Automating mundane tasks and driving data-driven decisions, big data enables businesses to make better decisions and drive transformation.
The use of AI has been shown as an effective way of streamlining operations and enhancing security measures, but it also has to examine its potential role in the facilitation and mitigation of insider threats as well.
There are new insider threat dimensions that organisations need a deeper understanding of and must be able to control with the introduction of sophisticated technologies such as Large Language Models.
There is a debate about the dual role of AI in the sphere of insider threats as well as the best practices in dealing with these threats, which will lay the groundwork for a deeper discussion on how to mitigate them.
The advanced algorithms that are built into AI can analyze vast quantities of data to identify patterns or patterns in behaviour within a network, so they can alert them to potential risks before they become life-threatening.
It is possible to train artificial intelligence to detect the signs of potential malware exfiltration or anomalous log-in activities, which can help prevent the spread of internal threats as a proactive solution.
An effective security tool, User Behavior Analytics identifies unusual behaviour and anomalies in user behaviour by analyzing a variety of different types of data collected from the user.
With UBA, a baseline of normal user behaviour is created by analyzing data from a variety of sources, such as logs, network traffic and endpoints, and by using machine learning, automation, and artificial intelligence.
As soon as UBA detects anomalous behaviour that may indicate an insider threat, it notifies security teams immediately.
There has been a significant amount of research conducted on the cost of insider incidents, including findings from IBM's 2023 Cost of a Data Breach Report, which shows just how much time and money insider incidents can eat into a company.
The use of artificial intelligence and machine learning by organizations can help them identify insider threats with more accuracy and speed as well as enhance their detection capabilities.
In addition to that, UBA also monitors user behaviour and establishes a baseline that typically lasts for a minimum of seven days to identify deviations that could indicate a security threat, so that deviations can be pinpointed.
Along with AI, machine learning, and UBA, the combination of these technologies has shown the dynamic nature of cybersecurity, demonstrating how threats evolve as well as how we must respond to them.
It is very unlikely that today's threats will remain the same as those of tomorrow.
In light of this, it is extremely important to integrate AI into security systems to continuously improve security systems.
Using these technologies, organizations will be able to take a proactive approach instead of just reacting to threats, enabling them to stay ahead of threats.
As a result, a strong cybersecurity strategy is based on a proactive approach, one that can adapt to the constantly changing threats that are lurking around the corner.
It is important to remember that there is no doubt that AI-enhanced UBA is a significant achievement in the fight against cyber threats, as it provides businesses and their data with an enhanced level of security.
It has demonstrated that technology can be used effectively to achieve better data security, thereby improving businesses' bottom lines.
For organizations to be successful in protecting their most valuable assets against insider threats and preventing data breaches, the strategies and tools they employ are essential to thwarting insider threats and preventing data breaches as they continue to navigate the complexities of digital security.
This Cyber News was published on www.cysecurity.news. Publication date: Wed, 27 Dec 2023 15:43:04 +0000