When one thinks of artificial intelligence or machine learning, the first place people often go to is science fiction–sentient computers, murderous robots, that kind of thing.
They’re both very large subjects of study and discovery going back decades. There are whole disciplines devoted to developing artificial intelligence (AI) and machine learning. There are hundreds of thousands of studies, papers, experiments, and real-world applications – and the fields of both studies are only growing.
They’re topics we aren’t always aware of in the real world but in reality, both touch our lives each and every day. Within the context of running a business, they are both crucial parts to your computer and network security platforms.
How does machine learning work?
In its essence, machine learning looks at pattern recognition and is often used in data mining, system optimization, and statistics. In relation to your own security platforms, machine learning is most often applied to email filtering. By working with datasets and data expectations, machine learning routines can analyze incoming email and determine what folders (such as Inbox or spam) it may end up going to. It also serves to detect incoming threats so problems are stopped before it’s too late.
Machine learning application in email gateways are growing more sophisticated and are adept at analyzing incoming emails at an efficient rate, determine what the best steps would be to take and then execute those steps.
The efficiency comes in the speed in which machine learning algorithms can analyze the incoming data, saving personnel hours of research and analysis, and automate repetitive tasks. Furthermore, after determining the validity of certain emails, this information can be shared across other networks and systems served by the same security provider.
How artificial intelligence comes into this
Artificial intelligence learns on its own from past actions and improves the performance of automated tasks and directives. An example of this might be some of the home AI devices currently on the market such as Alexa, Google Home, and Siri. All three look at past decisions users have made, such as shopping, appliance use, musical tastes, and viewing habits. Using that information, the AI anticipates future needs by preloading shopping lists with likely desired items or notifications regarding upcoming television shows or movies.
In network security, as machine learning focuses on threat detection, AI looks at the decision-making process and imitates that, also improving as it goes. By mimicking the decision-making process of a human security analyst based on previous practices and decisions regarding security, AI saves further time by promoting the removal of suspicious emails without requiring the direct input of the security team member. The AI’s own history of decisions is then included in the data experience used in its own decision making.
Machine learning and artificial intelligence working together
AI complements machine learning perfectly in that it looks at the past decisions, combined with data analysis, to form decisions and processes that more efficiently handle email threats to your security and save your security team time and money.
Machine learning sets it up. Artificial intelligence spikes it. Together, they both improve their performance.
When you apply this to network security, the effective application of ML and AI fill in the security gaps of most standard email gateways, drastically reduce the workload of human security analysts, and automatically perform their functions much more quickly.
This will save your business money and time. More importantly, it grants a sense of confidence in your security platform which can be shared with both employees and clients.