AI is continuously transforming IT workflows and traditional activities. A significant change is the advent of AI red teaming: a new practice based on traditional pen testing. It aims to identify, model, and correct unexpected AI cybersecurity issues.
Just as Shadow IT is a known challenge addressed by pen testing, Shadow AI is now a reality that requires addressing. Countering Shadow AI involves practices that test the security of machine learning models and data sets used in AI solutions. These practices focus on two main areas: identifying vulnerabilities in your AI implementation and modeling potential attack methods.
From a vulnerabilities perspective, AI red teaming focuses on the following areas:
Incident response: Address acute issues and manage slow-moving, chronic problems that accumulate over time.
Data leakage: Assess how much information is leaked during AI queries.
Data security practices: Ensure data is authenticated, encrypted, and stored securely, addressing any misconfigurations.
Creating sustainable models is crucial
AI's most promising application in cybersecurity is threat modeling. AI is set to democratize this space. For decades, we've built accurate threat and attack models, but the process has mostly been manual, making it slow and costly. If done right, AI can significantly improve the efficiency and accessibility of threat modeling.
How to speed up modeling process as PenTesters
Follow best practices! AI red teaming includes many of the same steps as a traditional PenTest. Although steps may vary between organizations or industry sectors, AI red teaming generally includes:
Engagement management: Identify stakeholders, define the scope, establish communication methods with stakeholders, and choose appropriate frameworks (e.g., STRIDE for data disclosure issues or DREAD for specific weaknesses). If you’re all focused on specific weaknesses of supporting technologies, you may want to adopt an approach that looks for the OWASP Top 10.
Conducting tests: Identify specific queries and attacks to capture real-time data on how the AI system responds. This phase generates a wealth of data for analysis.
Results analysis: Analyze results and implement mitigation strategies based on identified issues. A strong data analytics background can greatly enhance contributions in this step.
Report creation: Include the correct report components and create an effective method for communicating findings. Provide recommendations for addressing discovered issues.
So, there you have it—an overview of AI red teaming. If you're eager to dive deeper into the best practices of pen testing, check out the newly updated CompTIA PenTest+ certification. Explore the exam objectives to see how these principles can be applied to AI and elevate your cybersecurity expertise.
Blog Contribution by James Stanger, Chief Technology Evangelist
