How reliable is real-time security?
Today’s fast-paced world means changing the approach to security. Traditional ways of protecting networks or facilities often involved responding to threats after they occurred or preparing for the most likely attacks based on experience.
One option is called real-time security, or real-time adaptive security, which allows people to use a different approach.
Real-time configurations monitor an entire network and collect details about traffic levels, connected devices, which parties are attempting to access particular resources and when those attempts occur. They also learn what constitutes typical behavior, generating alerts when activity deviates from the norm.
Understandably, however, many people wonder about the reliability of real-time security systems. How could they minimize the chances of the technology causing false alarms?
Real-time security could ease the burdens on team members.
Many companies lack sufficient staff to deal with all potential security threats. There is also a greater likelihood that attacks will go undetected for longer periods of time, giving hackers time to cause serious and costly damage. However, many real-time systems have automated artificial intelligence (AI) functions that categorize threats and suggest which ones to address first.
A 2019 IBM survey found that 76% of people who use cybersecurity automation in their organizations rated its ability to detect threats highly compared to 53% of respondents who did not use automation as extensively.
However, a poorly trained or overly sensitive real-time system could bombard people with too much information, making it difficult to find genuine dangers. Real-time information, whether collected to improve physical or cyber security, is reliable and valuable. However, the system must eliminate irrelevant data.
Effective real-time security requires contextual analysis.
The security sector is not the only industry that relies on real-time information. Healthcare providers rely on it to make faster, more personalized care decisions for their patients. The research also showed that 92% of companies are increasing their investments in real-time analytics for financial decision-making.
Successfully relying on real-time data requires looking at information in context. Some people get fixated on single data points, unable to see the whole picture. That could become problematic when someone tries to access a network’s resources. For example, what if a U.S.-based worker provides the correct login information but does so from a German IP address? Inconsistency in location may be an indication of an attempted attack.
Las soluciones de autenticación adaptable se utilizan cada vez más en la industria de la seguridad. Utilizan el aprendizaje automático y los datos compilados sobre un usuario para calcular una puntuación de riesgo para cada interacción. Esas herramientas pueden eventualmente diferenciar entre usuarios y dispositivos confiables frente a aquellos que pueden representar un peligro.
Los hackers buscan causar el mayor caos posible y saben que para hacerlo es necesario actualizar sus métodos. De la misma manera que muchos líderes de empresas han modernizado sus enfoques de ciberseguridad con IA y opciones similares de alta tecnología, los ciberdelincuentes han ajustado sus estrategias para tener los impactos más severos con sus intentos.
Sin duda, las soluciones de ciberseguridad en tiempo real como la de redborder cuenta con unas herramientas altamente precisas y tienen protecciones internas que evitan que los hackers manipulen. En este contexto, se recomienda que en los análisis se aplique también el pensamiento crítico y no solo confiar exclusivamente en lo que dice el sistema.
