AI in Cybersecurity: Transformation is Now

ai in cybersecurity portnox

We live in a world where businesses suffer a ransomware attack every 40 seconds, and total malware infections have been on the rise for the last ten years. Withstanding such a heavy barrage of cyberattacks requires an intelligent and robust approach to cybersecurity. And as cybercriminals continue to sharpen their skills and sophisticate their techniques, safeguarding critical enterprise systems is only becoming more challenging.   

Luckily, artificial intelligence (AI) is increasingly playing a significant role in cybersecurity, offering powerful and efficient threat detection and elimination. And with artificial intelligence in the cybersecurity market expected to reach an eye-watering $46.3 billion by 2027, AI should be part of your strategic IT plan, if not already implemented in some areas.  

With this in mind, let’s look at how AI in cybersecurity is evolving and creating a safer world for businesses today and in the future.  

AI in Cybersecurity – Use Cases and Advantages 

Artificial intelligence and its subsets like machine learning and deep learning are vital to information security today. These technologies can rapidly analyze millions of data sets and uncover a wide variety of cyber threats. This section will dive into how AI is being leveraged in cybersecurity and its advantages.  

Threat Detection 

Perhaps the most significant use case for AI in cybersecurity is threat hunting. Typically, these systems use historical data, machine learning (ML), and statistical modeling of networks to create a baseline of expected network traffic. With the baseline established, the AI can rapidly identify anomalies and alert the relevant security teams of suspicious behavior. Critically, machine learning excels at spotting patterns in data that traditional approaches miss and can find these patterns much faster than human security analysts.  

Network Security 

Leading on from the last section, let’s look at some more specific ways AI is used in networking. Network security encompasses many elements, including network access controls (network authentication and authorization), perimeter security, data privacy, security monitoring, and policy management.   

With so many moving parts, maintaining strong network security can be challenging, but AI helps overcome much of this complexity. Additionally, AI makes enterprise networks more efficient by leveraging data-driven algorithms to identify critical patterns within the organization’s infrastructure. Here are the primary ways AI is changing network security: 

  • Log analysis: AI and ML algorithms can analyze logs from all over the enterprise environment, whether they be routers, switches, WAN optimization devices, or others, to provide real-time network performance insights. Armed with this information, network engineers can respond to problems at lightning speed.  
  • Traffic management and prioritization: Automated, AI-driven tools help manage traffic to optimize performance. AI built into smart switches can analyze Ethernet packets and automatically assign different priority levels to different types of network traffic.  
  • Scanning and patching: Many modern switches rely on AI to automate maintenance tasks, including patching. However, fully autonomous self-patching AI systems are also garnering more attention. These systems use AI to self-scan for vulnerabilities and deploy patches for these vulnerabilities without human involvement 
  • Supporting cloud-managed networks: Network architectures are increasingly moving towards centralized management structures like cloud-managed networks and Software Defined Networking (SND). AI can help fully realize the benefits of these architectures, offering increased ease of management and improved network flexibility 

In summary, AI helps improve network performance and reduce downtime and does this more accurately and quickly than a person ever could.  

Hunting Zero-Day Exploits (Identifying Unknown Threats) 

Defending against zero-day exploits is one of the most challenging aspects of modern cybersecurity. A zero-day exploit is a cyberattack targeting an unknown software vulnerability. Naturally, defending against something you don’t know exists presents significant hurdles. For example, the signature-based tools usually deployed by cybersecurity teams won’t be effective in catching Zero-days.   

AI, ML, and deep learning are increasingly being utilized to find relationships and patterns that human analysts and conventional security tools miss. Rather than using pre-defined criteria to identify anomalies, this type of AI is typically unsupervised and will teach itself what activity is expected within the organization. If it spots something unusual, like exfiltrating data to outsider servers or users visiting websites they have never visited before, it will flag this quickly. In some cases, these attacks will be cybercriminals exploiting Zero-day vulnerabilities to inject malicious software into the network 

Vulnerability Management 

A colossal 28,695 vulnerabilities were disclosed in 2021, a significant rise from the 23,269 disclosed in 20207. And alarmingly, more than 4000 of these flaws are remotely exploitable. But luckily, they’re also patchable. Security teams often struggle to keep up with the influx of new vulnerabilities and decide where to focus their efforts. But with AI scanning user accounts, endpoints, and servers for abnormal behavior, security teams get an in-depth insight into which flaws are most keenly targeted by cybercriminals.  

Threat Prioritization  

Depending on how sensitive an organization’s threat detection system is, security analysts could potentially receive an overwhelming number of threat alerts on any given day. In fact, a survey by Trend Micro found that 51% of IT security professionals said they were overwhelmed by the volume of threat alerts they received. Additionally, 55% of respondents said they weren’t confident in their ability to prioritize and respond to these alerts 

AI can help by leveraging machine learning to triage low-risk alerts, suggest solutions, and call for immediate attention to high-risk alerts. This means security analysts can spend less time manually combing through alerts and more time combating them.  

Reducing Pressure on the Cybersecurity Workforce 

AI reduces or entirely eliminates much of the manual labor involved in many cybersecurity tasks. The main drivers here are automation and AI’s ability to process copious amounts of data in minutes or even seconds.  

Wrapping Up 

While cybercriminals use AI to attack enterprise networks, we can use it to protect them. AI is emerging as a critical technology in the information security space and with good reason. It provides the analysis, speed, and detection needed to protect the dynamic enterprise attack surface. 

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