The Future of Cybersecurity: Artificial Intelligence and Kafka
In today’s cyberthreat landscape, it is not just the volume of threats that poses a challenge to organizations. Rather, it is the sophistication and resiliency of the attacks that pose a threat. This is because attackers are becoming more sophisticated in their malicious designs, continuously coming up with new methods to breach security and disrupt business operations. They are also leveraging AI-powered automation tools and malware such as ransomware to launch attacks with greater frequency and efficiency. Therefore, keeping ahead of these trends will be critical for any organization looking to efficiently tackle cyberthreats and protect their data from malicious actors in the future. This article outlines some of these disruptive trends in cybersecurity to provide insights into how we can better secure our digital assets in an increasingly complex world.
AI and ML in Cybersecurity
AI and machine learning (ML) are fast becoming essential tools for cybersecurity. With millions of new encounters of malware every day, security teams are continuously struggling to keep pace with the growing volume of threats. AI and ML can help by enabling security tools to automatically detect and flag malicious content without the need for manual intervention. Rather than manually examining each piece of content, analysts can then use their time to investigate flagged content and make decisions on how to respond to threats. In addition, AI and ML can also support a more proactive approach to cybersecurity, enabling security tools to predict and prevent threats before they occur. This is because ML can be trained to identify specific patterns in data to detect anomalies that may be indicative of an attack. For example, a security tool trained on user behavior in a healthcare organization can identify unusual patterns that point to an attempted breach. This can include an unusually large number of failed log-in attempts or unusual log-in locations.
Cloud Adoption Will Be Critical
The adoption of cloud computing has been increasing steadily over the past few years, with organizations across all industries looking to harness its benefits and gain competitive advantage. However, these organizations often overlook one key consideration when deciding on a cloud provider – cybersecurity. This is because cloud providers have a responsibility to secure all data stored on their systems, but there is often limited transparency into how they achieve this. The cybersecurity of a cloud provider should be one of the key considerations when selecting a provider, but it is often overlooked. This is because the cybersecurity of a provider is not always easy to assess, and organizations may not know where to start when investigating a provider’s security capabilities. This is particularly true for organizations that operate in highly regulated industries like financial services and healthcare. This is because the level of security required to protect sensitive data in these industries is often higher than that required by less sensitive industries.
Dark Data Will Be a Key Challenge
The growth of AI and the increasing digitalization of all types of data has led to an increase in what is being referred to as “dark data.” Dark data is defined as data that is neither used nor wanted by organizations. This is usually data that has been collected and is being stored, but is not being analyzed or used in any way. It is thought that across the globe, there is currently around 2.5 quintillion bytes of data being stored in organizations. However, only a fraction of this data is actually being analyzed and used. The rest is being stored, but is neither analyzed nor accessed. This represents the “dark data” in organizations. Because this data is being neither analyzed nor accessed, it is not being put to any useful purpose. This means it is not generating insights, so it is not helping organizations to make better decisions. This highlights a key challenge for organizations in the future – how can they identify and analyze their dark data so that it can be put to use?
Blockchain Is Shaping the Future of Cybersecurity
The rise of cryptocurrency such as Bitcoin, which is based on blockchain technology, has brought this technology into the spotlight. However, blockchain has the potential to be applied to a wide range of different industries, not just fintech. Cybersecurity is another industry where blockchain has great potential. This is because blockchain can be used to facilitate secure data sharing between organizations and provide a trusted environment to store sensitive data. This means that organizations would be able to rely on a secure environment to share data. This would help them to more easily share data to respond to cyberthreats, such as an outbreak of a computer virus. Blockchain technology can be used to encrypt and store data, such as identifying information about a virus. This data can then be distributed across a network of computers in a way that means it cannot be accessed or altered by just one person. This means that data would need to be accessed by a majority of computers on the network in order to be changed. This ensures that the data is secure, and any alterations to the data would be detected.
kdb – The Kafka-based platform for cybersecurity
A key challenge for organizations looking to use AI and ML to secure their data is access to sufficiently large data sets. This is because the large number of data points that are collected by security tools are needed to train these AI systems. Therefore, organizations will often need to collect and store new data for cybersecurity purposes. This can be a challenge for organizations that have limited resources and/or data scientists who are trained to analyze data. Organizations that are looking to develop AI and ML-based cybersecurity tools are therefore often choosing to use Kafka as their data platform. This is because Kafka can be used to stream data between different tools, manage data streams, and prepare data for ML algorithms. This enables organizations to collect the data they need and prepare it for AI and ML-based analysis. This prepares the way for organizations to build effective cybersecurity tools using AI and ML.
Conclusion
The future of cybersecurity will be shaped by these three trends. Cloud adoption must be considered when selecting a provider, as it will be critical to select a provider with a high degree of security. Dark data needs to be identified and prepared before it can be used by organizations. Finally, blockchain technology can be used to manage the data collected by cybersecurity tools, enabling organizations to prepare data for analysis by AI and ML algorithms. This will enable organizations to use AI and ML to respond faster to cyberthreats, protect their data better, and prevent malicious actors from disrupting their business operations.
