When it comes to providing cybersecurity experts the right tools they would need to take timely action, machine learning and automation can make a big difference.
A majority of businesses, especially the ones working in the online space, work with a massive amount of data which becomes too much for humans to process and safeguard in a limited timeframe.
Additionally, a mass level lack of automated contextualization requires manual follow-ups for taking remedial actions. For example, due to the absence of AI in cybersecurity, SOCs end up depending on the admin team to block the malicious connections, the IT support team for isolation of the host, and the mail team to delete hacked mailboxes.
In the current state of multi-cloud solutions, companies work with a diverse set of security toolset and legacy data centers, something that calls for automation-powering AI cybersecurity solutions. Solutions that would pull information from all these diverse platforms and give a comprehensive view of the organization’s security outlook.
As the graph suggests, artificial intelligence in cybersecurity has become the need of the hour for SOC analysts as it would provide them with increased visibility of threats across cloud and on-premise environments and ultimately investigate compliance-based risks better.
This need of the hour is defining a new role of AI in cybersecurity both on a holistic level and specific to SOCs. Let us look into the details of the use of AI in cybersecurity next.
The Impact of AI on Cybersecurity
Machine learning and AI have become essential to security as they are capable of analyzing millions of data and tracking a multitude of cyber threats. What works best for the technology is that they continually improve by learning about past data. Let’s look into some benefits of using AI for cybersecurity.
Automate Repetitive Tasks
AI-based cybersecurity eliminates the need for constantly updating and deploying security software, learning security skills, and backing data manually. AI-powered cybersecurity platforms enable businesses to do tasks such as compliance monitoring, incident response, etc. on auto-pilot. This level of automation offers a range of benefits to businesses –
- Correlating data
- Speedy creation of protection against threats
- Detection of infections in the system
Monitor, Identify, and Reply to Cyber Threats
The use case of AI for cybersecurity can be seen in the technology analyzing users’ behavior and inferring a pattern to identify abnormal deviation in business. This enables detection of vulnerable areas in the system and their quick redressal to prevent any future attacks. Moreover, machine learning is trained on a number of malware events that can lead to a proactive detection and prediction of malware which could infiltrate the IT network.
Track User Behavior and Activity
Understanding how business users and employees work is necessary for the identification and mitigation of anomalous behavior which can be harmful. By using AI for cybersecurity, SOC analysts can take multiple actions like tracking users’ daily behavior and activities on the business network. In turn, the AI system can develop an understanding of the behavior patterns to then detect irregularities and highlight malicious files, infected hosts, and compromised user accounts.
With bots forming a major part of the internet traffic, they can become a real menace. When left uncontrolled, it can lead to account takeovers and data fraud – something that cannot be managed alone with manual response. Through the incorporation of artificial intelligence in cybersecurity, businesses can create a detailed understanding of their website traffic and differentiate between good bots, bad bots, and humans.
Predict Breach Risks
AI cybersecurity solutions help determine the IT inventory which is an accurate record of users, devices, and applications with multiple access levels. Now considering the asset inventory and threat exposure elements, AI-powered cybersecurity can predict the chances of business systems getting compromised, leading to timely planning and allocation of resources towards addressable vulnerabilities.
With remote working becoming the new normal, updating legacy systems and creating hybrid networks and platforms have become a business necessity. Employees using cloud-based apps for work have extended business security practices beyond the typical “four walls” of a company. In order to strengthen this multi-location working environment, endpoint security resources are needed for managing transactions, communications, applications, and connections.
AI in cybersecurity use case in this context can be seen in SOC analysts using the technology for supporting, reaching, and scaling across these endpoints while creating a correlation between prospective threats to understand how a threat could impact other resources.
Incident Detection and Response
Another set of AI in cybersecurity examples can be seen in the technology’s capability to differentiate and prioritize different types of threats and distribute notifications accordingly. This can take multiple shapes from the automation of ticket creation and addition of pertinent remediation information to the detection of malware presence even before the malicious file or email is opened.
