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AI in CyberSecurity: A Problem Solver

Posted On August Tuesday 23, 2022

AI in CyberSecurity: A Problem Solver

Data protection is playing top priority to any organization be it small to large enterprises. Norton says that global cost to recover from a typical data breach is USD 3.86 million. According to IBM, it takes 287 days to identify a data breach and average time to contain a breach was 80 days.

Updating existing cybersecurity solutions and enforcing every possible applicable security layer doesn’t ensure that your data is breach-proof. To reduce the recurring task for security compliance check and audit reports, AI can be used to avoid both financial losses and waste of time. 

In recent years both the business and regulatory environment have become less predictable and more volatile. Artificial intelligence (AI) is emerging as a next major wave of innovation. This means that CISOs and security professionals will need to quickly get up to speed on AI-driven cybersecurity solutions. 

How AI can drive Cybersecurity solutions?

With fast-evolving cyberattacks and rapid multiplication of devices happening today, AI and machine learning can help to keep abreast with cybercriminals, automate threat detection, and respond more effectively than conventional software-driven or manual techniques. Let’s have a look at common factor involved in driving the Cybersecurity solutions with Artificial Intelligence and Machine Learning.

Automation in detecting security breach

With the help of machine learning technology and Artificial Intelligence (AI), it enables organizations to identify cybersecurity attack and threats quickly and search the potential risky links and eliminates the manual errors. AI can adapt and learn the pattern of errors and risk with the help of Machine Learning. In the present world, ML has made it possible for machines to learn by themselves. This means that they are able to create pattern models with no dependency towards the human. AI can process huge amounts of data without manual errors and risk, for example, suspicious links, ransomware, unknown file extensions, before initiating suitable remedies. 

Breach Risk Prediction 

Accounting for IT asset inventory, threat exposure, and controls effectiveness, AI-based systems can predict how and where you are most likely to be breached, so that you can plan for resource and tool allocation towards areas of weakness. Prescriptive insights derived from AI analysis can help you configure and enhance controls and processes to most effectively improve your organization’s cyber resilience. 

Round the clock suspicious activity detection 

Relying over the human to identify and find suitable solution for any suspicious activity is a tedious task to perform, as they work in a timely manner and will take time to resolve in their specified time. On the other hand, AI technologies are capable to run the detection process round the clock. AI is improving the process and duration of time it takes to identify suspicious activities over the website. For instance, Google has warned 20,000 websites that they might be hacked and injected with JavaScript redirect malware. Also, according to Google Safe Browsing against the top 1 million websites, 621 of them are blocked by Google Safe Browsing for malware. This means that there are approximately 20,000 websites which are affected with a malware and leveraging AI will be a boon to developers to identify threat on the website round the clock 24/7. 

Improved monitoring and analysis 

As we all know that cybersecurity industry growing exponentially to protect the data, networks and hardware for the organizations to operate and store data over a secured vault. To bring in an improved monitoring and analysis process toward these threats, AI offers greater visibility to the organizations within security environment company-wide. A well-designed AI-powered hunting technology will help organizations in building a strong security check for monitoring any suspicious activity over the intranet or organizational network. 

Secured authentication process 

Secured authentication is an early and most important layer of defense for business data. In order the enhance the layer of defense for authentication process, AI can assess multiple factors and weighs to come with a risk score for the login attempt. For example, certain IP addresses for attempt to login by a user during the certain time period, say midnight might indicate a potential risk. Some of the potential risk factors which will be assessed by AI tools to determine the authentication process are checking the network IP and reputation, analyzing the user’s geographic location and finding the pattern, analyzing the device fingerprint and time of login attempt, in order to give authentication to users for entering the portal. 

Automated responses 

According to O’Reilly Media, Organizations that use security tools with artificial intelligence (AI) and machine learning (ML) see a significant decrease in incident response time. 84% of survey respondents who use ML and AI security services said their response times are within minutes or hours. It means that AI can process multiple unstructured data to provide insights, can understand and learn the patterns and provides an automated response, making it quicker to stop cyberthreats. 

Error-free Cybersecurity solution 

While using AI for performing repetitive and mundane task over a regular cycle, the tools don’t get bored like humans and provides an error free report to the cybersecurity analyst. Nevertheless, human do need to work with these AI tools to configure and optimize the performance as per their requirements. We can’t argue with the fact that humans offer the common sense that machine lack in a standard condition. However, AI can perform better task in a non-standard condition. 

Threat Exposure  

Hackers follow trends just like everyone else, so what’s fashionable with hackers changes regularly. AI-based cybersecurity systems can provide up to date knowledge of global and industry specific threats to help make critical prioritization decisions based not only on what could be used to attack your enterprise, but based on what is likely to be used to attack your enterprise. 

Controls Effectiveness  

It is important to understand the impact of the various security tools and security processes that you have employed to maintain a strong security posture. AI can help understand where your InfoSec program has strengths, and where it has gaps. 

IT Asset Inventory 

Gaining a complete, accurate inventory of all devices, users and applications without any access to information systems. Categorization and measurement of business criticality also plays big role in inventory. 

Incident response  

AI powered systems can provide improved context for prioritization and response to security alerts, for fast response to incidents, and to surface root causes in order to mitigate vulnerabilities and avoid future issues. 


Key to harnessing AI to augment human InfoSec teams is Explainability of recommendations and analysis. This is important in getting buy-in from stakeholders across the organization, for understanding the impact of various InfoSec programs, and for reporting relevant information to all involved stakeholders, including end users, security operations, CISO, auditors, CIO, CEO and board of directors. 

On a summarized note, we can say that AI tool is the new normal to fight with the cybercrimes. Major role of AI in cybersecurity is to offload manual work and to handle repetitive task that humans can’t tackle fast and accurately. Above mentioned examples in the blog are that how AI can help and manage the cybersecurity for a company in forthcoming days. To look forward with the implementation of AI with Cybersecurity for the organization, why don’t you send the requirements with us a hello@techcloudpro.com