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Machine Learning and AI in Cybersecurity

By Elliot Anderson  |  September 28, 2022

Artificial intelligence (AI) and machine learning are positioned to assist today's enterprises as they fight to defend themselves against the rising number of cyber attacks. 

 

Real-time learning and analysis of potential cyber risks is made feasible by AI and machine learning. Additionally, they use computers to create behavioral models, employing these models to forecast cyberattacks as new information becomes available. By accelerating and improving cybersecurity responses, these technologies work together to help businesses strengthen their security defense. 

 

An Effective Tool for Combating Cyber Attacks 

Cyberattacks have increased as more firms adopt digital transformation strategies. According to the Identity Theft Research Center, 2021 has been a record-breaking year in the U.S., with the number of data breaches at the end of the third quarter surpassing all of 2020 by 17 percent. Likewise, ransomware assaults have been rising alarmingly, with the typical incidence costing businesses over $700,000. Today, a ransomware assault occurs every 11 seconds, causing a 21-day company outage average. 

 

AI and machine learning can guard against these advanced threats, which hackers are using to shut down business networks. In fact, these technologies are rapidly advancing into commonplace tools for cybersecurity experts in their continuing battle with malicious actors. 

 

61 percent of firms said they won't be able to recognize major risks without AI, and 69 percent think it would be vital to counteract cyberattacks, according to a survey by Capgemini Research Institute. In fact, it is predicted that the market for AI in cybersecurity would reach $46.3 billion by 2027. 

 

  • Finding anomalies: To find abnormalities that could be signs of an assault, AI and machine learning employ behavioral analysis and constantly changing parameters. 
  • Future data breaches can be predicted thanks to AI and machine learning, which allows for the processing of vast volumes of data of various kinds.
  • Real-time data breach response: AI and machine learning can deliver notifications when a cyber danger is discovered, or they may act independently without human assistance by automatically generating protective patches as soon as an assault is discovered. 

 

Benefits of AI and Machine Learning 

AI and machine learning are having a significant positive impact on cybersecurity programs at organizations. These consist of: 

 

  • Increasing the speed of detection and response: AI and machine learning are far faster at identifying dangers than humans because they can quickly examine vast volumes of data. Furthermore, they may rapidly improve reaction times by applying fixes and removing threats in almost real-time. Todays cyberattacks may swiftly infiltrate an organization's infrastructure; thus, success depends on having razor-sharp detection and reaction times. 
  • Lowering IT costs: AI and machine learning are efficient technologies because they need less work to detect and address cyber risks. According to the Capgemini analysis, the average cost reduction is 12 percent, with some firms achieving cost reductions of more than 15 percent. 
  • Increasing cyber analyst effectiveness: By reducing the amount of time needed to manually go through data logs, AI and machine learning lighten the strain for cyber analysts. These systems can notify cyber analysts of an assault while categorizing the attack's nature, better enabling them to respond appropriately. Cyber analysts are more capable of handling even the most complicated risks with less manual work when behavior patterns are continuously and thoroughly analyzed. 
  • Improving your overall security posture: As more data is reviewed and these systems learn from previous patterns, cybersecurity grows stronger with time as they become more adept at spotting questionable activities. Additionally, they safeguard an organization's infrastructure on both a global and micro level, erecting barriers that are more effective than those made by manual techniques. 

 

Potential Uses 

Although there are risks associated with AI and machine learning, their usage is only anticipated to grow in the future. These technologies have already shown themselves to be quite successful in a variety of application scenarios. The following are some typical use cases where businesses are effectively utilizing AI and machine learning: 

 

  • Rapidly detect intrusions: AI is now being used by businesses to automatically and precisely identify criminal activities. Organizations are responding to incursions as soon as they happen because to machine learning's capacity to identify, evaluate, and defend against cyber threats in real-time. 
  • Identifying suspicious behaviors: To identify questionable user activity, AI and machine learning are also applied. Organizations use machine learning to differentiate between typical behavior and aberrant behavior that may be the sign of a cyber attack in order to fix vulnerabilities until a data breach occurs. This is done by monitoring users' unexpected actions, such as when they log in at odd hours of the day or download an unusually high volume of data. 
  • Detecting fraud: Many businesses use machine learning algorithms to anticipate anomalous client behavior in order to protect themselves against financial fraud. These technologies are assisting companies in identifying potential fraud risks before they materialize, hence minimizing their financial losses. They do this by having the swift capacity to identify if client behavior is abnormal.
  • Discovering malware: Organizations are using AI and machine learning to forecast malware outbreaks in the future. Cyber analysts can foresee malware assaults and quickly mitigate the danger using machine learning, which uses patterns discovered in past infections. 

 

Planning Your Implementation 

It may be tough to know where to begin when integrating AI and machine learning into one's cybersecurity strategy, which is why many firms find it problematic. As you start implementing your implementation strategy, keep the following advice in mind to get the greatest results: 

 

  • Employ competent cyber analysts who are well-versed in the use of AI and machine learning and decide which jobs you want to automate and which you'd rather have handled by people. 
  • Ascertain that you have the data sets required to start employing AI algorithms, that this data is accurate and current, and that it is properly linked with your applications and infrastructure. 
  • Set defined success criteria for these test projects, starting with just one or two use cases that are simple to deploy and provide real value for your business. 
  • To validate, rank, and assess possible risks, create a clearly defined methodology. 
  • Create control mechanisms to aid you in recognizing when an AI system deviates from expected behavior so you can quickly resolve any problems. 
  • As you integrate AI and machine learning into other aspects of your cybersecurity strategy, evaluate the outcomes of your test projects and make any required adjustments. 

 

Powerful Tools for An Escalating Problem. 

AI and machine learning are potent tools that may aid firms in becoming more prepared as the volume and sophistication of cyberattacks rise. Your firm can identify and respond to cyberattacks in real-time with the correct technologies in place, while also resolving potential risks before they become major problems. As a consequence, you can better manage the pace and scope of today's risks and discover threats sooner, for less money, and with a security posture that is stronger. 

 

How Lumifi Can Help 

We not only utilize the industry’s leading threat intelligence platforms, but also deliver personalized security recommendations through scheduled calls with a dedicated Engagement Manager. Our suite of services allows you peace of mind knowing your organization is being monitored around the clock by an industry leading SOC which takes pride in its customers' security.   

 

By Elliot Anderson

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