Discover The Article
- 1 Improved Risk Detection
- 2 Training and Case Studies
- 3 Data Flow Monitoring
- 4 Improving Predictive Analytics
- 5 AI and Machine Learning Won’t Solve Everything
Last Updated on March 29, 2021 by Editor Futurescope
Machine learning allows artificial intelligence to learn, improve and grow without being explicitly programmed outside of its initial development. It uses a variety of data processing, analytics and developed algorithms to collect and retain information.
The option to learn and grow from a vast input of data makes machine learning one of the most promising and lucrative technologies today, across many industries. It can be used to automate and improve the efficiency of driverless vehicles, improve order fulfillment and shipping for online retailers, and much more.
It can also be exploited and leveraged to improve cybersecurity and data protection.
Improved Risk Detection
Security experts and engineers have a lot on their plate as they deal with the supply of data coming in or going out of an organization’s network. It is physically impossible for them to identify and assess every single threat, problem or vulnerability in a network. They can sure try, but it’s likely they’ll miss one or two components. Take into account the widening talent gap and that problem becomes exacerbated even more due to a resource shortage.
The solution is machine learning, or similarly powered systems. An AI or machine learning tool can be trained to look for certain vulnerabilities and attack patterns, for instance. It can then be programmed to take action or send alerts to the necessary experts. Whatever the case, a computer is infinitely more capable of assessing and monitoring a data stream than a human who is fallible and prone to exhaustion.
The beauty of using such systems for simple risk detection is that the bulk of the work can still be passed on and handled by human experts. While AI is currently great for automating tasks, especially rote work, it’s not an alternative to true, effective experience. Not to mention, hackers will likely turn to AI and automation tools, as well.
Training and Case Studies
Machine learning systems can be leveraged to help train security experts and professionals on certain attacks, patterns or even new technologies they might not be familiar with. This is essential to improving and maintaining proper security, because the industry — including hackers — continues to evolve and grow in the background. It only makes sense for security engineers and experts to stay as close to the bleeding-edge as possible.
Although it is from a separate industry, there are already talks of machine learning and AI being used to facilitate the training and education of human laborers in industries such as retail, manufacturing and more.
Data Flow Monitoring
With modern networks, data constantly flows in and out, and in many ways. You have employees accessing the internal network via their workstations, others tapping in using their mobile devices and smartphones, and even more relying on satellite equipment like conferencing or telecommunications hardware. Whatever the case, there’s a lot going on with working networks, and one of the most important ways to monitor security is to keep an eye on such data flows.
By discovering an unauthorized data transfer, for example, you can stop it altogether and prevent future access. This is a great example of how data protection and security remains a continuous and active process. Prevention is important, but so is the ability to react to an attack, and how you handle yourself in the wake of one.
An efficient machine learning platform could be used to monitor data flows and ensure everything is aboveboard. As far as your network is concerned. It can also handle the process in real-time. As well as sending alerts and notifications to the proper personnel when a breach or issue is flagged.
Service providers exist with the aforementioned capabilities already — Splunk’s IT Service Intelligence system is a great example.
Improving Predictive Analytics
Wouldn’t it be great if we had a crystal ball that could tell us when, where and how an attacker was going to strike? While such a device is a pipe dream, it is entirely possible to predict potential channels and methods of attack through predictive analytics. If you collect enough data, and spend enough time processing and sorting through it, you can easily find vulnerabilities, exploits and common forms of attack on your network or systems.
With machine learning, however, the process can be automated and improved. A constant flow of data means a constant series of insights and information for the system to implement. With the right algorithms, AI could even tell you days in advance when a suspected attack is coming. How? Hackers usually feel out the network and systems they’re targeting before diving in. Even if they don’t, AI will know exactly when they do and how they intend to breach the network thanks to its vast supply of performance and historical data.
AI and Machine Learning Won’t Solve Everything
While both technologies offer some incredible potential in the way of cyber-security and data protection. They are certainly not the end-all-be-all solutions that will solve every single problem in the industry. Furthermore, even a capable AI is no match for solid, human expertise and experience. IT professionals will need to learn and adapt so they can work alongside these systems and tools in stride, however.
Whatever the case, machine learning is poised to improve modern cybersecurity in more ways than one and that’s always a plus.