What challenges arise in ensuring the transparency and explainability of AI in cybersecurity?

Transparency challenges with AI in cybersecurity arise from the complexity and opacity of AI algorithms, which can make it difficult to understand how AI systems reach conclusions or make decisions. This lack of transparency can lead to issues such as bias, privacy concerns, and a lack of accountability. Improving explainability in AI can help address these challenges by making AI systems more understandable and accountable.

To improve explainability in AI for cybersecurity, techniques such as model explainability tools, interpretable machine learning models, and providing clear documentation on how AI systems work can be employed. Additionally, organizations can implement transparency measures such as keeping records of the data used to train AI models, documenting the decision-making processes of AI systems, and regularly auditing AI algorithms to ensure they are fair and unbiased. These efforts can help increase transparency and trust in AI systems used for cybersecurity.