Find out how alert fatigue affects your team and how AI-Assisted Cybersecurity can help reduce it
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How Alert Fatigue Impacts Cybersecurity


Alert fatigue is a common phrase in the world of cybersecurity. Between the blaring alarms and the feeling of being overwhelmed an inadvertent psychological response is caused that desensitizes individuals. It becomes difficult to distinguish between legitimate and false alerts, while also conditioning analysts to find ways to tolerate the fatigue, normalize it and eventually begin to ignore alerts.


In order to combat this, SOCs and CISOs need to lean into Decision Intelligence tools to augment their existing teams and address the painful resource issue in this area. 


What is Alert Fatigue? 

Alert fatigue, on one hand, results in a cognitive overload due to the amount of work, the complexity of the work, and the effort between distinguishing the legitimate from the false alerts. Consequently, the more you’re exposed to something, the easier it becomes to find ways to tolerate it, normalize it and eventually begin to ignore it.

What it becomes is essentially the fable of the boy who cried wolf, brought to life, with far more unfortunate outcomes. 

Cybersecurity is no different. Every day a malicious actor is looking to steal data or transfer funds via a cybersecurity intrusion. Each attempt triggers an alarm, but not every alarm is heeded.


The Effects of Alert Fatigue

This leads to a lot of risks being taken by Security Operations Center (SOC) teams worldwide. When false positives are a key part of their daily routines, it becomes close to impossible to handle all the alerts and give attention to the ones that are critical. 

Ideally, every alert is investigated to see if it’s genuine, but in reality the challenge of resources, time and focus comes into full effect. Which leads to missed or ignored alerts

In SecOps this rolls down into more incidents that lead to breaches, hacks, and other major incidents. The results lead to consequences in revenue, costs, and their brand reputation more on top of the data that they are entrusted with protecting. 

There is also the danger of slow response times. An alarm doesn’t need to be missed or ignored to become a critical issue; it can also be temporarily ignored. If the previous alerts have been false, will an analyst give their attention immediately to the next? Probably not, and it’s not the fault of the analyst themselves. They’re tired and burnt out.

According to Forrester Research:

  • A single day shows that, on average, a SOC team will receive 11,000 alerts per day.
  • 18% of those manually reviewed
  • 32% are false positive
  • 28% are ignored
  • 17% are touched by automation
  • Nearly 50% of SOC managers admit that their staff cannot manage to investigate every alert.

These are some troubling numbers. It gets more critical when the company size increases. In a company with 20K+ employees, for example, the number of ignored alerts rises to 36%. The more entry points and points for human error, the security risk exponentially increases.


Solving the unsolved - Decision Intelligence in Cybersecurity

Without a doubt, with the increasing flow of big data, Artificial Intelligence (AI) solutions have delivered many benefits that are now widely recognized in multiple sensitive sectors such as healthcare, defense or finance.


However, traditional cybersecurity strategies are no longer anywhere near effective and cybercriminals have a pool of attack vectors to rely on as well as many new challenges to overcome.

The recent advancements in AI research have enhanced techniques that possess the flexibility and adaptability needed to emulate human behavior for complex processes.

A unique approach to the cybersecurity industry is successfully applied by the platform overcomes the limitations of AI tools used in SOCs today to achieve better decisions, with fewer resources, at scale by bridging human experts and AI. 

By enabling this unprecedented human & AI partnership, SOC analysts are able to make faster, better and more accurate decisions. This directly addresses the ever-present challenges of misread, suppressed, or unchecked alerts, signals, offenses or other notable events and proves to be the next best tool in a cybersecurity arsenal.


Benefits of adopting Decision Intelligence in your SOC team

Decision intelligence includes decision augmentation and management in a descriptive and predictive manner and has shown multiple benefits encompassing faster decisions, multiple problem-solving options or reduction of mistakes or biases in the process. And while some advantages are easily noticeable, here are some more boxes this type of tool ticks for your benefit: 


  1. Deep Learning and Assistance

The collective knowledge of the most experienced analysts of the SOC team is aggregated in AI-Assisted Cybersecurity. Not only is the tool capable of assisting based on the experiences of your non-AI teams, but the tool can also learn as it goes. An AI-Assisted Cybersecurity relies not only on historical data and team behavior to manage alerts but is also augmented with context through analyst feedback. Thus, the tool offers a deeper skillset to your team of experts


  1. Assisted Decision Making. 

At the Decision Stage, based upon the data available and knowledge embedded into the model to AI-Assisted Cybersecurity, the model makes an automated decision to either “escalate” or “drop” an alert. This is presented to analysts and drastically reduces the number of false alerts that SOC analysts chase down and allows for attention to be given to legitimate breaches and cybersecurity concerns.


  1. Retains Knowledge and Experience

Since the AI-Assisted Cybersecurity represents all of the experts who have provided input to the AI model this will not only benefit the existing team, but also future analysts and the entire SOC team. This offers a safety net for new analysts and keeps critical business intelligence in-house. As well, it helps with staff turnover, promotions, as well as role changes to allow talented analysts to grow and advance within the organization.


  1. The AI is Adaptable and Customisable

The model has two advantageous ways in which it can be customized. 

First, a feature selection exists to address specific organizational needs or threat categories while also integrating seamlessly within any cybersecurity ecosystem. 

The second is that an AI-Assisted Cybersecurity such as can adapt to the particularities of the ecosystem in which it runs based on the context it gets.


  1. Takes Care of Post-Decision Manual Tasks

The AI-Assisted Cybersecurity can also reduce manual work such as case and ticket creation by working in tandem with the SOAR playbook. This means that time-consuming tasks can be taken away from analysts and automated.


  1. Easy to Use Via The No-Code Model

Designed to be simple to implement and intuitive. Installation, integration and the use of the platform don’t require any specialized knowledge, outside of what a traditional SOC team already has. Any analyst with security knowledge can benefit from - and contribute to - the model.


  1. Offers Scalability

Regardless of organization size, your tools and systems need to be scalable. The AI-Assisted Cybersecurity harnesses the collective expertise of the entire SecOps team and continues to add additional efficiencies to your cybersecurity processes as you grow. Not only is it a valuable asset to the IT team, but offers efficiency and productivity to your human resources team and boosts their ability to focus on company growth. 


  1. The Model is System Agnostic

Your internal infrastructure and existing cybersecurity tools will not be a hindrance when implementing AI-Assisted Cybersecurity. The tool is completely system agnostic. It does not replace or change already existing processes/tools but enhances them by automating the decision and post-decision processes using AI


As a result, the weight of responding, potentially missing and mishandling alerts is negated. 

The AI-Assisted Cybersecurity tool streamlines and enhances the work of your SOC analysts, throughout all the phases of threat detection and negation. The model provides predictions on your alerts and reduces the time spent on false positives to help analysts focus on the real threats. Along the way, the model continues to adapt and learn and offers new opportunities to enhance your security parameters. 

Most importantly, a Decision Intelligence platform such as offers a future-proof, efficient, and scalable solution to alert fatigue for the cybersecurity industry.

Contact us to learn more about Decision Intelligence in Cybersecurity 

Contact Us Team