Artificial Intelligence is an indisputable factor in the way we do business now and it will only continue to reinforce our processes and operations moving forward. Driven by big data analytics, supervised and unsupervised machine learning; the question is to what degree the power of AI can change the face of cybersecurity in order to streamline business?
At face value, machine learning enables a more efficient, straightforward and proactive cybersecurity function. Sifting through the data at a quicker pace than its human counterparts, the machine enables the cybersecurity team to respond to cyber-attacks in real time. Cyber teams can rely on the machines to analyse millions of websites in search of anomalies, uncover threats and rate them according to risk, block malicious users, restrict unauthorised access and keep a summary of all errors; and all in a fraction of the time it typically takes.
The challenges that cybersecurity is currently facing when it comes to keeping data secure are a lack of relevantly skilled practitioners, insight and speed. Thus with the support of AI, the function as a whole can operate far more effectively.
As the nature of cyber-attacks themselves evolves at an alarming rate and pace, so too the tools and skills of cyber warriors evolve right along with them. Thus having aptly skilled individuals and keeping those individuals’ skillsets up to date is vital to combat cybercrime. The mere reality of coming under pressure from laws and regulations, such as GDPR, which require notification of breaches anywhere as quickly as 4 hours after the event, means businesses absolutely must react quickly to threats and attacks.
The obscure nature of the cyber landscape and the volume of data to be analysed makes the concept of keeping pace with it virtually impossible. Thus machine learning systems are imperative to successful cybersecurity. Insights from big data analytics tools, one of the key features of AI, can be used to detect a range of cyber threats as well as examining irregularities within a network to help analysts identify areas of vulnerability.
Certainly on its own big data analytics provides a wealth of opportunity in fighting cybersecurity threats. However, it is most effective when combined with machine learning and intelligent risk management insights. Utilising big data analytics programs gives you a deeper view of your cyber threats, assessing the class and intensity via data sources and patterns. Leveraging these insights will help to predict the impact of a potential attack. Moreover, experts can build predictive models from those insights which will issue alerts as soon as it detects an entry point for an attack. This type of mechanism of course would not be feasible without machine learning and AI.
Machine learning can be understood both as supervised, wherein the data scientist has all the information but wants to train the data to learn from the outcome so it can find more of the same, and unsupervised, where the outcome is unknown which means the machine must learn new things in real time. The latter is more commonly used by most organisations as for the most part we are relying on the machine to unearth the anomalies for us. Unsupervised learning systems will run an extensive search that uncovers most anomalies though it will also throw out a few false positives too.
The beauty of machine learning systems is their ability to refine the information needed to adequately defend data against a cybersecurity breach. For example, rather than blurting out a handful of random single alerts which then need to be analysed and pieced together before the full picture of threat can be understood, the system will correlate all alerts into one as well as provide an analysis about where, how and why the problem originated.
Both supervised and unsupervised strains of machine learning in addition to big data analytics require an intelligent collection of data to begin with. This creates a cohesion between the AI and the data its examining in order to streamline your cybersecurity and make it more proactive, less costly and ultimately more effective.