It is important to separate the hype from reality before setting expectations for AI and ML-based cybersecurity systems by clearing all the misconceptions about the technology.
Artificial intelligence (AI) and Machine-to-Machine (M2M) connectivity are two highly talked-about subjects in the tech community. The applications of these technologies have been explored in every field, from manufacturing and retail to finance and healthcare. And due to the hype surrounding these technologies, business leaders often assume that AI and ML can be used to solve all their problems — including those associated with cybersecurity. While such an assumption may not be entirely fallacious, business leaders should understand that AI cannot solve all of their cybersecurity woes. At least, not in the present.
Thus, it is important to separate the hype from reality before setting expectations for AI and ML-based cybersecurity systems by clearing all the misconceptions about the technology. And the biggest misconception that people have about AI is that it will completely eliminate the need for human cybersecurity personnel from their roles. However, nothing can be further from the truth.
AI won’t replace but assist cybersecurity personnel
Many companies that have sizeable IT infrastructures and operations are facing a dearth of skilled cybersecurity personnel. The cybersecurity personnel that is employed with these companies spend most of their time fixing emergent cyber threats. They are left with very little time to perform strategic assessment and upgrade of their cybersecurity systems. Thus, these cybersecurity teams are stuck in a reactive cycle, where cyberattacks, leaks, and vulnerabilities keep emerging one after the other, using up the experts’ time in just fixing them. There is little scope for implementing proactive cybersecurity in these situations.
The use of AI and ML, which can process large volumes of data rapidly, can help overburdened cybersecurity teams. These AI and ML systems are much more complex than most existing cybersecurity applications and devices, with in-built capabilities to monitor every node in an enterprise for potentially malicious elements and activities. These systems can detect emerging threats and execute appropriate countermeasures suitable for every instance of risk. These systems can also ‘learn’ from past instances of cyberattacks and improve their responses to future threats as required. The enterprises utilizing such complex AI and ML-based cybersecurity systems can easily stop most emerging cyberattacks without requiring their cybersecurity team to constantly monitor all IT systems. These AI systems can highlight vulnerabilities in the enterprise network to enable the cybersecurity team to fix them promptly.
AI’s pattern recognition can aid threat detection
AI and ML, while still not as good at independently solving complex problems as humans, are extremely good at picking up and memorizing patterns. They can analyze large sets of data and monitor large numbers of entities to identify hidden patterns that human cybersecurity personnel may not be able to perceive at all. These patterns may include individual user behavior or data access and usage patterns. And after these systems memorize such patterns, they can easily spot anomalies or instances where established patterns are not followed. This helps AI and ML systems in fraud and threat detection.
AI’s pattern recognition ability also enables it to predict attacks and stop them before they happen, making it perfect for use in Network Intrusion Prevention Systems (NIPS), Web Application Firewalls (WAF), anti-spam, and anti-malware systems. ML’s pattern recognition capability can help in the detection of botnets by analyzing communication patterns and DNS query information. Machine learning can also greatly enhance anti-malware capabilities by accurately classifying malware applications and isolating or sandboxing them before they cause damage to the enterprise network.
However, AI and ML, like most other technologies, can be a double-edged sword. While it can be a great defensive asset in the hands of your cybersecurity team, it can be a devastating weapon in the hands of cybercriminals. AI can make malware programs more resilient and self-evolving, enabling them to cause greater damage to enterprises than traditional forms of cyberattacks. Stopping such attacks can become really hasslesome for cybersecurity teams. Thus, the emergence of AI and ML in the field of cybersecurity can potentially lead to a very complicated situation, intriducing new beneficial applications as well as unprecedented levels of threat.