Tackling AI Security

It has been a while since I wrote, as I was totally tied up with catching up with various facets of Application Security. My work is continuing, and you might see gaps in my blog-writing, but I will try my best to keep you posted on the trends. Something interesting came up recently, which is security of AI systems, AI security in short. It is a very involved topic, as both AI and security are deeply involved topics. As a test and quality professional, there’s tons to grasp, and it’s clearly overwhelming, not to mention the multitude of international standards that are already there and the ones that are evolving. In this blog, let’s look at some aspects of security of AI systems.

To me, AI systems are a type of software systems, although a very complicated type in scope to grapple. While AI systems can be very helpful in getting things done because they have access to the information and assets, things won’t be nice if they go rogue! We need to exercise caution in granting privileges to AI systems, because they have the tendency to make conclusions on their own – well, not in the way we expect but in their own way. This can result in unexpected consequences of how they use our data. So it is important to secure our AI systems. And we need to have a framework to do that.

One of the key considerations in AI security is the knowledge of the model itself. As AI systems learn from new data, they may exhibit behaviors that diverge from their original design. This can be positive, that improves performance, but it can also lead to negative outcomes if the model or the data it operates on is manipulated.

In light of these complexities, adopting a zero-trust approach is paramount. Rather than blindly trusting AI systems based on their initial configuration, organizations must continuously verify and validate their behavior. Guardrails must be established to govern the access and behavior of AI systems, ensuring that they operate within predefined boundaries and constraints.

One crucial aspect of securing AI systems is to limit their exposure to external influences, particularly The Internet. Just as we exercise caution in exposing sensitive data to external networks, we must similarly shield AI models from untrusted sources. By minimizing their connectivity to the internet, we reduce the risk of malicious actors exploiting vulnerabilities or manipulating the model’s behavior.

To address the multifaceted challenges of AI security, we need to adopt industry-wide initiatives and frameworks that provide guidelines and best practices for securing AI systems, covering aspects such as data privacy, model integrity, and threat detection.

A word on security awareness of Data Scientists and Engineers…

Ultimately, securing AI systems requires a collaborative effort between data engineers, data scientists, and cybersecurity professionals. Data engineers and scientists must be equipped with the knowledge and tools necessary to write secure code and design AI systems with AI security in mind from the outset. By embedding security principles into the development lifecycle, organizations can mitigate the risks associated with AI deployment. It is also important that security professionals create awareness about the key aspects of security to the data professionals.

As AI continues to permeate various aspects of our work and life, ensuring AI security is so important. By treating AI as a handler of sensitive data, understanding its access privileges and implementing robust security measures, organizations can utilize the power of AI while safeguarding against potential threats and vulnerabilities.

For more thoughts on AI and security, please feel free to reach out to me.