Machine learning and artificial intelligence are playing crucial roles in bridging the workforce gap. However, there is still a question on the extent machines can support and enhance an organization’s cybersecurity initiatives? And, if successful, will it remove human intervention from the equation?
The past few months have been harsh for organizations, with the pandemic forcing the workforce to adopt remote working and the businesses staring at a financial loss. The increase in cyber-attacks during the COVID-19 crisis is well documented at this point. As ransomware, insider threats, phishing, and other types of attacks rise, IT and business leaders anticipate an urgent and increased need for more cybersecurity measures.
The Rise of Cyber attacks
Analysis of the colossal volumes of data produced by security devices and network sensors is probably one of the most resource-intensive tasks required of security teams. Network breaches often remain undetected for months, letting cybercriminals establish elaborate botnets and access millions of records with customer information and intellectual property.
This challenge is aggravated with the growing skills shortage the cybersecurity industry is facing, further adding to the risks faced by organizations. CISOs believe at least one intrusion or breach faced by organizations over the past year can be partially accredited to a gap in cybersecurity skills.
Cyber-attacks on several tech giants have brought to the fore the challenge of bridging the skills gap in the cybersecurity space. Artificial intelligence (AI) could be a crucial solution to this gap faced by the tech industry worldwide.
Bridging the Cybersecurity Skills Gap
CISOs suggest several steps that businesses can take to close the cybersecurity skills gap. They need to ensure that security tools don’t operate in isolation. If a security tool detects an anomalous behNewavior, it needs to be able to share that data with other devices, so that data can be compared against other data and cross-referenced against external threat intelligence feeds. This process can help detect suspicious activity faster, especially with these tools tightly integrated.
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Data also needs to be collected from access control points and network devices to see the bigger picture. Behavioral analytics can be used to identify abnormal activities, such as devices or applications poking the network looking to connect to other devices, data moving upstream out of the data center, or services not under their usual activity domain.
Even though these solutions can help evaluate massive volumes of data from various locations, they still have their limitations. Networks and systems are in a state of constant flux today. Remote offices, dynamic cloud environments, SaaS applications, Shadow IT, and DevOps projects complicate the ability to monitor and evaluate data.
Besides, all these processes in no way eliminates the need for having human analysts to review, manage, supervise, and respond to events detected by this collection. The security skills gap is part of the problem. There aren’t enough cybersecurity professionals to fill critical roles.
Solving Cybersecurity Ailments with AI
Organizations need to be on their best security game when dealing with a threat landscape. An efficient way to improve the security operation is to utilize artificial intelligence to streamline the analysis, identification and investigation, and prioritization of cybersecurity alerts.
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Artificial intelligence can also help businesses by being a crucial tool for security professionals, and can then be applied directly to the application processes. Moreover, integrating AI within the organization’s cybersecurity infrastructure is the perfect way to encourage business leaders and employees to take a more proactive approach, which is miles better than the traditional reactionary approach.
To fill the skills gap, companies can integrate artificial intelligence to their workforce, which will not supplant people. It will offer employees with a combination of man and machine to strengthen their performance. The beauty of AI and machine learning is that it takes up tasks, that are minimal and requires repetition and, letting human employees focus on the bigger picture, or innovations.
