Artificial intelligence in cybersecurity: applications & the future

Malware is software designed to cause disruption in organizations’ networks by exploiting connected devices. Traditional malware detection methods involve monitoring the network for signature matches, which requires a considerable amount of manual work from security experts. In comparison, AI systems can automatically pinpoint even zero-day malware by analyzing huge amounts of historical data. Moreover, AI can identify patterns and trends in past attacks and alert companies, helping to improve the existing security strategy and proactively protect corporate systems from such future exploits.

Verizon's 2021 Data Breach Investigation report observes that 85% of cyber security attacks are now social engineering attacks that prey upon humans’ trusting nature. Previously, the problem with using AI for social engineering attacks was that they couldn’t be fully “explained” to ML systems. However, deep learning models, the most sophisticated machine learning models that mimic human thinking, can analyze even unlabeled data and learn independently. This makes deep learning-based systems more effective in combating social engineering attacks than traditional Secure Email Gateway and similar systems. Additionally, AI can also be used to create “phishing simulations” that can test user susceptibility to social engineering attacks. This is a great way of raising awareness and ensuring that employees are better equipped to deal with real-life attacks.

Even for seasoned cybersecurity professionals, one of the biggest pain points is incident response prioritization. According to research by Trend Micro Incorporated, 70% of employees at security operation centers are emotionally overwhelmed by security alert volumes. And while AI can’t replace cybersecurity professionals, it can streamline their work and make it easier. Today’s AI models can keep track of the constantly evolving cyber threat landscape and help human analysts decide what security alert to respond to first. As a result, AI can help enterprises allocate their efforts to the most critical threats first, reducing the overall risk to the organization.

As the world switched to remote working, old cybersecurity challenges have become more prevalent while new ones arose as well. However, traditional vulnerability assessment tools have grown outdated in increasingly more hybrid environments often containing mobile and IoT devices. The most important distinction between AI-based and conventional vulnerability assessment tools is that the former can accurately define the risk score of all devices, regardless of how critical they are to running a given business. There are situations when even the most isolated devices without an internet connection can pose a significant threat to the organization’s well-being, and AI can provide additional context for assessing their vulnerability.