Google's Secure AI Framework

The potential of AI, especially generative AI, is immense. As innovation moves forward, the industry needs security standards for building and deploying AI responsibly. That’s why we introduced the Secure AI Framework (SAIF), a conceptual framework to secure AI systems.

Six core elements of SAIF

SAIF is designed to address top of mind concerns for security professionals, such as AI/ML model risk management, security and privacy – helping to ensure that when AI models are implemented, they are secure by default.

Expand strong security foundations to the AI ecosystem
Extend detection and response to bring AI into an organisation’s threat universe
Automate defenses to keep pace with existing and new threats
Harmonise platform level controls to ensure consistent security across the organisation
Adapt controls to adjust mitigations and create faster feedback loops for AI deployment
Contextualise AI system risks in surrounding business processes
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Enabling a safer

We’re excited to share the first steps in our journey to build a SAIF ecosystem across governments, businesses and organisations to advance a framework for secure AI deployment that works for all.

Bringing SAIF to governments and organisations

We collaborate with governments and organisations to help mitigate AI security risks. Our work with policymakers and standards organisations, such as NIST, contributes to evolving regulatory frameworks. We recently highlighted SAIF's role in securing AI systems, aligning with White House AI commitments.

Expanding SAIF with industry allies

We are fostering industry support for SAIF with partners and customers, hosting SAIF workshops with practitioners and publishing AI security best practices. We partnered with Deloitte on a whitepaper on how organisations can use AI to address security challenges.

Additional resources
Common Questions about SAIF

How are SAIF and responsible AI related?

Google has an imperative to build AI responsibly and to empower others to do the same. Our AI Principles, published in 2018, describe our commitment to developing technology responsibly and in a manner that is built for safety, enables accountability and upholds high standards of scientific excellence. Responsible AI is our overarching approach that has several dimensions such as ‘Fairness’, ‘Interpretability’, ‘Security’ and ‘Privacy’ that guide all of Google’s AI product development.

SAIF is our framework for creating a standardised and holistic approach to integrating security and privacy measures into ML-powered applications. It is aligned with the ‘Security’ and ‘Privacy’ dimensions of building AI responsibly. SAIF ensures that ML-powered applications are developed in a responsible manner, taking into account the evolving threat landscape and user expectations.

How is Google putting SAIF into action?

Google has a long history of driving responsible AI and cybersecurity development, and we have been mapping security best practices to new AI innovation for many years. Our Secure AI Framework is distilled from the body of experience and best practices we’ve developed and implemented, and reflects Google’s approach to building ML and gen-AI-powered apps, with responsive, sustainable and scalable protections for security and privacy. We will continue to evolve and build SAIF to address new risks, changing landscapes and advancements in AI.

How can practitioners implement the framework?

See our quick guide to implementing the SAIF framework:

  • Step 1 – Understand the use
    • Understanding the specific business problem that AI will solve and the data that is needed to train the model, will help drive the policy, protocols and controls that need to be implemented as part of SAIF.
  • Step 2 – Assemble the team
    • Developing and deploying AI systems, just like traditional systems, are multidisciplinary efforts.
    • AI systems are often complex and opaque, have a large number of moving parts, rely on large amounts of data, are resource intensive, can be used to apply judgement-based decisions and can generate novel content that may be offensive, harmful or that can perpetuate stereotypes and social biases.
    • Establish the right cross-functional team to ensure that security, privacy, risk and compliance considerations are included from the start.
  • Step 3 – Level set with an AI primer
    • As teams embark on evaluating the business use, the various and evolving complexities, risks and security controls that apply – it is critical that parties involved understand the basics of the AI model development lifecycle and the design and logic of the model methodologies, including capabilities, merits and limitations.
  • Step 4 – Apply the six core elements of SAIF (listed above)
    • These elements are not intended to be applied in chronological order.

Where can I find more information about SAIF and how to apply it to my business or entity?

Stay tuned! Google will continue to build and share Secure AI Framework resources, guidance and tools, along with other best practices in AI application development.

Why we support a secure AI community for everyone

As one of the first companies to articulate Al principles, we've set the standard for responsible Al. It guides our product development for safety. We’ve advocated for and developed industry frameworks to raise the security bar and learned that building a community to advance the work is essential to succeed in the long term. That’s why we’re excited to build a SAIF community for all.


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