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AI and Big Data Analytics: A Winning Combination for Cybersecurity

This guest post was contributed by Melanie Johnson, Tech Educator and Technical Writer

The amount of data shared on the web is beyond measurable nowadays. An increasing number of organizations struggle to manage this data in a way that’s secure and immune to cyber attacks, while employees fail to follow cybersecurity best practices. Two technologies that are gaining popularity in the cybersecurity space are artificial intelligence (AI) and big data analytics.

What is AI and Big Data Analytics?

The fact that computer systems today can “think” and communicate as humans are powered by Artificial Intelligence and Machine Learning. Big Data Analytics is the process of examining and interpreting large and complex data sets to identify patterns, trends, and insights that, in their turn, serve to train AI models.

Both AI and Big Data Analytics can be used independently in organizations looking to enhance their cybersecurity efforts. And when combined, businesses can further automate cybersecurity tasks by utilizing AI to examine the data gathered by big data analytics technologies. Because that’s the way AI models are built: first and foremost, you collect a reliable dataset, you feed it to the model and iterate until satisfactory performance. Sure enough, the practices of big data analysis can facilitate dataset creation and hence, AI establishment in the organization. Let’s now see what AI can do to strengthen your cybersecurity.

AI Use-Cases in Cybersecurity

One of the best things about AI in cybersecurity is how it uses machine learning algorithms to analyze network data and detect any unusual activity. This means it can quickly alert us to potential cyber threats and help us take action to prevent them from doing any harm. But that's not all - AI's learning capabilities mean it can get even smarter over time, adapting to new attack techniques and staying ahead of cybercriminals. So the more data and experience AI has, the more protected your network will be. Here are a couple of examples of what AI can do when integrated into the cybersecurity workflow:

  • Evaluate email content and sender information: AI can help identify questionable emails with phishing intent and stop them from reaching recipients.

  • Examining network data: AI can be trained to “notice” strange activity, and discover malware and other harmful software, stopping it from spreading throughout a company's network.

  • Monitor and analyze user behavior: AI can assist in preventing data breaches by finding any odd activity or data access patterns that may suggest a possible danger.

  • Analyze historical data: AI can improve security by looking at previous data to find patterns and trends that may be used to forecast upcoming attacks and vulnerabilities.

Challenges and Considerations

While AI and big data analytics offer significant benefits for cybersecurity, there are also potential challenges to keep in mind. For example, ethical considerations around the use of AI in cybersecurity must be carefully considered, as there is a risk of bias and discrimination in the data used to train AI algorithms. Additionally, the complexity of AI and big data analytics technologies may require specialized expertise and resources, which may be difficult for smaller organizations to access.

To wrap up

As cyber threats continue to evolve and become more sophisticated, organizations must find new ways to enhance their cybersecurity defenses. Despite the challenges and accessibility issues, it is likely that AI and big data analytics will play an increasingly important role in the years to come as organizations explore new ways to protect their data and asset.



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