Secure AI innovation without interruption

According to the 2025 State of AI Security and Governance Report, the top risks cited by enterprises are sensitive data exposure (52%) and regulatory compliance (50%). Other emerging threats include Shadow AI, prompt injection, and model theft. A comprehensive defense requires securing the data, the model, and the user interaction layer simultaneously.

Shadow AI occurs when employees use unsanctioned models or datasets, bypassing IT governance. To detect it, organizations need automated discovery tools. Google Cloud’s Security Command Center provides a real-time inventory of all AI assets—including unauthorized "shadow" models—giving security teams the visibility needed to apply consistent security policies to unauthorized models, reducing risk without disrupting business workflows.

While skill gaps are a common barrier (cited by 53% of organizations), you don’t need to build everything from scratch. Google Cloud embeds AI security directly into its existing platform. Tools like Security Command Center use AI to summarize threats and recommend fixes, helping your existing security team manage AI risks without needing to be data science experts.

Securing Agentic AI requires treating autonomous agents as high-value identities. Unlike standard users, agents need strict Identity and Access Management (IAM) policies that enforce "least privilege." Google Cloud helps you map agent interaction paths and assign specific agent identities, ensuring they can only access the data required for their specific tasks.

Yes. Google Cloud supports a multi-model security strategy. Whether you are using first-party models like Gemini or third-party open-source models on Vertex AI, you can apply unified runtime guardrails using Model Armor. This protects your stack from prompt injections and data leakage regardless of the underlying model provider.

Model Armor provides a dedicated security layer that filters prompt injections and sensitive data leakage before they reach the model—eliminating the need for custom-coded interceptors. It detects and blocks malicious prompts (like attempts to jailbreak the model) and filters unsafe content before it reaches your applications, protecting both your data and your brand reputation.

Preventing data leakage requires a "defense-in-depth" approach. Google Cloud offers Confidential Computing to encrypt data while it is being processed (in use), along with Data Loss Prevention (DLP) controls that can automatically scan and redact sensitive information (like PII) from model responses before they are shown to users.

For regulated industries, data sovereignty means keeping sensitive data within specific physical and digital boundaries. Google Sovereign Cloud enables you to adopt AI while maintaining full control over data residency and encryption keys (using External Key Manager), ensuring compliance with regional regulations like GDPR or local data laws.

The Secure AI Framework (SAIF) is Google’s blueprint for responsible AI adoption, designed to help organizations integrate security into their AI development lifecycle. It aligns with industry standards to help you assess risks, automate controls, and build a culture of security that scales as fast as your AI innovation.

Security focuses on technical defenses (like firewalls and encryption), while Governance focuses on policy, accountability, and risk management. As highlighted in our AI Innovation Without Interruption guide, successful AI adoption requires both: robust security tools to enforce controls, and a governance framework to define who is responsible for AI decisions and data usage.