Artificial Intelligence and the Fight Against Cybercrime
Despite efforts to make data more readily available across the organization, the modern IT environment is increasingly complex. Hardware, software, cloud services, networking, and telephony all need to be managed, maintained and protected against compromise by cybercriminals.
Unsurprisingly, this adds significantly to the administrative overhead and costs of managing infrastructure. It also means that you need to devote an increasing number of resources to full-time monitoring of infrastructure, actively looking for breaches.
If the IT estate was static and unchanging, the human-led approach might work. But infrastructure continues to grow and evolve – as do the methods used by cybercriminals to break into the network, find weaknesses in applications and websites, and similar. Which is why the time has come to automate the process of detection and reporting.
Cutting through the noise
Usually the tell-tale signs of a security breach are readily available – if you know where to look. Buried in the various log files generated by hardware, software and network monitoring tools are the alerts and patterns that indicate a problem.
But when every device on the network is creating entries, most of which are completely innocuous, combing through logs manually is impossible – there are simply too many to work with. This is where artificial intelligence and machine learning comes into play.
By filtering log input through a machine-learning algorithm, the system can quickly establish an operating baseline. At this point, artificial intelligence is called into play, comparing every incoming event against the baseline. Any anomalies are flagged for further investigation by a member of the security team.
Over time the machine-learning algorithm will fine tune itself, improving the accuracy of detection. The artificial intelligence engine will also be able to prioritize alerts automatically, ensuring that the most urgent issues are dealt with first.
Getting to know your users
Machine learning isn’t restricted to reading log files however. Similar algorithms can be deployed to monitor and assess virtually any activity on your network.
A report by Ponemon Institute found that 78% of IT managers thought the primary reason for the difficulty in managing endpoint risk is negligent or careless employees who do not comply with security policies. Again, manually monitoring and auditing systems to identify these activities is all but impossible; users are almost always caught by accident, or as a result of a glaringly stupid mistake.
Machine learning systems can be used to observe all activities, from the files and applications used by an individual, down to the micro-movements they make with their keyboard and mouse. This helps to build an incredibly detailed picture of user behavior – another baseline that can be used to define normality.
And as with the logs, any activities outside the norm will raise an alert. Artificial intelligence will prevent even the most covert activities from going unnoticed – especially those being perpetrated by a third party using compromised credentials. It may be possible to disguise malicious activities, but the micro-behaviors of the user are impossible to fake – and artificial intelligence is perfectly capable of spotting them.
An ongoing journey, aligning AI to human review
Artificial intelligence driven security technologies are undergoing rapid development – and many are already available for deployment on your network. But although AI systems can reduce a lot of the grunt work, it’s important to maintain an element of human review - and managed cloud service providers like Navisite can combine the latest AI solution with human expertise to optimize the overall detection of potential security threats. To learn more about how Navisite’s Security Services may help to reduce the administrative overhead of your network, please get in touch. For more information on cloud security, click here or call us (888) 298-8222.