AI assets: General-purpose assets, such as compute and storage assets, used to process AI data or workloads.
    • Model-as-a-Service (API only): Assets that have programmatic calls into first-party or third-party AI models.

Use the AI Security dashboard

The AI Security dashboard lets you visualize your organization's AI asset inventory and review proposed mitigations for risks and threats.

Access the AI Security dashboard

To access the AI Security dashboard, go to the Risk overview > AI security page in the Google Cloud console:

Premium

Go to AI security

Enterprise

Go to AI security

For more information, see AI Security dashboard.

Understand risk management for AI systems

This section provides information about potential risks that are associated with AI systems. You can view the top risks in your AI inventory.

You can click any issue to open a details pane that provides a visualization of the issue.

View AI threats

This section provides insights into threats associated with AI systems. You can view the top 5 recent threats associated with your AI resources.

On this page, you can do the following:

  • Click View all to see threats that are associated with your AI resources.
  • Click any threat to see further details about the threat.

Visualize your AI inventory

You can view a visualization of your AI inventory on the dashboard that provides a summary of the projects that involve generative AI, the first-party and third-party models in active use, and the datasets that are used in training the third-party models.

On this page, you can do the following:

  • To view the inventory details page, click any of the nodes in the visualization.
  • To view a detailed listing of individual assets (such as foundational models and custom-built models), click the tooltip.
  • To open a detailed view of the model, click the model. This view displays details such as the endpoints where the model is hosted and the dataset used to train the model. If Sensitive Data Protection is enabled, the datasets view also displays whether the dataset contains any sensitive data.

Review AI framework findings summary

This section helps you assess and manage findings from the AI framework and data security policies, and includes the following:

  • Findings: This section displays a summary of findings generated by AI security policies and data security policies. Click View all findings or click the count against each finding category to view details about the finding. Click a finding to display additional information about that finding.
  • Sensitive data in Vertex AI datasets: This section displays a summary of the findings based on sensitive data in datasets as reported by Sensitive Data Protection. For more information, see Introduction to Vertex AI.

Examine Model Armor findings

A graph shows the total number of prompts or responses scanned by Model Armor and the number of issues that Model Armor detected. In addition, it displays summary statistics for various types of issues detected, such as prompt injection, jailbreak detection, and sensitive data detection.

This information is populated based on the metrics that Model Armor publishes to Cloud Monitoring. For more information, see Model Armor overview.

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