Cognitive security is the application of AI technologies patterned on human thought processes to detect threats and protect physical and digital systems.
Like other cognitive computing applications, self-learning security systems use data mining, pattern recognition and natural language processing to simulate the human brain, albeit in a high-powered computer model. Such automated security systems that are designed to solve problems without requiring human resources.
Machine learning algorithms make it possible for cognitive systems to constantly mine data for significant information and acquire knowledge through advanced analytics. By continually refining methods and processes, the systems learn to anticipate threats and generate proactive solutions. The ability to process and analyze huge volumes of structured and unstructured data means that cognitive security systems can identify connections among data points and trends that would be impossible for a human to detect.
Cognitive security may be particularly helpful as a way to prevent cyberattacks that manipulate human perception. Such attacks, sometimes referred to as cognitive hacking, are designed to affect people's behaviors in a way that serves the attacker's purpose. Cognitive security efforts in this area include non-technical approaches to making individuals less vulnerable to manipulation as well as technical solutions designed to detect misleading data and disinformation and prevent its dissemination.
See also: weaponized information