As AI becomes more ingrained in businesses and everyday life, the importance of security becomes increasingly paramount. In fact, according to the IBM Institute for Business Value96% of leaders say they adopt Generative AI (GenAI) makes a security breach likely in their organization within the next three years. Whether it is a model performing unintentional actions, generate misleading or harmful responses or revealing sensitive information, in the age of AI, security can no longer be an afterthought to innovation.
AI red teaming is now emerging as one of the most effective first steps businesses can take to ensure systems security. But security teams can’t approach testing AI the same way they do software or applications. You need to understand AI to test it. It is imperative to acquire knowledge of data science: without this skill, there is a high risk of “false” reporting on safe and secure AI models and systems, thus widening the window of opportunity for attackers .
And while important, testing in the AI era must consider more than just extracting patterns and weights. Currently, not enough attention is paid to securing AI applications, platforms, and training environments that have direct access to or are adjacent to an organization’s core data. To fill this gap, AI testing should also consider machine learning security – machine learning security operations or “MLSecOps”. This approach makes it possible to evaluate attacks against the machine learning pipeline and those coming from backdoor models and code execution within GenAI applications.
Ushering in a new era of red teaming
That’s why the new IBM X-Force Red AI Testing Service is delivered by a team with deep expertise in data science, AI Red Teaming, and applications. penetration testing. By understanding algorithms, data processing, and model interpretation, testing teams can better anticipate vulnerabilities, guard against potential threats, and maintain the integrity of AI systems in an increasingly digital landscape. powered by AI.
The new service simulates the most realistic and relevant risks facing AI models today, including direct and indirect risks. rapid injections, member interference, data poisoning, pattern mining, and adversarial evasion to help businesses uncover and remediate potential risks. Concretely, the test offering covers four main areas:
- GenAI Application Testing
- AI Platform Security Testing
- MLSecOps Pipeline Security Testing
- Safety and Security Testing Model
AI and generative AI technologies will continue to develop at breakneck speed, and new risks will emerge along the way. This means that the red team’s AI will have to adapt to match this moving innovation.
X-Force Red’s unique AI testing methodology is developed by a cross-team of data scientists, AI red teams, and cloud, container, and application security consultants who regularly build and update day of methodologies. This unique approach integrates automated and manual testing techniques and includes NIST, MITER ATLAS, and OWASP Top 10 methodology for extended language model applications.
Red Teaming is a necessary step to securing AI, but it is not the only step. IBM’s framework for securing AI details the most likely attacks on AI and the most important defensive approaches to help quickly secure AI initiatives.
If you’re attending the RSA conference in San Francisco, come to IBM booth #5445 on Tuesday May 7 at 2:00 p.m. to learn more about AI testing and how it differs from traditional approaches.
