Network Security

How AI is altering the cybersecurity automation landscape and its effect on cybersecurity job burnout

How AI is altering the cybersecurity automation landscape and its effect on cybersecurity job burnout
Written by ga_dahmani
How AI is altering the cybersecurity automation landscape and its effect on cybersecurity job burnout

AI has played a crucial role in automating cybersecurity. One of the key advantages of AI in cybersecurity is its ability to process massive amounts of data with the efficiency of the assembly line that organizations use. This enables AI to automate the creation of algorithms to detect cybersecurity threats across a wide range of elements in the IT network, including emails, websites visited, third-party applications, file shares, and more. Algorithms with the help of AI learn over time and become smarter to detect early threats. Furthermore, AI-enabled cybersecurity solutions are familiar with general network traffic and can detect any changes that arise to eliminate risks. Such algorithms can also help IT security professionals detect cyber risks and mitigate them before they cause damage to the business. AI-powered programs learn with algorithms like machine learning and deep learning. These algorithms recognize many trends and expect changes.

Some of the benefits of cybersecurity automation through AI are:

  • AI helps with the detection of new security risks

AI can dramatically eliminate or minimize the consequences of an advanced hacking technique that has yet to be explicitly classified and clarified by the annals of cybersecurity, as hackers specialize in developing ways to enter undetected networks and take over companies off guard. AI-powered cybersecurity solutions can detect new and unknown threats along with threats that have occurred within the network and known threats.

  • Automation through AI enables 24/7 monitoring of security threats

The integration of AI and cybersecurity allows companies to make more productive use of human capital. Enabling AI-enabled cybersecurity applications to perform security diagnostics and providing IT security personnel to review legitimate threats diagnosed by the application enables the business to utilize the time, skills and talents of IT staff more effectively. effective and efficient. IT workers posted on shifts to track network security are neither cost-effective nor reliable compared to using an AI-powered cybersecurity solution. Third-party cybersecurity solutions are a more feasible option, but these solutions come at a reasonable monthly price.

  • Automation-led machine learning helps fight scams and avoid redundant procedures.

AI enables cybersecurity researchers to work on developing algorithms or exploring emerging threats that can help recognize suspicious and malicious emails to alert and protect users. AI can also help eliminate duplicate processes, while its algorithm can save analysts a great deal of time spent on thousands of data sets repeating the same procedures.

Some of the drawbacks of cybersecurity automation are:

  • High prices: Very often, AI tools are very expensive, making them unaffordable for small and medium-sized business organizations to adopt and implement.
  • Requires huge data points for AI engines to learn: AI becomes smart when it is fed large amounts of data to understand patterns. For any zero-day vulnerabilities and new threats that have fewer data points, AI may not be effective.
  • Increase in unemployment: It can make the task of large monitoring teams redundant and eliminate their jobs.
  • False positive and false negative cases: You can mistakenly send alerts for cases that are not really positive. Sometimes it becomes a more dangerous situation when you can wrongly indicate that a particular alter is negative even though he or she is a positive threat.

The Effect of Automation on Cybersecurity Jobs

According to a report by the Data Security Council of India (DSCI), the demand for cybersecurity professionals will increase by 35% CAGR YoY. Some of the key skills that are in high demand include Cloud Security Architect, SecOps, and security engineering. Also, with new technologies emerging like data science and artificial intelligence, existing cybersecurity professionals need to be trained accordingly. Skilled cybersecurity talent remains a key challenge for the Indian cybersecurity industry and with a more distributed workforce after the COVID pandemic, cybersecurity threats have multiplied and created a need for more security professionals. cybernetics. However, the automation of cybersecurity through AI is catalyzing this increase in the unemployment rate in the cybersecurity industry, as with its multiple benefits it can make the work of the monitoring team redundant and eliminate the need of these jobs.

Conclusion:

The future is normally difficult to predict. However, when it comes to IT security automation, it’s only headed in one direction: forward. There is a lot of investment in this sector, as it is difficult to find trained IT security personnel around the world and the industry is constantly evolving and developing, drawing more and more attention to it. This has led to an increase in wages, which is a real problem for those employers who are lucky enough to retain staff. The lack of manpower forces organizations to automate certain tasks and the savings obtained from the normally high salaries pay the development costs. Another reason is the demand for reduced reaction time. The risks of a serious breach have increased significantly due to increased network connectivity and confidence in data availability and confidentiality. In general, AI has helped automate multiple redundant tasks in the cybersecurity landscape, but with it comes the rational fear of losing the need for staff in the industry, resulting in reduced jobs and opportunities.



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Disclaimer

The opinions expressed above are those of the author.



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