Google Secure AI Framework
A conceptual framework for cooperatively securing AI technologies is being released by Google.
AI has enormous promise, particularly generative AI. However, in order to develop and implement this technology responsibly, there must be clear industry security standards in place as it moves forward in these new areas of innovation. The Secure AI Framework (SAIF), a conceptual framework for secure AI systems.
Why SAIF is being introduced
Incorporating Google’s knowledge of security mega-trends and hazards unique to AI systems, Secure AI Framework draws inspiration from the security best practices it has implemented in software development, such as evaluating, testing, and managing the supply chain.
In order to ensure that responsible actors protect the technology that underpins AI developments and that AI models are secure by default when they are implemented, a framework spanning the public and private sectors is necessary.
At Google, they adopted a transparent and cooperative approach to cybersecurity over the years. To assist respond to and prevent cyberattacks, this entails fusing frontline intelligence, experience, and creativity with a dedication to sharing threat information with others. Building on that methodology, Secure AI Framework is intended to assist in reducing threats unique to AI systems, such as model theft, data poisoning of training data, quick injection of harmful inputs, and extraction of private information from training data. Following a bold and responsible framework will be even more important as AI capabilities are used in products worldwide.
Let’s now examine Secure AI Framework and its six fundamental components:
1. Provide the AI ecosystem with more robust security foundations
To safeguard AI systems, apps, and users, this involves utilizing secure-by-default infrastructure safeguards and knowledge accumulated over the previous 20 years. Develop organizational knowledge to stay up with AI developments while beginning to expand and modify infrastructure defenses in light of changing threat models and AI. For instance, companies can implement mitigations like input sanitization and limiting to assist better defend against prompt injection style attacks. Injection techniques like SQL injection have been around for a while.
2. Expand detection and response to include AI in the threat landscape of an organization
When it comes to identifying and handling AI-related cyber incidents, promptness is essential, and giving an organization access to threat intelligence and other capabilities enhances both. This involves employing threat intelligence to foresee assaults and keeping an eye on the inputs and outputs of generative AI systems to identify irregularities for companies. Usually, cooperation with threat intelligence, counter-abuse, and trust and safety teams is needed for this endeavor.
3. Automate defenses to stay ahead of both new and current threats
The scope and velocity of security incident response activities can be enhanced by the most recent advancements in AI. It’s critical to employ AI and its existing and developing capabilities to stay agile and economically viable when defending against adversaries, who will probably use them to scale their influence.
4. Align platform-level rules to provide uniform security throughout the company
To guarantee that all AI applications have access to the finest protections in a scalable and economical way, control framework consistency can help mitigate AI risk and scale protections across various platforms and technologies. At Google, this entails incorporating controls and safeguards into the software development lifecycle and expanding secure-by-default safeguards to AI platforms such as Vertex AI and Security AI Workbench. The firm as a whole can gain from state-of-the-art security by utilizing capabilities that cater to common use cases, such as Perspective API.
5.Adjust parameters to mitigate and speed up AI deployment feedback loops
Continuous learning and testing of implementations can guarantee that detection and prevention capabilities adapt to the ever-changing threat landscape. In addition to methods like updating training data sets, adjusting models to react strategically to attacks, and enabling the software used to create models to incorporate additional security in context (e.g. detecting anomalous behavior), this also includes techniques like reinforcement learning based on incidents and user feedback. To increase safety assurance for AI-powered products and capabilities, organizations can also regularly perform red team exercises.
6. Put the hazards of AI systems in the context of related business procedures
Last but not least, completing end-to-end risk assessments on an organization’s AI deployment can aid in decision-making. An evaluation of the overall business risk is part of this, as are data lineage, validation, and operational behavior monitoring for specific application types. Companies should also create automated tests to verify AI’s performance.
Why we are in favor of a safe AI community for everybody
To lower total risk and increase the standard for security, it has long supported and frequently created industry guidelines. Its groundbreaking work on its BeyondCorp access model produced the zero trust principles that are now industry standard, and it has partnered with others to introduce the Supply-chain Levels for Software Artifacts (SLSA) framework to enhance software supply chain integrity. These and other initiatives taught us that creating a community to support and further the work is essential to long-term success.
How Google is implementing Secure AI Framework
Five actions have already been taken to promote and develop a framework that benefits everyone.
With the announcement of important partners and contributors in the upcoming months and ongoing industry involvement to support the development of the NIST AI Risk Management Framework and ISO/IEC 42001 AI Management System Standard (the first AI certification standard in the industry), Secure AI Framework is fostering industry support. These standards are in line with SAIF elements and mainly rely on the security principles included in the NIST Cybersecurity Framework and ISO/IEC 27001 Security Management System, in which Google will be taking part to make sure upcoming improvements are appropriate to cutting-edge technologies like artificial intelligence.
Assisting businesses, including clients and governments, in understanding how to evaluate and reduce the risks associated with AI security. This entails holding workshops with professionals and keeping up with the latest publications on safe AI system deployment best practices.
Sharing information about cyber activities involving AI systems from Google’s top threat intelligence teams, such as Mandiant and TAG.
Extending existing bug hunters initiatives, such as Google Vulnerability Rewards Program, to encourage and reward AI security and safety research.
With partners like GitLab and Cohesity, it will keep providing secure AI solutions while expanding its skills to assist clients in creating safe systems.
