What Does Explainability in AI Cybersecurity Entail?
What Does Explainability in AI Cybersecurity Entail? Artificial intelligence has revolutionized various domains, including cybersecurity. However, this promising technology brings forth concerns regarding explainability and transparency. Machine learning has witnessed remarkable advancements in recent years.
Today, with massive databases, increasingly sophisticated models can classify complex and diverse attacks without the need for explicit definitions. However, this progress comes with growing opacity. While advanced ML methods, such as deep neural networks, exhibit excellent efficiency in the laboratory, their use as "black boxes" can lead to unexpected and challenging-to-understand errors in real-world conditions. It is therefore essential to understand what explainability in AI entails in the cybersecurity realm and why it has become a necessity.
The Concept of AI Explainability
Explainability is the ability of a system to make its reasoning process and results intelligible to humans. In the current context, sophisticated models often operate as "black boxes," concealing the details of their functioning. This lack of transparency raises issues. Without a clear understanding of the decision-making process, identifying, let alone correcting, potential errors becomes challenging. Moreover, it is complex for humans to trust AI that delivers results without apparent justification.
The Significance of Explainability
In fields where decision-making is critical, understanding how AI operates is crucial to bestow trust upon it. The absence of explainability and transparency is currently a hindrance to integrating AI into these sensitive sectors. Consider the example of a security analyst; they need to know why a behavior was classified as suspicious and obtain in-depth attack reports before taking significant actions such as blocking traffic from specific IP addresses. However, explainability benefits not only end users. For AI system engineers and designers, it simplifies the detection of potential ML model errors and avoids "blind" adjustments. Thus, explainability is central in designing reliable and trustworthy systems.
How to Make AIs Explainable
ML models like decision trees are naturally explainable. While generally less efficient than more sophisticated ML techniques like deep neural networks, they offer complete transparency.
Certain "post hoc" techniques, such as SHAP and LIME, have been developed to analyze and interpret "black-box" models. By altering inputs and observing corresponding variations in outputs, these techniques allow the analysis and deduction of how many existing models function.
The "explainability-by-design" approach goes beyond post hoc techniques by integrating explainability from the inception of AI system design. Instead of seeking to explain models after the fact, "explainability-by-design" ensures that every step of the system is transparent and understandable. This may involve the use of hybrid methods and enables the creation of appropriate explanations.
Explainability in AI is not a luxury but a necessity, especially in sensitive areas like cybersecurity. It builds user trust and facilitates continuous improvement in detection systems. It is a crucial consideration when choosing a security solution.