Within the ongoing arms race in the perimeter of information security, artificial intelligence and machine learning are two of the most promising innovations.
While AI in common „personal assistants“, like those developed by Amazon, Alibaba and Google has recently reached levels at which it can convincingly make phone calls on behalf of their users, the capabilities of AI in the hands of defenders, as well as attackers, will likely evolve from buzzword to technology of significant importance over the next years.
On the defensive side, artificial intelligence powered intrusion detection will deliver the ability to pick up on anomalies within an organizations network or perimeters and raise alerts or even countermeasures much quicker than would be possible for any human security team. AI technologies supreme and literally superhumanly quick pattern recognition capabilities enable it to consistently collect intelligence regarding new threats, attempted attacks, acceptable user behaviour and constantly evolve its knowledge. This does allow AI-powered intrusion detection mechanism to find the proverbial needle in the haystack (and react to it) much faster and more concise than classical signature-based intrusion detection systems or a human security analyst.
This does have a flip-side, of course: The same AI capabilities could be used to learn about specific defences and normal user behaviour pattern in an organization and mask the malicious behaviour so it will not be recognized by classical intrusion detection systems or human onlookers.
Over the last few years, cloud computing has been the buzz. Cloud computing services offer an infrastructure that is highly scalable and supports high-performance computing. With high adoption by businesses of all sizes. Development and deployment of applications within the cloud platform are easy and time to market is done in a fraction of the time.
Artificial intelligence is not a new technology. It has been here for a long time and has helped develop computers and software that perform tasks that are associated with intelligence. Machine learning and deep learning are subsets of artificial intelligence that involve the development of algorithms that learn from data inputs and give intelligent output based on that data and the learned patterns.
A lot of research has been done and still is being done on implementing artificial intelligence into cloud computing. Cloud service providers such as Amazon, Google and Microsoft have already integrated AI into their clouds to improve service delivery. AI brings about capabilities such as machine learning, recognition of patterns and robotics to the cloud. On the other hand, the cloud is able to provide a wide range and large volumes of data since these capabilities are largely dependent on data as input so as to produce the desired output. The cloud also allows the systems to open-access and open-source data which is very crucial in facilitating collaborative learning.