Challenges for AI in Cybersecurity

AI is commonly viewed as a cohesive field where uniform solutions are presumed applicable to all scenarios. However, the reality dictates that effective implementation of AI in real-world contexts demands specialized knowledge tailored to specific domains, with each use case presenting unique challenges. Cybersecurity, in particular, poses numerous distinctive challenges for applied AI, and we will delve into several of the most critical ones.

Common Challenges for artificial intelligence (AI) in Cybersecurity

1. Lack of Labeled Data

In the cybersecurity field, unlike many others, data and labels are often scarce and typically require highly skilled labor to create. When examining a random set of logs in most cybersecurity systems, it’s common to find no labels at all. No one has designated whether a user downloading a document is considered malicious or benign, or whether a login attempt is legitimate. This scarcity of labeled data is a unique challenge in cybersecurity. In contrast, many other applied AI fields benefit from abundant labeled data that allows for the use of sophisticated techniques.

Due to the lack of labels, most detection methods in cybersecurity rely on unsupervised learning approaches like clustering or anomaly detection, which do not require labeled data. However, these methods come with significant limitations.

2. Anomalous Is Not Malicious

Expanding on the prior point, many strategies in cybersecurity resort to anomaly detection and clustering to identify potentially suspicious activities. While these methods offer some advantages, they also tend to flag numerous benign activities.

In mature network environments, various assets and operations are deliberately designed to appear anomalous, such as vulnerability scanners, domain controllers, and service accounts. These intentionally anomalous elements create substantial noise for anomaly detection systems, leading to alert fatigue among SOC analysts who must sift through the generated alerts. In contrast, attackers often operate stealthily, staying below the threshold of anomalous activity to evade detection by these systems. They exploit the fact that the level of deviation required to achieve their goals is typically lower than that exhibited by legitimate but deliberately anomalous assets.

Alternatively, supervised learning systems could address this challenge by filtering out activities and assets that are anomalous by design, even when incorporating unsupervised techniques within the model. However, these systems heavily rely on labeled data, which, as previously highlighted, is often scarce in cybersecurity.

3. Domain Adaptation and Concept Drift Are Abundant

Domain adaptation and concept drift represent significant challenges in data science. Typically, models undergo training on a subset of data multiple times to simulate real-world scenarios. When a model begins to diverge from real-world data, resulting in diminished precision and recall, it’s referred to as “concept drift.” Conversely, if a model fails to produce consistent results across various scenarios, it’s termed “domain adaptation.”

In the cybersecurity domain, constant evolution characterizes both attackers’ and defenders’ strategies, leading to substantial concept drift. For instance, a review of the MITRE definition of process injection reveals significant changes over recent years, with new subtechniques continually emerging. This evolution is likely to persist as attackers adapt their tactics. Consequently, models designed to detect such activities require periodic retraining to remain effective, or they risk becoming obsolete.

Furthermore, models trained in one environment may struggle to generalize well to others. Given the vast array of configurations in real-world settings, cybersecurity models often encounter significant domain adaptation issues. Consider a model trained in a controlled lab environment; such a model lacks exposure to the diverse configurations inherent in specific applications, not to mention the potential influence of other installed applications on behavior changes.

4. Domain Expertise Is Critical and Hard to Find

In contrast to various other fields, validating models in cybersecurity entails a specialized skill set. While distinguishing between a green or red traffic light doesn’t demand specialized knowledge, determining the maliciousness of a file requires expertise in malware analysis. Developing AI models for cybersecurity mandates the involvement of trained professionals capable of validating results and labeling cases to evaluate key performance indicators (KPIs). With a shortage of such experts and the predominant reliance on supervised learning in cybersecurity AI, this presents a notable challenge to the proper implementation of AI in this realm.

5. Explainability Is Key for Successful Incident Response

Even if a model achieves high precision and recall, it must also produce clear outputs to be considered effective. Incident response requires a thorough understanding of what occurred to respond appropriately to the threat. Models are instrumental tools for detecting attacks, but without clear explanations of their outputs, they do not provide tangible security value to analysts. This presents challenges for unsupervised learning, as explaining model behavior becomes more complex. Additionally, supervised models must meet a high standard of providing clear explanations of events, their importance, and how they detect suspicious activity. This clarity is essential for translating model results into actionable insights for cybersecurity professionals.

FAQ’s

Why is labeled data scarcity a significant challenge in cybersecurity AI?

In cybersecurity, unlike many other fields, data and labels are often scarce and require highly skilled labor to generate. This scarcity hampers the training of AI models, as labeled data is essential for supervised learning approaches, which are preferred for their precision and recall.

How do cybersecurity AI models handle the issue of benign activities being flagged as suspicious?

Many cybersecurity strategies rely on anomaly detection and clustering to identify potentially suspicious activities. However, these methods often flag benign activities due to deliberately anomalous elements in mature network environments. This creates alert fatigue among analysts and poses challenges in distinguishing between true threats and false positives.

What are domain adaptation and concept drift, and why are they challenging in cybersecurity AI?

Domain adaptation refers to the need for AI models to adapt to different environments, while concept drift occurs when models become less accurate over time due to changes in the data distribution. In cybersecurity, constant evolution in attacker tactics leads to significant concept drift, requiring models to be regularly retrained to remain effective.

Why is domain expertise critical for validating models in cybersecurity AI?

Validating models in cybersecurity requires specialized knowledge, particularly in areas such as malware analysis. Trained professionals are needed to validate results, label cases, and evaluate key performance indicators, but there is a shortage of such experts, posing a challenge to the implementation of AI in this field.

How important is explainability for incident response in cybersecurity AI?

Explainability is crucial for incident response in cybersecurity AI. Even if a model achieves high precision and recall, clear outputs are necessary for analysts to understand what occurred and respond appropriately to threats. This clarity is essential for translating model results into actionable insights for cybersecurity professionals.

Conclusion

AI faces significant challenges in cybersecurity, including the scarcity of labeled data, difficulty in distinguishing threats, evolving attack tactics, and the need for domain expertise. Despite these hurdles, the potential benefits of AI in cybersecurity are immense. Collaboration and innovation are essential to overcome these challenges and leverage AI effectively for enhanced threat detection and incident response, ensuring the resilience of our digital systems.