Challenges of using AI
Implementing AI for security data analytics can be incredibly beneficial as it can assist organizations in detecting and responding to threats more effectively. However, it comes with various challenges that organizations need to address. Some of these challenges include the following:
- Data quality and quantity: AI models, especially DL ones, require large amounts of high-quality data to perform effectively. Obtaining sufficient data that accurately represents various cyber threats can be challenging in cybersecurity. Additionally, the data needs to be well labeled and up to date to ensure the AI model’s accuracy and reliability.
- Imbalanced data: Cybersecurity datasets often suffer from class imbalance, where malicious activities are much less frequent than normal activities. This can lead to biased models that perform well on normal data but struggle to detect rare threats. Addressing class imbalance is crucial to avoid false negatives. ...
