The AI revolution is here, and it's moving fast. You know the promise: transformative capabilities, game-changing efficiencies, better tools. But with great power comes great responsibility, and in the case of AI, a whole new set of interesting security challenges.

That's why we're thrilled to share our latest paper “SAIF in the real world". This isn't just another theoretical document. It's a deep dive into applying Google's Secure AI Framework (SAIF) in the real world, throughout the entire AI development lifecycle. 

We've put these principles to the test, and we're ready to share what we've learned. A few of these  things may surprise you:

1. AI Security is about More Than Just managing the risk of Prompt Injection: Yes, it's a hot topic, but the paper reveals a much broader landscape. Data governance, infrastructure security, and application security are equally critical.


2. Data Governance is the Foundation: You might think the model is the core, but it's the data. Proper data governance from the start can prevent a cascade of security issues later.


3. AI Security is Software Security 2.0: Many traditional security practices apply, but they need to be adapted. Secure coding, supply chain security, and vulnerability management are still vital.


4. "Build or Buy" Has Security Implications: Choosing between training your own AI model or using one from a provider affects everything from cost to access controls approaches. SAIF applies in both scenarios, but the context matters.


5. The Model Lifecycle is Key: Security isn't a one-time exercise. It needs to be integrated into every stage, from opportunity discovery to ongoing monitoring.


6. Threat Modeling Must Evolve: Don't just focus on the latest AI-specific threat. Consider threats in context across all dimensions of data, infrastructure, application, and model.


7. Agentic AI Ups the Ante: AI systems that perform actions on their own introduce a new types of risk. Security needs to account for the real-world consequences of those actions.

Consider these tips to help reduce the risk:

1. Start with Data Governance and Extend It to AI Governance: Review and update your data governance policies to include AI use cases. Look at data, track lineage, and implement strong access controls.


2. Contextualize AI Risk: Understand the specific use cases of your AI systems. This will help you prioritize risks and make informed decisions.


3. Secure the Model Lifecycle: Integrate security into every stage of the AI development process, from design to deployment and monitoring.


4. Treat Models as Code: Apply software supply chain security practices to your models. Track provenance, manage vulnerabilities, and secure your development environment.


5. Enhance Infrastructure Security: Secure the infrastructure where AI models are trained and deployed. Implement access controls, monitor for anomalies, and protect data at rest and in transit.


6. Secure APIs: If your AI systems use APIs, ensure they are properly authenticated, validated, and rate-limited.


7. Monitor Model Behavior: Implement robust monitoring and logging to track model performance and detect anomalies. Watch for drift, biases, and unexpected outputs.


8. Stay Informed and Adapt: AI security is a rapidly developing field. Stay up-to-date on the latest threats, best practices, and frameworks. 

Reference our new paper for more in-depth guidance on  navigating the complexities of AI security. It's time to move beyond the hype and get practical. Let's secure the future of AI together!