This article is part of a series of written products inspired by discussions from the R Street Institute’s Cybersecurity-Artificial Intelligence Working Group sessions. Visit the group’s webpage for additional insights and perspectives from this series.
Although AI’s integration into cybersecurity is not new (see Part 1: Understanding Current AI Applications and Benefits), its rapid evolution requires continuous adaptation. Technology companies are focused on expanding AI integration with their existing security products while actively tracking emerging developments for further enhancements. With the market for AI-based cybersecurity products projected to grow from $15 billion in 2021 to around $135 billion by 2030, we must anticipate and prepare to integrate emerging AI advancements, equipping ourselves for an inevitable and dynamic AI-fueled cybersecurity landscape. While many promising AI advancements and emerging technologies are currently under development, there are three primary areas within the field of cybersecurity to which the next wave of AI applications is expected to bring significant—even transformational—advancements.
1. Advanced Threat Detection
Quantum machine learning (QML) leverages the unparalleled power of quantum computing to perform complex data analyses. Its proficiency in handling large-scale and computationally intensive tasks makes it superior to current computing and machine-learning capabilities. This rapid analysis accelerates threat detection, thereby enhancing a cybersecurity practitioner’s ability to respond to cybersecurity incidents promptly.
Predictive threat intelligence is another transformative offering for advanced cyber threat detection. Currently, AI models are being developed to predict new and unknown threats and vulnerabilities by analyzing vast datasets and identifying patterns. These models are unique because they scrutinize trends from previously identified threats like malware and ransomware attacks, empowering businesses to prepare and strengthen the defenses on their systems and data without impacting them directly. The ability to predict the likely evolution of these threats marks a major advance from today’s reactive threat-intelligence strategies.
Moreover, AI is also being used to develop digital-twin technology for simulating cyberattack scenarios. As virtual replicas of physical objects or systems, digital twins will enhance preparation for a wide range of potential real-world threats. For instance, a power-grid company could use a digital twin of its infrastructure to run hundreds or even thousands of excursions that simulate various cyberattack scenarios, using the results to develop tailored and robust mitigation strategies. These simulations could be particularly helpful for critical infrastructure sectors, where the ramifications of cyberattacks can be far-reaching.
2. Dynamic Incident Response and Adaptive Cyber Defense
Driven by AI, self-healing systems repair and adapt to evolving cyber threats in real time without human intervention. For instance, a cloud server detecting a software flaw could autonomously implement a patch to an identified software vulnerability and reroute traffic to maintain uninterrupted service. These systems enhance traditional human-led responses with more resilient capabilities. While they include features like automated software patching and reduced operational and service disruptions, their primary focus remains on system maintenance and resilience rather than active threat engagement.
In contrast, autonomous response systems extend beyond current automated response capabilities because they can execute immediate, holistic, and strategic actions to mitigate damage during a cyberattack. For instance, if a cybersecurity system identifies the beginning of a ransomware attack, it could make the split-second decision to independently isolate affected network segments, alert the security team, and initiate recovery processes immediately. Current cyber defense capabilities are generally confined to basic threat detection, vulnerability management, and remediation recommendations that still require human intervention and take more time.
AI systems are also being used to engage in continuous simulations