Applied AI in cybersecurity has many unique challenges, and we will take a look into a few of them that we are considering the most important.
On the other hand, supervised learning systems can remediate this issue and filter out anomalous by design activities and assets, even when using unsupervised techniques as part of the model.
Models are usually trained on a subset of data many times in a simulation of the real world.
Models trained to detect such activity require retraining, or they become obsolete.
Models trained in one environment don't necessarily generalize well for others.
Due to the large set of configurations in real-world environments, models trained for cybersecurity issues tend to have considerable domain adaptation issues.
Imagine a model trained on a lab environment, that model has never been fed with examples of the myriad of configurations applicable to a specific application, let alone how different applications might change the behavior due to other installed applications.
Unlike many other domains, validating models requires unique cybersecurity expertise.
Building AI models for cybersecurity requires trained experts that can validate the results and label cases to assess key performance indicators.
Even if you can train the best model that has high precision and recall but the output isn't clear, it's not a good model.
Models are just tools that help reach the goal of detecting the attack, but without explaining what happened, those don't translate into actual security value for analysts.
This creates challenges for unsupervised learning as it's harder to explain the model behavior.
It also creates a high bar for any supervised model that must provide a proper explanation on what happened, why it's important, and how it's detecting the activity.
We have invested considerably into explainability and transparency with documentation and explainability models where needed.
Tailor your AI solutions to the specific cybersecurity challenges you face.
Models trained on outdated or limited data may become obsolete.
Regularly retrain models and consider the dynamic nature of the threat landscape.
Domain Expertise Is Essential: Building AI models for cybersecurity requires domain expertise.
Validate models with cybersecurity experts who can assess key performance indicators.
Models must not only detect threats but also provide clear explanations of what happened, why it's important, and how they detected the activity.
This Cyber News was published on www.paloaltonetworks.com. Publication date: Tue, 12 Mar 2024 17:28:05 +0000