In part one of this series, we discussed the evolving landscape of cybersecurity and the roles artificial intelligence (AI) and machine learning (ML) play in the security space today. Here in part two, we discuss the advancements that have been made in AI and ML that strengthen cybersecurity and the challenges that come with implementing this evolving technology.
Advancements in AI and ML for Cybersecurity
Cybersecurity solutions are making use of advancements in AI and ML to overcome the limitations of traditional, signature-based detection methods. These new systems analyze vast amounts of data in order to learn patterns and behaviors and make informed decisions based on what they’ve learned.
Among the most impressive advancements in ML are deep learning systems. These systems use artificial neural networks that mimic the functions and structure of the human brain. In practice, AI/ML-driven solutions that leverage this technology can analyze enormous amounts of data to learn user behavior throughout an organization, detect when deviations occur, and take appropriate action. Among many other implementations, these solutions are capable of preventing new malware from entering a system as well as identifying insider threats and compromised accounts.
Looking toward the future, AI/ML-driven cybersecurity solutions will have more sophisticated threat detection and response capabilities and will most likely perform their tasks faster and more efficiently. They could also evolve into completely autonomous security systems that can operate without any human intervention.1 As AI/ML-driven cybersecurity solutions edge closer to these realities, security professionals need to keep a few concerns top of mind.
Challenges and Limitations of AI and ML in Cybersecurity
Despite the exciting contributions of AI and ML in cybersecurity, their challenges and limitations cannot be ignored. One major concern lies with algorithmic bias, which can occur if an AI/ML- driven solution is trained with biased data. If the data is biased, then the solution is likely to perpetuate and even magnify those biases.
For instance, an AI/ML-driven solution trained on historical data that is biased toward specific types of threats may disregard other attacks that behave outside of those previously learned parameters. Cybersecurity professionals thus recommend training AI/ML-driven solutions with diverse data sets and performing regular audits to identify and fix possible biases.
Furthermore, AI/ML-based solutions still struggle to understand intent and context. This limitation can lead to false positives or negatives, which are misidentifications of normal behavior as malicious or vice versa. False reporting is one of the many reasons why AI/ML-
driven solutions cannot yet be completely autonomous, as human intervention is still sometimes needed to interpret AI/ML-generated results.
Preparing for an AI-Driven Cybersecurity Future
Your organization’s unique needs will dictate how you implement AI and ML, but there are a few must-do’s to keep in mind when onboarding these technologies.
Be sure to extensively inspect and test any ML training models on a secure, virtual machine before you fully deploy them. Some training data might be “poisoned” deliberately as a form of cyberattack to force your AI to learn incorrectly and not function properly. It's also possible for a model to be tampered with by an inside adversary.
Provide your system with updated data as often as possible. Your AI/ML-driven solution is only as good as the data it can learn from—making it imperative for your system to continuously learn from diverse data sets to adapt to the evolving threat landscape as well as to the changes in your organization. Also, remember that just because your solution can learn doesn’t mean it is autonomous. Human oversight and intervention will be necessary to keep your system trained properly and to also reinforce/discourage certain behaviors such as false reporting.
AI and ML stand to change the game for security professionals in their never-ending quest to get ahead of the bad guys. While these technologies come with some limitations, they are worth adopting to better defend against threat actors who now leverage similar technology in an attempt to out-gun defenders. Don’t fall behind in this escalating arms race—check out CASM® today.
1.https://www.analyticsvidhya.com/blog/2023/02/future-of-ai-and-machine-learning-in-cybersecurity/
