Artificial intelligence (AI) has been around for decades, but we are only starting to realize its full potential now. AI development has accelerated exponentially across (almost all) industries. For instance, AI’s role in cybersecurity has evolved significantly in recent years, changing how security professionals and organizations approach cyberdefense.

In the 1980s and 1990s, cybersecurity software and systems depended on rule-based algorithms to detect threats. Every potential malicious scenario had to be explicitly programmed to defend against known threats. However, these algorithms were hopeless in protecting against evolving threats.

The rise of machine learning (ML) in the 2000s led to a shift in cybersecurity as smart algorithms could learn from the data and identify anomalies and patterns that suggested a potential threat. ML models are now quite effective in detecting new and evolving advanced threats and cybersecurity has significantly improved.

According to Carmen Kolcsár, CTO at rinf.tech, “While machine learning (ML) models have improved threat detection, the scalability of these solutions became the next focus areas for organizations. As the volume of data and complexity of threats continue to grow, ensuring that ML-based cybersecurity systems can handle large-scale operations efficiently becomes crucial. Also, ensuring continuous model updates and adaptation to new attack vectors becomes crucial.”

The advent of deep learning (DL) in the 2010s enabled enhanced threat detection and response and automated security protocols. It allowed organizations to detect and defend against complex threats quickly and more accurately and even predict potential attacks and vulnerabilities before they occurred.

However, these AI models couldn’t explain how they got these results. Developers and security professionals couldn’t explain or identify the reasoning behind certain decisions. That’s where explainable artificial intelligence (XAI) comes in.

What is Explainable AI (XAI)?

XAI is a set of methods and processes that help human users understand output and results generated by ML algorithms. It also allows us to trust the results provided by machines.

“AI solves the black box problem by generating human-understandable explanations for the predictions made by the AI system. These explanations can be visualizations, textual descriptions, or interactive interfaces that let users explore the decision-making process of the AI system. This enhances trust in AI systems and facilitates the detection and correction of biases, errors and other issues in the models. New advancements contribute to further improving the solution for the back box problem, such as a normative framework for XAI or techniques like input heatmapping, feature-detection identification and diagnostic classification.” Kolcsár shared.

How Does XAI Optimize Cybersecurity?

XAI in cybersecurity is like a colleague who never stops working. While AI helps automatically detect and respond to rapidly evolving threats, XAI helps security professionals understand how these decisions are being made.

“Explainable AI sheds light on the inner workings of AI models, making them transparent and trustworthy. Revealing the why behind the models’ predictions, XAI empowers the analysts to make informed decisions. It also enables fast adaptation by exposing insights that lead to quick fine-tuning or new strategies in the face of advanced threats. And most importantly, XAI facilitates collaboration between humans and AI, creating a context in which human intuition complements computational power.,” Kolcsár added.

By making AI-powered cybersecurity systems more transparent, comprehendible and interpretable, XAI helps build trust, improve decision-making (and response), enable rapid response to advanced threats and facilitate human and AI collaboration.

Build Trust and Respond With Confidence

During an active security event, security teams don’t have time to second-guess the recommendation provided by AI. They need to trust the guidance and quickly act upon it. XAI’s transparency into AI reasoning helps build and nurture trust over the long term.

XAI can also help ensure compliance during the decision-making process, especially with decisions that may impact data security and privacy.

Eliminate Bias and Enhance Accuracy

When analyzing oceans of data, there is always room for bias. XAI’s transparency helps shed light on potential biases and errors in training data. Over time, this approach helps improve the accuracy of AI models.

AI models that are accurate, fair and transparent lead to better outcomes in AI-powered decision-making.

Furthermore, it also empowers organizations to take a responsible approach during AI development.

Such an approach to AI-driven security also ensures that ethical considerations remain at the forefront and are quickly addressed.

Adapt to New Threats and Respond Effectively

With XAI working behind the scenes, security teams can quickly discover the root cause of a security alert and initiate a more targeted response, minimizing the overall damage caused by an attack and limiting resource wastage.

As transparency allows security professionals to understand how AI models adapt to rapidly evolving threats, they can also ensure that security measures are consistently effective. As threat actors increasingly use AI in their malicious activities, XAI can help security teams better understand advanced threats aiming to go undetected by AI models.

Challenges in Implementing XAI in Cybersecurity

Although XAI optimizes cybersecurity protocols and enhances the user experience of security professionals, there are some challenges:

Adversarial Attacks
There is always an ever-present risk of threat actors exploiting XAI and manipulating the AI model and how it works. As XAI in security systems becomes more prevalent, this will remain a key concern for all stakeholders.

Complex AI Models
Complex algorithms like DL can be a challenge to explain, even for XAI. So, understanding the reasoning behind AI decisions may not always be straightforward.

Computational Resources
XAI demands extra processing power to elucidate AI decisions. This can be challenging for many organizations and security teams who are already working with limited resources.

“The main selling point of XAI is transparency, but it usually must be balanced with the budgets. There are several factors to consider for XAI to be effective, and all of them put pressure on the finances. The first one is infrastructure scalability which must be considered by design at the same time with the seamless integration of the XAI with the existing setups. Opting between cloud (scalability, but also cost), on-prem (more control but upfront investments) or a hybrid approach is one of the choices every team must make. The second one is performance (or the trade-offs with performance): deciding where to draw the line between interpretability and system efficiency is not an easy task. The third one is the training and maintenance overhead. Without allocating resources for model fine-tuning, retraining and maintenance, any great XAI can quickly become outdated or biased. Last, but not least, security teams already have plenty of tasks on their plates, so it is mandatory to strategically prioritize XAI within resource allocation.” Kolcsár stated.

Data Privacy and Security

The techniques XAI uses to explain AI decisions have the potential to reveal sensitive data employed by the company to train the AI model. Whenever this happens, it will create a conflict between transparency and privacy.

Lack of User Understanding

XAI can provide explanations, but it’s useless if security professionals don’t understand them. For example, some XAI explanations may be far more technical than what they are accustomed to. Thus, it is important to customize XAI for the audience and enable effective communication.

Kolcsár added that as “XAI research and development continue, with new methods like Concept Relevance Propagation, or advanced neural network interpretability, and as the system evolves, we can expect that XAI will become more effective and easier to implement.” As AI’s influence on security grows, explainability will become critical to ensure that security measures are ethical, accountable and consistently effective.