Artificial Intelligence: The Next OT Cybersecurity Influencer

AI is a double-edged sword when it comes to facing the OT cybersecurity landscape. While it empowers organizations to proactively detect and respond to threats and continues to push the limits of automation in the technology that is trusted to secure essential data, devices, and networks, its susceptibility to adversarial attacks and potential for misuse by malicious actors can turn it into a new attack surface, posing a significant risk to critical infrastructure and operational technology systems.

Human operators play a vital role in AI-driven OT cyber defense. While AI systems excel in data processing, they lack the nuanced understanding and judgment of humans. Effective collaboration between human security analysts and AI systems is crucial for achieving a high level of cyber resilience.

To best foster this collaboration, organizations must invest in training and upskilling their workforce. Human operators can leverage AI-generated insights to make informed decisions while AI automates routine tasks and provides real-time threat intelligence. This synergy enables human operators to focus on more strategic, high-level tasks, ultimately strengthening the organization's defense capabilities.

The other aspect of the human element in this symbiosis is our responsibility to develop a deep understanding of AI and have answers for the questions swirling around the fast-paced technology. Let’s look at some pressing concerns popping up around AI as it pertains to cybersecurity:

AI in OT cybersecurity can be developed in-house or customized using the popular platforms of the day. When considering external AI platforms, organizations must carefully assess data confidentiality and sharing. It is essential to ensure that sensitive information is adequately protected.

Hesitance exists within the critical infrastructure sector regarding trust in AI systems. Given the critical nature of OT cybersecurity, this caution is understandable. However, trust in AI can be built through rigorous testing, validation, and transparent communication about AI's capabilities and limitations. As AI technologies continue to prove their effectiveness, trust in their role in cyber defense will likely grow. One must keep in mind where and how AI can be effective to increase its benefits while still maintaining full control over key production systems and equipment. This is paramount to maximize uptime.

While AI enhances security, it can also become a potential attack surface. Adversaries may attempt to manipulate AI models or their training data to deceive security systems. The risk of AI predictions intentionally inducing Byzantine failures in decision-making processes is a legitimate concern. This underscores the need for ongoing monitoring and robust defenses around AI systems to protect them from exploitation.

AI models are trained on extensive datasets, some of which may contain copyrighted materials. Organizations must navigate this challenge by ensuring they have the necessary rights and permissions for the data used in AI training. This may involve licensing agreements, data anonymization, or alternative data sources to avoid legal issues.

AI is poised to significantly improve Red Team/Blue Team cyber simulations by enhancing both offensive and defensive capabilities. On the Red Team side, AI-powered attack simulations can make threat scenarios more sophisticated and dynamic, mimicking the evolving tactics of real attackers. This challenges Blue Teams to improve their detection and response strategies. For Blue Teams, AI can augment threat detection and incident response by quickly identifying unusual patterns, vulnerabilities, and anomalies, thus improving defenses and helping to automate routine tasks. Furthermore, AI can generate valuable insights by analyzing vast amounts of data, enabling more efficient decision-making and proactive security measures.  

Tabletop Exercises (TTX) conducted at the operation room level offer significant value in enhancing preparedness for real-world cyber incidents. They help organizations practice response strategies, identify weaknesses in their incident response plans, and improve communication and coordination among team members. AI's impact on TTX drills lies in its ability to provide real-time threat intelligence, analyze vast datasets, and simulate realistic cyberattack scenarios, thereby increasing the sophistication and realism of these exercises.

Feedback loops, where AI-driven security responses influence system behavior that, in turn, impacts AI detections, require careful management. Organizations must implement safeguards and control mechanisms to prevent unintended consequences. Regular evaluation and adaptation are essential to finding the right balance.

Defending against adversarial machine learning attacks and deepfakes in an environment where AI systems are continually evolving poses a considerable challenge. The boundary between attacker and defender in the digital realm is blurring. For this reason, among others, the US government issued an executive order in an effort to formally address these concerns. Never-the-less, organizations must invest in evolving their AI defenses, conduct ongoing research, and collaborate with the wider cybersecurity community to stay ahead of emerging threats.