It takes a year and almost £3 million pounds to contain the average data breach, according to a 2018 study by the Ponemon Institute. And despite growing cloud adoption, many IT professionals still highlight the cloud as the primary area of vulnerability within their business.
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, this approach shifts 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.
Up to 95 percent of data leaks in the cloud through 2020 will happen because of human error. Learn more about how AI and machine learning are helping combat cybercriminals in this whitepaper.
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 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.
Almost three quarters of successful data breaches gain access through an endpoint. Download this whitepaper now to learn more about securing your laptops, tablets and mobiles through the cloud.
Delegation to the Automation
When security teams have AI and machine learning technologies handle routine tasks and first level security analysis, they are free to focus on more critical or complex threats.
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.