Machine learning has already made significant improvements in various areas, but it also comes with risks. The responsible use and development of AI require the identification and mitigation of these risks. Organizations are using red teams to assess and enumerate the immediate risks of AI. The NVIDIA AI red team philosophy and ML system framing are introduced in this post. The assessment framework provides a comprehensive approach to addressing risks and setting expectations for ML security. It covers various aspects, including technical vulnerabilities, harm-and-abuse scenarios, and compliance. By adopting this framework, organizations can strategize and improve the security of their ML systems.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Increasing availability of AI technology | From restricted to public access | Widespread availability and use of AI | Advancements in AI technology and increased demand |
| Use of red teams to assess AI risks | From unassessed risks to identified and mitigated risks | Improved assessment and mitigation of AI risks | Need for responsible use and development of AI |
| Framework for ML security assessments | From unstructured assessments to standardized assessments | Standardized ML security assessments | Need for categorizing, assessing, and mitigating AI risks |
| Integration of ML security into information security | From separate perspectives to integrated perspectives | Holistic view of ML security | Need for responsible use and development of AI |
| Governance, risk, and compliance in ML security | From lack of compliance to adherence to standards | Compliant ML systems and reduced risks | Need for adherence to GRC standards |
| Development lifecycle of ML systems | From disjointed systems to integrated systems | Tightly integrated ML systems with reduced vulnerabilities | Need for secure and efficient ML development |
| Aggregation of skill sets for assessments | From separate assessments to collaborative assessments | Enhanced assessment effectiveness and learning | Increased collaboration and knowledge sharing |
| Addressing new prompt-injection techniques | From vulnerability to defense against prompt injection | Improved defense against prompt injection in LLMs | Need for input validation and security controls |
| Defining security boundaries in ML systems | From unsecured systems to compartmentalized systems | Reduced attack surfaces and increased visibility in ML systems | Need for secure ML systems and increased control |
| Use of privilege tiering in ML development | From unrestricted access to controlled access | Improved security and access control in ML development | Need for secure ML development practices |
| Conducting tabletop exercises for ML security | From unpreparedness to preparedness for security incidents | Enhanced preparedness and response to security incidents | Need for proactive security measures and incident response planning |