Each and every organization, big or small, transfer and receive a large number of data every day, both nationally and internationally through networks and online devices. This data could be an organization’s personal or financial data, or any sort of other important information, which, if get leaked then can lead to major consequences or security issues. This type of information is often prone to attack by any external interference because of insufficient cybersecurity measures.
Cybersecurity has always been a major concern in the digital world. Data breach, ID theft, and Captcha cracking have affected a number of individuals as well as organizations. Sadly, we tend to rely on Firewalls as a defense, but firewalls won’t be able to stop a skilled hacker. The challenges have always been endless in inventing right controls and procedures and implementing them with acute perfection for tackling cyber attacks and crimes.
What was the need for AI in Cybersecurity?
Detection of cyber threats manually is not an easy task. It requires a long chain of data analysis for identifying the details (like identification and effect on the system) of the threat. Artificial Intelligence (AI) is a scalable way to ensure the security of the organization.
“AI is an area of computer science that focuses on finding innovative solutions to complex problems by taking intelligent decisions. They are intelligent machines that work and behave like the human brain.” From healthcare to robotics, it has been applied in almost every field of science and engineering.
Following are the factors ensuring the need for AI:
Managing High Volume of Secured Data: Artificial Intelligence quickly scans the high amount of data and lists the problems and abnormalities in seconds.
High Identification and Reaction Time: AI speeds up the process of early identification of security threats and suggests proposals according to their optimization.
Eradicating Human Efforts: With so much amount of data, it is quite obvious for humans to make mistakes. AI helps in supporting them so as to get the desired solutions.
Extracting the threat needles in the network: Hackers are always there in the network, wanting to grab the right moment to attack. AI immediately examines the track of situation and behavior to extract the threat needles that can cause mischievous activities.
Advantages of applying Artificial Intelligence in Cybersecurity
AI provides real-time threat detection, helping organizations to stay ahead of cyber attackers. Below are some of the advantages of using AI as a part of a security strategy:
Fast Threat Detection Technique: Cybersecurity systems produce a large amount of data every day. AI produces all of this data to detect threat events. More the data, the higher the threat pattern detection which is used to spot any abnormality in the normal pattern flow.
Threat Prediction: By analyzing the behavior of transferred and received data out of secured endpoints. AI proactively detects and alerts on weak and unprotected data. This data could be exploited at the same time or in the near future by cyber attackers.
This Threat Predictive Approach gathers all the endpoint activity data instead of only detecting the “bad activity”. AI not only minimizes the detected threat, but it also enhances the threat from other resources to help address the root cause of it.
Threat Detection and Blocking: When AI processes the data generated by the systems, they immediately respond either by alerting a human or by shutting out a specific user.
By this, threats are detected and blocked immediately by slamming the flow of potentially risky codes available in the network, preventing the data leakage. With the detection and blocking of threats, organizations get a specific amount of time to take necessary actions to avoid the attacks.
Where can Artificial Intelligence be applied in Cybersecurity?
In Cybersecurity solutions, AI is highly considered or is applied to some of the applications listed below:
Email Scanning: Gmail uses Artificial Intelligence to detect and block unwanted spam and deceitful emails. AI recognizes the spam emails as it detects the pattern of marking emails as spam or not spam by humans.
Fraud Recognition: AI developed a fraud detection solution for the benefit of people using MasterCard. MasterCard Implemented Decision Intelligence is an AI-based security solution that is made using algorithms based on expected customer behavior.
It evaluates customer’s regular spending habits, the trader, location of the purchase, and a variety of other sophisticated algorithms, to determine whether a purchase is out of the ordinary.
AI for Antiviruses: Traditionally, antivirus programs were signature-based. The issues in the traditional approach were the delayed antivirus detection, scalability, and relevancy of signatures.
AI-based Antivirus solutions focus on detecting abnormal behavior by programs rather than matching the signatures. It helps to detect zero-day exploits and other previously unspecified malware.
Behavioral Model of the End User: Artificial Intelligence based solutions provide Cyberdefenses, that captures the behavior of users with their devices. It observes and rectifies the account takeover attacks.
For example: If your credentials are hacked and the hacker is trying to gain access to your account. AI-based algorithms will detect this account takeovers by sensing out the behavior of the hacker and will proceed with an account lockout.
Artificial Intelligence is re-evaluating every aspect of cybersecurity today. From upgrading the organization’s ability to predict data threats, attack breaching and protecting the proliferating number of threat surfaces with Zero Trust Security frameworks to make passwords obsolete, AI is essential in securing the perimeters of any business.
Humans are the most intelligent creatures in the world. The objective of Artificially intelligent systems is to function intelligently and independently, just like humans. At Jellyfish Technologies, we build super cool intelligent technologies based on a simple approach. Our engineers have worked on predicting stocks, weather, spam emails, and more using TensorFlow. We have adapted different predictive models like decision trees, linear and regression models and neural networks.