We all know that AI is the new big thing in the technological world. When it was introduced, handling and securing data was a major concept. However, with its advancement, AI started being used for security operations.
In this blog, we are going to understand AI in cybersecurity, including its definition, benefits, use cases, evolution over time, and threat detection and response.
So, let’s get started and understand all within minutes.
AI in Cybersecurity: A Brief Definition
When AI models are used to collect, analyze, and correlate data to provide security insights, they are known as AI in cybersecurity. However, AI's scope extends even further in the cybersecurity domain. It's also used to monitor the entire infrastructure and take appropriate actions in case an exploit, data breach, or illegal activity is detected.
In short, you can say that AI in cybersecurity is mostly for easing, automating, and streamlining the tasks of a cybersecurity team. You can refer to it as primary support, but it is not an alternative to the professionals.
How Does AI Evolved for Cybersecurity?
In recent decades, AI has evolved in three phases, as listed below:
Phase 1: The Millennial Era
In the '90s or the beginning of the 2000s, AI or ML was not even developed. Then, the security teams were only using detection and alert systems. And slowly, they transformed into detection, prevention, and mitigation solutions.
Phase 2: The 2000s
In the early and mid stages of the 2000s, machine learning came into the limelight. Security professionals started utilizing it to analyze large amounts of information. It helped to make insights that sometimes get missed through manual mechanisms available. As a result, security policies and configurations were strengthened.
Phase 3: The GenZs Era
Now, the current era is where high-processing systems, top-notch algorithms, and well-experienced and educated professionals work together. In this era, AI has evolved to the extent that it can single-handedly perform the work of tens of analysts. In addition, it can communicate with multiple systems to analyze logs, correlate evidence, and perform mitigation techniques.
The Benefits of AI in Cybersecurity
The top benefits that AI implementation can offer in cybersecurity operations are listed below.
#1: Automates Repetitive Tasks
To ensure the prevention of cyber-attackers and maintain data integrity, security professionals have to collect, analyze, and manage multiple repetitive tasks. Due to this, sometimes their time and efforts are wasted, and other priority tasks get paused. But, AI automates such time-consuming tasks, leading security experts to complete the work needed most.
#2: Better Situational Decision Making
A security analyst is loaded with data from multiple sources, which makes it complex to make appropriate data-driven decisions. However, with the support of AI, data collection and analysis are streamlined. It helps the security teams to quickly conclude the required information and take further action within minimal time.
As a result, the attacks are detected in the early stages, and data integrity is maintained.
#3: Faster Threat Detection
Whether it's an SIEM or XDR solution, manually analyzing their thousands of logs with utmost accuracy is not possible. There can be a possibility of missing an anomaly or links between multiple different activities that can exploit the organization's system.
AI helps to eliminate such possibilities and detect abnormal behaviors much faster than a security analyst. In addition, it can also correlate the activities, providing a better insight to prevent breaches and exploitations.
#4: Streamlined Analysis and Reporting
Sophisticated and complex cyber-attacks are planned in accordance to evade detection. For such purposes, the attack components move across applications, files, infrastructure, and devices. Due to this, it becomes a time, effort, and resource-consuming task to manually discover attacks.
But, as you know, AI can undergo extensive data within seconds, making it a primary pillar in supporting threat analysis. Also, it can be used to provide reports in a defined manner, helping to take relevant mitigation actions before any malicious activity.
Top Use Cases of AI in Cybersecurity
The following are the top use cases of AI in cybersecurity, which will help you understand where AI can be implemented in an organizational infrastructure.
#1: Endpoint Security
End-users use a variety of devices, such as a Windows system, Mac desktops, and Chrome books. Managing the security of all such operating systems and devices is a tedious task, which is streamlined by artificial intelligence.
AI detects the end devices and scans them to ensure that their security configurations are meeting the required standards. In addition, AI can self-update itself with the latest protocols and install relevant patches on end systems to ensure data protection.
#2: Identity and Access Management
In IAM (Identity and Access Management), AI plays three primary roles, as listed below:
- It learns users' sign-in behavior to quickly detect anomalous patterns.
- It's used to force the configuration of multi-factor authentication in certain circumstances.
- It’s utilized to block and unblock users according to the sudden change in their activities per defined responsibilities. Although all these use cases are for systems handling sensitive or confidential information.
#3: Threat Detection and Response
The two most important solutions, namely SIEM (Security Information and Event Management) and XDR (Extended Detection and Response), are highly dependent on artificial intelligence. AI supports such applications to monitor email services, end devices, user patterns, and identities.
In addition, AI also helps aggregate signals across enterprise infrastructure, which provides better visibility to every business operation.
#4: Network Security
For network security, AI utilizes deep learning and machine learning techniques. It monitors the flow of packets and frames between the routers, switches, access points, and end-user devices. If it finds an abnormal flow, such as a sudden surge of ICMP, BPDU, or any other such packets, the security team is quickly informed with all essential information.
In addition, AI also helps to ensure that there are no rogue devices in the network and that all systems reside in their defined area, autonomous system, and virtual LAN.
AI-based Prevention and Detection of Cyber-threats
With the quick and extensive adoption of digital solutions by every small, medium, and large organization, the requirement for always-active security mechanisms has also increased. However, with traditional approaches, it's not possible to meet the current objectives. And that’s where artificial intelligence helps. However, big data and machine learning capabilities are also needed to support core operations.
With AI in cybersecurity, the four primary prevention and detection aspects are beneficial.
#1: SOC Operations
With the help of AI, SOC operations are streamlined, and efficiency is increased. The AI models are capable of monitoring multiple networks and hundreds of devices simultaneously. Also, they can work 24/7, aiding to minimize response times and alert volumes.
#2: Security Advancement and Innovation
The extensive data available online and offline can be provided to an AI model. It'll help the organization to avail itself of additional security insight that can help align with the latest standards. In addition, AI can process the data in such a way that it can help to create a single robust multi-layer security architecture, helping to prevent network, cloud, endpoint, web, and all other threats.
#3: Training and Development
Training and development are core pillars for strengthening an organization's security. AI can analyze the performance and pattern of security professionals and suggest they undergo relevant training. In addition, it can help you create a custom training module for your enterprise, helping to prevent cyber-attackers and data breaches.
Wrapping Up
AI in cybersecurity is an advanced concept, aiding automation, threat detection, innovation, and analysis, and all other security operations. The artificial intelligence models are considered to be a great support for maintaining data integrity and confidentiality. However, its primary usage is found when working with SIEM, MDR, and XDR solutions. Concludingly, AI in cybersecurity is a game-changing technology that can assuredly make an enterprise superiorly secure.
Top comments (2)
Good explanation 👍👍
Thank You @chahat_arora_0212