Today, thanks to large databases, increasingly sophisticated models can classify complex and varied attacks without the need to clearly define them. However, this development is accompanied by increasing ambiguity. Although advanced ML methods, such as deep neural networks, show excellent performance in the laboratory, their use as a “black box” can cause unpredictable and hard-to-understand errors in real-world situations. So it is useful to understand how the understanding of AI in the world of cyber security and why it has become necessary.
The concept of AI explainability
Clarity is the ability of a system to make its reasoning process and results comprehensible to humans. In today’s context, state-of-the-art models often act as “black boxes”, hiding the details of their operation. This lack of transparency raises questions. Indeed, without a clear understanding of the decision-making process, it becomes difficult to identify, let alone correct, possible errors. In addition, it is difficult for humans to trust AI that produces results without apparent justification.
Importance of persuasiveness
In areas where decision-making is critical, understanding how AI works is essential to trusting it. Lack of clarity and transparency is a barrier to the integration of AI in these sensitive sectors today. Let’s take the example of a security analyst; It needs to know why the behavior was classified as suspicious and obtain deep attack reports before taking significant action such as blocking traffic from specific IP addresses. But persuasiveness doesn’t just benefit end users. For engineers and designers of AI systems, it simplifies the detection of potential errors in the ML model and avoids “blind” adjustments. Clarity is therefore central to the design of reliable and trustworthy systems.
How to make AI explainable
ML models like decision trees are naturally interpretable. Although generally less efficient than more sophisticated ML techniques such as deep neural networks, they provide complete transparency.
Some “post hoc” techniques, such as SHAP and LIME, have been developed to analyze and interpret “black box” models. By modifying inputs and observing corresponding variations in outputs, these techniques can analyze and predict the performance of many existing models.
The “understanding-by-design” approach goes beyond post hoc techniques by integrating explainability into the design of AI systems. Rather than explaining the model a posteriori, explainability by design ensures that every step of the system is transparent and understandable. This may involve the use of hybrid methods and allows the formulation of appropriate explanations.
So clarity in AI is not a luxury, but a necessity, especially in sensitive areas like cybersecurity. It makes it possible to gain user trust but also continuously improve the search engine. This is an essential point to consider when choosing a security solution.
