Benefits of AI and machine learning for cloud security

Robot hand and human hand joining together behind a blue security shield icon on a dark background

AI and machine learning may not be a silver bullet, but they can still play an important part in cloud security strategies

The growth of cloud shows no signs of slowing, with 96% of companies now using it for at least some of their operations, according to Rightscale.

But despite growing cloud adoption, many IT professionals still highlight the cloud as the primary area of vulnerability within their business, with 49% of companies planning to increase their cloud security budgets over the next 12 months, says a report from Cybersecurity Insiders.

To combat this and lower their chances of experiencing a breach, some companies are turning to AI and machine learning to enhance their cloud security.

AI, or artificial intelligence, is software that can solve problems and think by itself in a way that’s similar to humans. Machine learning is a subset of AI that uses algorithms to learn from data. The more data patterns it analyses, the more it processes and self-adjusts based on those patterns, and the more valuable its insights become.

While not a silver bullet or a panacea, AI and machine learning can be used to shift practices from prevention to real-time threat detection, putting companies and cloud service providers a step ahead of cyber attackers. Here are some of the benefits of using these technologies as a part of your security strategy.

Big Data processing

Cybersecurity systems produce massive amounts of data—more than any human team could ever sift through and analyse. Machine learning technologies use all of this data to detect threat events. The more data processed, the more patterns it detects and learns, which it then uses to spot changes in the normal pattern flow. These changes could be cyber threats.

For example, machine learning takes note of what’s considered normal, such as from when and where employees log into their systems, what they access regularly, and other traffic patterns and user activities. Deviations from these norms, such as logging in during the early hours of the morning, get flagged. This, in turn, means that potential threats can be highlighted and dealt with in a faster fashion.

Event prediction

By using a more data-driven approach, artificial intelligence can be used to detect and proactively alert on weaknesses and vulnerabilities both that are being exploited right now, or that might be exploited in the future. This works by analysing data coming in and out of protected endpoints, both detecting threats based on known behaviour, and spotting yet known threats based on predictive analytics.

This more predictive approach collects all endpoint activity data rather than just the 'bad' activity and enriches it from other sources to help address the root causes of a potential attack, rather than just minimising the effects once an attack is detected. It can also help create a shorter cycle between detection and remediation by ensuring a security team has the ability to react faster with better data.

Event detection and blocking

When AI and machine learning technologies process the data generated by the systems and find anomalies, they can either alert a human or respond by shutting a specific user out, among other options.

By taking these steps, events are often detected and blocked within hours, shutting down the flow of potentially dangerous code into the network and preventing a data leak. This process of examining and relating data across geography in real-time enables businesses to potentially get days of warning and time to take action ahead of security events.

Delegating to automated technologies

Alerts about potential threats or anomalies are very common with many security platforms, but there is a lot of potential with automated technologies to eliminate a lot of the noise to be able to focus on the important things. When security teams have AI and machine learning technologies handling routine tasks and first-level security analysis, they are free to focus on more critical or complex threats.

This is particularly important given the current skills shortage in cybersecurity. With 51% of organisations claiming to have a problematic shortage of cybersecurity skills, companies can relieve some of the pressure by delegating the first level of analysis to bots, allowing security professionals to focus their efforts on combatting more difficult attacks.

This does not mean these technologies can replace human analysts, as cyber attacks often originate from both human and machine efforts and therefore require responses from both humans and machines as well. However, it does allow analysts to prioritise their workload and get their tasks done more efficiently.

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