AI can’t replace security professionals, but it can improve their work and potentially lead to greater job satisfaction. Here, we will discuss the future of AI.

A valuable weapon against future AI-related cybercrimes

The features that make AI a valuable weapon against security threats – rapid data analysis, event processing, anomaly detection, continuous learning and predictive intelligence – can also be manipulated by criminals to develop new or more effective attacks and detect weaknesses in systems.

For example, researchers have used generative adversarial networks – two neural networks that compete to create training-like data sets – to successfully crack millions of passwords. Similarly, an open-source deep learning language model known as GPT-3 can learn the nuances of behavior and language. It could be used by cybercriminals to impersonate trusted users and make it nearly impossible to distinguish between genuine and fraudulent emails and other communications. Phishing attacks could become much more contextual and believable.
Advanced adversaries can already infiltrate a network and maintain a long-term presence undetected, usually moving slowly and quietly, with specific targets. Coupled with AI malware, these intruders could learn to disguise themselves quickly and evade detection while compromising many users and quickly identifying valuable data sets.

Organizations can help prevent such intrusions by fighting fire with fire: With enough data, AI-driven security tools can effectively anticipate and counter AI-driven threats in real time. For example, security professionals could use the same technique researchers use to crack passwords to measure password strength or generate decoy passwords to help detect intrusions. Contextual machine learning can be used to understand email users’ behaviors, relationships and habits to dynamically detect abnormal or risky user behavior.

What next for AI?

Humans and AI have been working together to detect and prevent breaches for some time, although many organizations are still in the early stages of using AI. But as attack surfaces and exposure outside of traditional enterprise networks continue to grow, AI is providing an answer.
Approaches such as machine learning, natural language processing and neural networks can help security analysts distinguish signal from noise. Through pattern recognition, supervised and unsupervised machine learning algorithms, and predictive and behavioral analysis, AI can help identify and repel attacks and automatically detect abnormal user behavior, network resource allocation or other anomalies. AI can be used to secure both on-premises architecture and enterprise cloud services, although securing workloads and resources in the cloud is generally less challenging than in traditional on-premises environments.

AI alone (or any other technology, for that matter) will not solve today’s or tomorrow’s complex security problems. AI’s ability to identify patterns and learn adaptively in real time, as events occur, can speed detection, containment and response, help reduce the heavy load on SOC analysts and enable them to be more proactive. These workers will likely remain in high demand, but AI will change their role. Organizations will likely need to refresh analyst skills and training to help them move from triaging alerts and other low-level skills to more strategic and proactive activities. Finally, as elements of AI and machine learning-based security threats begin to emerge, AI can help security teams prepare for the potential development of AI-based cybercrimes.

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