AI-based cybersecurity software aimed at incident detection and response doesn’t just lower dwell time and speed up the time to repair but also gives businesses the capability for taking proactive and preemptive measures.
In order to gauge the role of AI in cybersecurity, it is critical to look into the real-world examples of projects centered around the implementation of artificial intelligence in cybersecurity.
Real-World Examples of Using AI for Cybersecurity
The set of AI in cybersecurity use cases that we covered above have multiple real-world examples to support them. Let us look into the top ones of them next.
- Google uses AI for analyzing mobile endpoint threats and protecting the growing number of mobile devices. MobileIron and Zimperium also announced their collaboration for helping businesses adopt AI-based mobile anti-malware solutions.
- Cognito gathers and stores network metadata and builds upon it with unique security insights, which is then used for detecting and prioritizing attacks in real-time.
- Another set of AI in cybersecurity examples can be seen in Darktrace Enterprise Immune System, based on AI and ML, models the behaviors of every user, device, and network to study specific patterns and automatically identify anomalous behavior to then alert companies in real time.
One of the biggest challenges that the SOCs face today is the detection and response to futuristic attacks in a proactive mode. Modern day hackers have become smarter at launching attacks to a compromised system using next-gen technologies like deepfake and generative AI. The blind spot that this situation creates calls for a strategic adoption of AI cybersecurity solutions.
The first step of automating your enterprise security through AI based SOCs lies in understanding which functions should be automated.
Here’s an infographic listing the different IT functions that a business should automate using AI for cybersecurity.
How does Appinventiv Approach SOC Automation through AI in Cybersecurity?
At Appinventiv, when we work on enterprise-grade use cases of artificial intelligence in cybersecurity, the primary intent is to automate systems. After we have brought all the above mentioned security functions into an auto detection and notification mode, the next step is to implement AI for cybersecurity with a focus on SOC software.
Here’s what our typical implementation plan looks like when we work on a project as an AI cybersecurity provider:
- Conducting a needs assessment for identifying organization’s SOC requirements.
- Building a software that would meet the business-specific needs of AI in cybersecurity in-house.
- Integrating the new AI cybersecurity solution with the existing security infrastructure.
- Creating some detection/response playbooks to be used as a business standard.
- Testing the system against accuracy and proper functioning.
- Developing process and policy for using the technology and measuring the impact of AI on cybersecurity.
- Monitoring system’s performance and making adjustments wherever required.
- Documenting the AI/ML algorithms used alongwith the implementation steps.
- Preparing a detailed report of the AI cybersecurity project’s method, results, and recommendations for further improvements.
- Evaluation of the system’s effectiveness against monitoring and response to security incidents.
In our extensive AI development company experience of working with new-gen technologies like artificial intelligence, IoT, blockchain, etc. we have helped several enterprises ranging from SaaS companies to manufacturing units, and fintech businesses implement the use of AI in cybersecurity on a mass scale, with the assurance of predictive detection and on-time addressal. Get in touch with us to build your AI cybersecurity solution.
FAQs around Artificial Intelligence in Cybersecurity
Q. How does cybersecurity AI work?
A. Artificial intelligence in cybersecurity works by analyzing millions of events, user behavior patterns, and types of threats to identify potential attacks in real-time. Additionally, it combines the capabilities of IoT, machine learning, and blockchain to create a transparent, real-time ecosystem that would notify the stakeholders of malicious events.
Q. How does AI affect cybersecurity?
A. The impact of AI in cybersecurity can be seen in: automation of repetitive tasks, establishing correlation of data, speedy creation of protection against threats, detection of infections in the system, tracking user behavior and activity, fighting bots, predicting breach risks, landscape analysis, incident detection and response.
Q. What are some examples of AI in cybersecurity?
A. The examples of artificial intelligence in cybersecurity can be seen through – breach, phishing, and malware detection, spam filtering, bot identification, thread intelligence, vulnerability management, incident response, fraud detection, and network segmentation.