In today’s constantly evolving high-tech business environment, business leaders require reliable information to set high standards and enhance security protocols and keep sensitive information safe. Decision-making AI techniques provide insights that help organizations better understand the threat and perform actions accordingly. Moreover, artificial intelligence and machine learning techniques play a crucial role in the enhancement of cybersecurity protocols.
Lately, we’ve been witnessing an unprecedented level of vulnerability in the companies’ security systems resulting in a massive amount of data breaches and business network disruptions. According to Norton, the standard recovery cost from frequent data breaches is approximately $3.86 million. The same research reveals that companies need approximately 196 days for the identification of data breaches.
We provide companies with senior tech talent and product development expertise to build world-class software. Let's talk about how we can help you.Contact us
Below are the top 5 ways how artificial intelligence is reshaping the cybersecurity field significantly.
Table of Contents
The incorporation of artificial intelligence and machine learning in cybersecurity allows identifying and taking actions against suspicious events, preventing business disruption effectively. Organizations aim to provide a safe environment by automating the security functions and minimizing or eliminating human assistance (it is particularly important for complex tasks that are practically impossible to attain without the help of AI).
Group CIO at McLaren, Karen McElhatton briefly explains that
“It would be a myth saying that data exists only in the form of bits and bytes. Data exists in numerous other formats such as videos, emails, chats, etc. A variety of inputs are coming from individuals, resulting in data evolution.
It is very much for human beings to be capable to manage and automate tools for the monitoring of such suspicious activities which are non-negligible.”
Artificial intelligence algorithms can also help monitor employees actions. As is known, some employees tend to acquire shortcuts which can possibly lead to a security protocol disruption. AI-powered cybersecurity tools generate immediate alerts whenever they encounter suspicious activity, creating an efficient detection and fraud prevention system. Therefore, thanks to AI and ML, the cybersecurity infrastructure can be enhanced essentially. At the same time, the organizations should realize that cybersecurity tools cannot replace human security staff completely.
Traditional network security techniques mainly focus on two major aspects which are addressed by AI-powered cybersecurity measures:
Security policies help identify legitimate and malicious network connections. The appropriate policies can also define the types of traffic which cannot be trusted at all. However, it is quite challenging to create, manage and maintain such policies for a massive number of networks.
Numerous organizations do not have precise naming conventions for workloads and applications, which leads to the necessity for security teams to spend a lot of time and energy to determine what group of workloads belongs to what applications.
Artificial intelligence and asset intelligence plays an important role in enhancing network security protocols by learning network traffic patterns and recommending functional workload together with security policies. Also, Artificial intelligence can optimize and monitor critical data center processes.
In 2019, 20,362 new data vulnerabilities were encountered, – 17.8% increase compared to 2018. Organizations are struggling with the management and prioritization of new vulnerabilities on a daily basis. Traditional vulnerability management methods tend to respond and nullify high-risk vulnerabilities only after cybercriminals try to exploit them.
There is no doubt that machine learning and artificial intelligence can enhance vulnerability management and database security. In addition to that, innovative tools such as User and Event Business Analytics (UEBA), when integrated with AI algorithms, allow analyzing user behavior on servers as well as endpoints for the detection of anomalies that might indicate an unknown attack. This helps organizations protect themselves against suspicious attacks even before the vulnerability has been discovered or reported.
AI-powered identity verification solutions and implementation of data security measures play an influential role in the fight against cybercriminals. In the wake of the technological revolution, identity theft became one of the most frequently encountered security issues. Cybercriminals can utilize numerous ways to get unauthorized access to sensitive data of individuals. Since the hackers’ community is growing at an alarming pace, more and more network disruptions and unauthorized accesses occur. Doubtlessly, AI-powered solutions can help companies from almost every sector, whether it is finance, healthcare, education or travel industry.
Identity verification solutions include face verification, address verification, biometric authentication, and compliance solutions. In addition to that, applying preventive measures such as changing passwords frequently, thinking twice before clicking on a random referral link can also help fight fraudulent activities.
The most frequently used part of artificial intelligence, machine learning, has moved from hype to mainstream already, especially when it comes to the mobile devices. The application of machine learning became particularly noticeable in the past decade when the widespread mobile phone usage became a new normal. For example, enhanced voice recognition features are incorporated in mobile phone applications such as Alexa from Amazon and Siri from Google.
Among mobile devices there was also observed the enhancement of the security protocols. Enterprises are constantly looking for new opportunities for the protection of a rapidly growing number of different mobile devices. For example, a tech giant Google is adopting machine learning and artificial intelligence algorithms for threat analysis against mobile endpoints.
In October 2020, Zimperium and MobileIron announced that they would be collaborating to assist enterprises in improving mobile- anti-malware solutions with the incorporation of machine learning. MobileIron claimed that it would be consolidating Zimperium’s artificial intelligence and machine learning-based threat detection with MobileIron’s security engine. After integration, they would be selling their joint solution to address complex challenges such as device detection, unauthorized access to the network, and application threats. With the better understanding of existing challenges, organizations would be able to take immediate actions against occurring threats.
Since hackers are never going to stop, organizations must stay one step ahead to combat tech-savvy criminals. Today companies need to invest and enhance their cybersecurity practices of detecting the identity theft by incorporating AI-powered cybersecurity protocols. Modern security operations centers (SOCs) have invested millions of dollars without giving it enough thought on how to prevent data loss and unauthorized access.
Undoubtedly, predictive artificial intelligence paves the way for modern and evolving SOCs. AI-powered cybersecurity solutions address the fundamental requirements of identity theft detection platforms, UBA, and SIEM with the additional advantages of self-learning and predictive artificial intelligence. Third-wave AI provides proper and real-time protection for network assets in the cloud and across numerous connected devices. Hence, it’s high time for organizations to start incorporating all the mandatory identity verification solutions to combat illicit money transfers, unauthorized access, and fraudulent activities.