The term artificial intelligence is used to describe an IT system’s simulation of human intelligence processes, such as the ability to adapt, solve problems or plan. Artificial intelligence systems cover several of these features at present and, with the advent of ChatGPT, their use has become widespread in everyday life.
However, this has also resulted in organizations exploiting the term "artificial intelligence," seeking to capitalize on its appeal. This is because the term AI is often used loosely and can refer to a variety of different technologies. With so many companies boasting they have AI capabilities, it’s essential to be able to distinguish real AI solutions from those that simply claim they are based on this technology.
How to distinguish real AI?
The most common misconception about AI is that it is synonymous with automation. But the reality is that automated systems must be manually configured to execute monotonous and repetitive tasks, whereas AI systems are able to adapt independently once they have data to process. While AI does leverage aspects of automation, it goes beyond simply executing tasks. Here are the key differences between real AI and technologies that only appear to be based on it:
AI systems use machine learning (ML) to generate algorithms= that learn from the data they are fed with and use statistical algorithms to identify patterns in them. In contrast, intelligent systems, which do not integrate AI, only work with algorithms. These systems are created using a set of predefined rules and decision trees that specify how they should behave in certain situations.
AI is designed to continuously learn and improve over time. As new data becomes available, the system can retrain itself to enhance its accuracy and capabilities. Solutions that rely on automation are limited in scope and can only perform specific tasks within the constraints of pre-programmed rules.
AI is designed for non-repetitive tasks, so it can analyze situations and make decisions without human intervention whereas automated systems are incapable of making decisions on their own.
Benefits of real AI for cybersecurity
AI has great potential for cybersecurity. While automation makes it possible to combat automated bot attacks and alleviate alert fatigue thereby enabling analysts to apply their knowledge and skills more efficiently, real AI offers benefits such as:
1. Improved performance over time:
Solutions using ML improve performance over time thanks to their ability to learn from experiences and network patterns to refine effectiveness. This brings adaptability to security defenses and steps up accuracy in detecting anomalies in standard network activity.
2. Improved threat detection:
Thanks to its ability to learn and adapt to changes in malicious cyber actor behavior, AI improves threat detection by identifying patterns that human analysts cannot. It adds value when detecting unknown threats and is a powerful ally when dealing with customized APT (advanced persistent threat) attacks.
3. Helping address talent shortages:
By analyzing large amounts of data, AI can identify patterns, anomalies, and potential threats much faster than human analysts. These capabilities don’t mean human expertise isn’t relevant, but they allow us to stay ahead of the curve by discovering evolving threats and detecting attacks in near real time. In this respect, AI enables us to do more in less time and is a boon to cybersecurity teams struggling with talent shortages.
4. Better endpoint protection:
AI-based endpoint detection and response tools such as WatchGuard's EPDR and EDR establish a behavioral baseline for endpoints. Our Zero Trust Application Service, included in both solutions, only allows applications classified as trusted to run on each endpoint. Moreover, the execution of malicious applications and processes or unknown applications are classified in a maximum time of 4 hours and blocked by our AI in 99.98% of cases and thanks to the actions of our technical experts in the remaining 0.02%.
In this regard, an AI-powered XDR solution, such as WatchGuard's ThreatSync, which uses these security products as a foundation, can continuously learn, adapt, and improve its threat detection and response capabilities. By using AI and ML technologies to alert us to potential threats in real time and across multiple domains, it reduces mean time to detection (MTTD), adds greater visibility, and enables multi-product response. These measures help build robust security.