Ask These 5 AI Cybersecurity Questions for a More Secure Approach to Adversarial Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) present limitless possibilities for enhancing business processes, but they also expand the potential for malicious actors to exploit security risks. Like many technologies that came before it, AI is advancing faster than security standards can keep up with. That’s why we guide security leaders to go a step further by taking an adversarial lens to their company’s AI and ML implementations. 

These five questions will kickstart any AI journey with security in mind from the start. For a comprehensive view of security in ML models, access our white paper, “The CISO’s Guide to Securing AI/ML Models.”

5 Questions to Ask for Better AI Security

1. What is the business use-case of the model?
   Clearly defining the model’s intended purpose helps in identifying potential threat vectors. Will it be deployed in sensitive environments, such as healthcare or finance? Understanding the use-case allows for tailored defensive strategies against adversarial attacks. 
2. What is the target function or objective of the model?
   Understanding what the model aims to achieve, whether it’s classification, regression, or another task, can help in identifying possible adversarial manipulations. For instance, will the model be vulnerable to attacks that attempt to shift its predictions just slightly or those that aim for more drastic misclassifications? 
3. What is the nature of the training data, and are there potential blind spots?
   Consider potential biases or imbalances in the training data that adversaries might exploit. Do you have a comprehensive dataset, or are there underrepresented classes or features that could be manipulated by attackers?
4. How transparent is the model architecture?
   Will the architecture details be publicly available or proprietary? Fully transparent models might be more susceptible to white-box adversarial attacks where the attacker has full knowledge of the model. On the other hand, keeping it a secret could lead to security through obscurity, which might not be a sustainable defense. 
5. How will the model be evaluated for robustness?
   Before deployment, it’s crucial to have an evaluation plan in place. Will the model be tested against known adversarial attack techniques? What tools or benchmarks will be used to measure the model’s resilience? Having a clear evaluation plan can ensure that defenses are systematically checked and optimized.

The most successful technology innovations start with security from the ground up. AI is new and exciting, but it leaves room for critical flaws if security isn’t considered from the beginning. At NetSPI, our proactive security experts help customers innovate with confidence by proactively planning for security through an adversarial lens. 

If your team is exploring the applications of AI, ML, or LLMs in your company, NetSPI can help define a secure path forward. Learn about our AI/ML Penetration Testing or contact us for a consultation.  

Learn about NetSPI’s AI/ML Penetration Testing in our Solution Brief.