Cybersecurity | Future of Cybersecurity: 4 Evolving Technologies

Future of Cybersecurity: 4 Evolving Technologies

Swami nathan

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More than ever, we conduct more of our personal lives and business activities online, making cybersecurity a key issue of our time. Understanding what cybersecurity’s future is – will teach you how to make the best use of your resources and remain secure not only today but tomorrow as well.

The future of cybersecurity is difficult to foresee, as the market is continuously changing in response to cybercriminals’ shifting activities and the new attacks they are creating. For instance, between 2019 and 2020, the number of global ransomware attacks increased by almost 25 percent, causing cybersecurity developers and businesses to build new applications to combat the phenomenon.

Despite the challenges, there is a promise to reduce human dependence and to strengthen the capacity for cybersecurity. Myriad of evolving cognitive technologies can help us improve cybersecurity and navigate the increasingly malicious and disruptive landscape of cyber threats. They cover:

Artificial Intelligence

Artificial Intelligence (AI) has come to life in many sectors, as a technology, over the past few years. Today, it is possible to use AI and machine learning algorithms to automate jobs, crunch data and make decisions much faster than a person ever could.

Nonetheless, potentially new technologies, like AI, pose cybersecurity threats as future vulnerabilities are poorly understood at release time. This means that AI systems are sure to become a major target for hackers, with more organizations relying on machine learning for mission-critical operations. In addition, potential tools and staff for cybersecurity will be forced to build techniques to detect and combat AI corruption attacks.

However, AI changes the world of cybersecurity by offering hackers a new way to hit target organizations. Cybersecurity developers will use AI to address vulnerabilities themselves, detect security issues before they can be exploited and repel cyberattacks once they have started.

Computing systems employing AI and ML are becoming more inevitable and vital to cyber operations and have become a major focus area of advancement of cybersecurity research and innovation in both the public and private sectors. The discovery, categorization, and synthesizing of data are certain advantages in mitigating threats to cybersecurity. Holistically, AI technologies can help protect against ever more sophisticated and malicious attacks on malware, ransomware, and social engineering. AI is not (yet) intelligent but in anticipating and preventing cyber-attacks there is potentially a future of AI cognitive autonomy.

Machine Learning

Effective cybersecurity technology can’t be deployed today without relying heavily on machine learning. At the same time, deploying machine learning effectively is impossible without a comprehensive, rich, and complete approach to the underlying data.

Cybersecurity systems can identify patterns with machine learning and learn from them to help prevent repeated attacks and respond to the changing behaviour. It can help cybersecurity teams be more proactive in prevention of threats and the real-time response to active attacks. This will reduce the amount of time spent on repetitive activities and allow companies to use their resources more strategically.

In short, machine learning will make cybersecurity easier, more proactive, less expensive, and much more efficient. But only if the underlying data supporting machine learning offers the full image of the world can it do such things.

Machine learning is about developing patterns and using algorithms to manipulate those patterns. You need a lot of rich data from everywhere to develop patterns, because the data needs to represent as many potential outcomes as possible from as many potential scenarios.

It’s not just about the amount of data; it’s about the quality, too. The data must have a detailed, appropriate and rich background from every possible source — whether at the origin, on the network, or in the cloud. You will need to concentrate on cleaning up the data so you can make sense of the data that you are collecting, and you can interpret outcomes.

Supercomputing

Locating a needle in a haystack can be like identifying cybersecurity threats from raw internet data. For example, the amount of internet traffic data generated in a 48-hour span is too large for one or even 100 laptops for human analysts to process into something digestible. For this purpose, analysts rely on sampling to check for possible threats, choosing small pieces of data to investigate in depth, trying to detect unusual behaviour.

Although this form of sampling can work for certain tasks, such as identifying common IP addresses, the identification of subtler threatening patterns is inadequate.

Supercomputing is promising in cybersecurity. MIT Lincoln Labs Fellow Jeremy Kepner states that “Detecting cyber threats can be greatly improved by providing a detailed model of regular background network traffic,” and that researchers should equate the Internet traffic data they are examining with these models in order to bring anomalous activity to the surface more readily.

At a conference sponsored by DARPA, supercomputers sans humans, this type of capability were shown to be exposed to bugs which the computers were able to detect and quickly repair the threats.

Human factor fallibility has become a weakness in cybersecurity. It will probably get more as we become more immersed in digital interconnectivity (i.e. remote work on the Internet of Things Smart Cities) associated with the realities of a larger cyber-attack surface.

Smart cybersecurity plays a promising and an important role in identifying, filtering, neutralizing, and remedying cyber-threats. Through harnessing the emerging market technologies such as artificial intelligence, machine learning, automated and adaptive networks, and supercomputing, companies would be able to address potential challenges more readily.

Adaptive Networks

Adaptive Networks are automated and programmable networks that can configure, track, manage and adapt to changing needs. These networks are based on three basic layers: programmable infrastructure, analytics and intelligence, and control and automation applications.

The programmable layer of infrastructure serves as a sensor and generates a real-time data on network efficiency and vulnerabilities, enabling agencies to fix them proactively and assign resources accordingly.

While the analytics layer brings insight to the network, it applies machine learning to analyse data based on performance, and to predict network issues and threats more accurately.

The end layer is monitoring and automation applications. To simplify network management and service delivery through multi-vendor, multi-domain hybrid networks, it leverages software-defined network architectures and multi-domain service orchestration.

Such layers combine to create a more flexible, scalable and stable network.

An Adaptive Network helps agencies meet rising bandwidth stresses, as well as demands for modernization and security, delivering high-performance connectivity and faster services to constituents.

The only way to protect what you’ve worked hard to build is to be vigilant when it comes to cybersecurity. If you’d like to know more about how your business can benefit from managed services, just give us a call, we are here to help.