Although they are both fairly new developments AI and machine learning can be still be used as an important factor in cloud security strategies.
A data breach can cost a company almost £3M on average, and can take approximately a year to contain the breach. With the increasing number of data breaches and shocking statistics on the impact of poor cyber security, companies are looking into more ways to be more secure with their business activities.
Artificial Intelligence is software that adopts similar thinking to humans in order to solve complex problems quickly. Machine learning is an aspect of AI which uses algorithms to learn from data analysing patterns and adjusting as more patterns and insights become available.
These changes and technical advances put users in the driving seat, with the ability to proactively protect themselves from security breaches.
Event Detection and Blocking
When AI and machine learning technology process the data generated by the systems and find anomalies, there are various options available, such as alerting a human or shutting a specific user out.
These steps allow for events to be detected and blocked within minutes and hours rather than days and weeks, restricting the flow of potentially dangerous code into the network and preventing any further data leaks. Processing and examining data across different locations in real-time can also give business ample warning and time to take action ahead of security events.
The addition of AI and machine learning technology to handle routine tasks and first level security analysis has big advantages for a workforce. One key advantage is that it allows the company more time to focus on critical and complex threats that may occur during their work.
Of course, these technologies cannot replace human analysts, and as cyber-attacks are often caused by both human and machine efforts they require responses by both humans (your workforce) and the technology you use. With the correct use of AI and machine learning, analysts are able to prioritise their workloads and tasks more effectively and strategically.
The processing of big data
As cyber-security systems produce extremely large amounts of data, more than enough for any human to ever be able to interpret and sift through, it is important to use machine learning technologies to detect threat events. The more data the machine learning processes, the more patterns it is able to detect and the quicker its ability to spot anomalies.
Any deviations that occur to normal workforce activity can then noted, examples may include logging in during the night, these activities can then be flagged leading to early spotting of and dealing of potential security threats with speed and efficiency.
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