What challenges do cybersecurity professionals face when using deep learning?

1 Data quality and quantity

One of the key challenges of deep learning is that it requires a lot of data to train the models and achieve high accuracy and performance. However, data is not always available, reliable, or representative of the real-world scenarios that the models need to handle. For example, data can be noisy, incomplete, outdated, biased, or manipulated by adversaries. This can affect the quality and validity of the models and their predictions, and make them vulnerable to errors and attacks. To address this challenge, cybersecurity professionals need to ensure that they have access to sufficient and relevant data, and that they apply rigorous data cleaning, validation, and augmentation techniques to improve the data quality and diversity.

3 Interpretability and explainability

A third challenge of deep learning is that it often lacks interpretability and explainability, which means that it is difficult to understand how the models work and why they make certain decisions or predictions. This can be problematic for cybersecurity professionals who need to verify, validate, and audit the models and their outputs, and to ensure that they comply with ethical and legal standards. For example, if a model makes a mistake or causes a breach, it can be hard to identify the root cause, the responsible party, and the appropriate response. Moreover, if a model makes a decision or a prediction that affects a human's rights, interests, or well-being, it can be hard to justify, explain, or challenge it. To address this challenge, cybersecurity professionals need to adopt methods and tools that can enhance the interpretability and explainability of the models, and that can provide transparency, accountability, and fairness for the users and the stakeholders.

4 Scalability and complexity

A fourth challenge of deep learning is that it involves scalability and complexity issues, which means that it can be costly, time-consuming, and resource-intensive to develop, deploy, and maintain the models and their infrastructure. For example, deep learning models can have millions or billions of parameters, and require massive amounts of computing power, storage, and bandwidth to train and run. This can increase the operational and financial risks and burdens for the organizations that use deep learning, and make them dependent on external providers or platforms. Moreover, deep learning models can have intricate and dynamic architectures, and require constant updating and tuning to adapt to changing data and environments. This can make them hard to manage, monitor, and secure, and expose them to potential errors and vulnerabilities. To address this challenge, cybersecurity professionals need to optimize the models and their infrastructure, and use techniques such as compression, pruning, distillation, and federated learning to reduce the size, complexity, and overhead of the models, and to improve their efficiency and performance.

5 Human factors

A fifth challenge of deep learning is that it involves human factors, which means that it can be influenced by the human behavior, cognition, and emotions of the developers, users, and attackers of the models. For example, human factors can introduce biases, errors, or conflicts into the models and their outputs, and affect their quality and reliability. Moreover, human factors can create ethical, social, or psychological issues that can affect the trust, acceptance, and satisfaction of the users and the stakeholders of the models. For example, some users may feel uncomfortable, insecure, or threatened by the models and their capabilities, and some stakeholders may have different or conflicting expectations, interests, or values regarding the models and their outcomes. To address this challenge, cybersecurity professionals need to consider the human factors that can impact the models and their applications, and to involve the users and the stakeholders in the design, development, and evaluation of the models, and to ensure that they respect the human dignity, rights, and preferences of the users and the stakeholders.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?