According to a recent market research analysis, the market for artificial intelligence in cybersecurity will rise at a CAGR of 24.2 percent and reach $66.22 billion by 2029. The market is split into machine learning, natural language processing, and context-aware computing, depending on technology. During forecasting, the machine learning technology segment will likely hold the most significant market share for artificial intelligence in cybersecurity. The significant market share of this sector is due to its advanced potential to gather, analyze, and handle big data from many sources that enable quick analysis and prediction. It also helps identify risks and conduct real-time user behavior analysis to defend against cyberattacks. Process behavior analysis with Microsoft Defender 365 is an excellent real-world use of AI and ML in cybersecurity. It uses machine learning and behavior-based detections to determine when endpoints need to be fixed and then performs the necessary actions automatically without requiring any human intervention. In the past decade, the cybersecurity industry has emerged as one of the leading technological areas, utilizing artificial intelligence and machine learning in many applications, including detecting spam emails, network traffic analysis, facial recognition, and more. AI and ML can revolutionize cybersecurity, thwart ransomware attacks, and learn from data trends to forecast potential attack scenarios and methods. Today’s attacks are more organized, faster, and more precise than ever before, typically relying on machine-to-machine interaction. AI and ML offer the highest possibility of keeping up with the flood of cyberattack attempts while accelerating innovation to deceive attackers who are constantly scaling up their efforts. ProjectPro Machine Learning Project is the best way to learn more about Machine Learning and Cybersecurity.
Cybersecurity and its sub-branch, machine learning, have poised human life at a much deeper level. According to CEOs and visionaries, AI will transform how humans use to interact with technology and information. Various industries, including the cybersecurity industry, are also leveraging the benefits of artificial intelligence and machine learning technology for deploying comprehensive, feature-rich security solutions for the organization. Since cyberattacks are getting sophisticated and capturing more news headlines, so cybersecurity researchers and corporate professionals should automate security measures with more detailed security. That is where artificial intelligence can save the day. This article will give you a comprehensive view of all the emerging trends and growing roles AI and ML brings in the field of cybersecurity.
Emerging Trends of AI and ML in Cybersecurity
- AI will reduce the burden of cybersecurity professionals’ shortage: With the advent of more remote work culture, many employees are working from home remotely. Although this significantly reduces companies’ employee-related costs and office expenses (such as electricity bills), it brings tremendous concerns for cybersecurity departments. Such a gap in the network’s continuous monitoring process and other security activities often lurk attackers to attack those systems. But with artificial intelligence and machine learning techniques, enterprise systems can reduce cyber threats and eliminate the shortage of cybersecurity professionals. In fact, AI and ML algorithms that got training with accurate data sets can work more accurately compared to humans and security employees.
- Detecting new threat signatures and attack patterns: All modern security solutions like Identity and Access Management (IAM), firewalls, anti-malware, threat detection systems, etc., leverage artificial intelligence and machine learning algorithms wherein these solutions understand cyber threats from previous threats and data fed to them during training. Researchers and AI engineers design these solutions so that they can constantly run pattern recognition and threat detection on every activity that comes under the responsibility of these apps. Thus, even the minute illicit behaviors get detected and notified to the IT department or security professionals as an alert. Then, the security professionals can take further actions to allow or deny those activities.
- Battling and preventing network systems from bots: Today, bots are responsible for creating a massive amount of internet traffic. Cybercriminals and attackers use them for various malicious purposes, like creating botnets by infecting victims’ systems with malware and making their systems zombie computers. Other attackers use bots for sending spam emails or performing massive DDoS attacks. Professionals cannot stop these threats through manual approaches. Thus artificial intelligence and machine learning algorithms help defend our systems against such threats. Google uses spam detectors in their Gmail systems that automatically filter out spam emails and unnecessary or malicious email-based ads. By looking at the behavior and patterns previously encountered, AI algorithms can identify the bots and remove them from damaging the network or website’s standard workflow.
- Emphasis on data security compliance and regulations: As we all know, this is a data era, and data is the new currency. Therefore, data is valuable. Thus, organizations should protect business data & customers’ valuable data. Often cybercriminals & adversaries try to steal those data and sell it on the dark market or dark web. To prevent such threats, security professionals incorporated AI and ML-based automated scripts and programs to minimize data breaches and handle the consequences of such breaches. Also, since data privacy regulations like GDPR, HIPAA, CCPA, etc., have been thoroughly assigned to all enterprises and businesses, enterprises should leverage AI algorithms and programs to save themselves from heavy penalties in case of data breaches and losses.
- Better end-point and cloud protection with risk prediction: As the number of devices for the remote workforce or on-premise employees increases, the chance of exposure to cyber threats increases proportionately. To provide dynamic security or to understand a threat before it happens, security professionals prefer to deploy threat detection and response systems that leverage AI systems to gauge the threat or predict whether it is an attack or not using machine learning algorithms. Organizations are also using similar algorithms for dynamically checking the cloud infrastructure and analyzing threats across multiple cloud environments using AI algorithms.
- Hyperautomation through SIEM and SOAR: Machine learning can leverage security orchestration, automation, & response (SOAR) tools, along with security information & event management (SIEM), to enhance intelligence gathering, threat detection, and automating security response features for better enterprise-grade security. All the legacy business process automation that includes security flavor can be made autonomous through machine learning models trained with accurate data sets. Again, many industries and factories leverage IoT and smart devices to automate multiple tasks. Modern SOAR and SIEM tools leveraging machine learning models can also pinpoint defects while analyzing network traffic and complex architectures with multiple smart systems incorporated.
AI Adoption Opening Up Increased Opportunities in Cybersecurity
Besides advancements in various security solutions, AI has also introduced new roles and job profiles for cybersecurity professionals. To preserve proper data security and privacy, companies hire for roles like data governance specialists, data privacy professionals, information security engineers, etc. For developing security solutions and plugins in-house, companies are hiring full-stack developers and AI security engineers with proficiency in machine learning, data analysis, and deep learning. For cloud security, organizations hire cloud security architects with specializations in ML and AI. Some early adopters of AI and ML in cybersecurity are Google, IBM Watson, Juniper Networks, Amazon, Cisco systems, etc.
As most enterprises use various tools and technologies across their working ecosystem, they get inundated with multiple external and internal threats. These threats target networks, databases, employee systems, customer data, compromising business plans, or web applications. To defend today’s modern cyber threat landscape, enterprises should deploy AI algorithms and automated ML programs that can spontaneously track such threats, protect systems from them and notify them about such threats before they start damaging.