In a world where digital connectivity is rising, and cyber-threats are becoming increasingly sophisticated, smart cybersecurity has become essential. In response to a shortage of cybersecurity skills and the administrative burdens of managing data security management, smart cybersecurity can be seen as a logical way to mitigate risk by reducing security gaps typically associated with manual processes.
While there are challenges, it promises to reduce human reliance and increase cyber-security measures. We can enhance cybersecurity using the numerous cognitive technologies emerging today and navigate an increasingly disruptive and malicious cyber threat sphere with the help of these technologies.
Here are the most prominent ones:
The Automation and Adaptation of Networks
To automate security tasks within a network, machines are used to execute the tasks programmatically. There are many types of network security automation, but the most common uses are those concerned with detecting, investigating, and mitigating high-volume, low-complexity attacks.
Using network automation, it is possible to scan the horizon, monitor networks in real-time, and identify deviations or anomalies as they occur, which can be resolved as quickly as possible.
Automating the network allows for seamless updates of layers-by-layer security protection, including anti-virus, firewalls, payload, network, and endpoint protection, to be applied throughout the network. Aside from that, it is also responsible for ensuring that cyber security forensics and diagnostics are updated on time.
Many applications of automated and adaptive networks can be developed using artificial intelligence (AI) and machine learning (ML) as the core components and supporting applications.
AI and machine learning have the potential to be very useful in many areas, one of which is robotic process automation (RPA). This capability uses robots to automate routine tasks (for example, transactions and clerical tasks) within IT applications.
AI and machine learning can also serve as facilitation tools for Security Orchestration Automation and Response (SOAR) products by taking advantage of orchestration processes, automating them, managing incidents and collaborating with them, and providing visualization and reporting tools under a single interface.
The SOAR system can also be used by security operations centers (SOCs) to analyze the massive amounts of data generated by cybersecurity systems both faster and more accurately, as well as to find and eliminate threats that may be looming or already present.
With so much happening these days – an abundance of data, threats, and an overabundance of attack surfaces – you will not even be able to defend yourself without the automated capabilities that artificial intelligence and machine learning provide.
Artificial Intelligence (AI)
AI is a technology that mimics human performance in several ways, such as learning and generating its conclusions.
This also includes the ability to apprehend complex topics, converse naturally, augment cognitive ability, or take over non-routine tasks that humans typically carry out. Computers with artificial intelligence are now designed to perform essential tasks, including language recognition, learning, planning, and finding solutions.
In the public and private sectors, AI and machine learning-based computing systems are becoming increasingly pervasive and critical to cyber operations. While AI is still not conscious, with cognitive autonomy, it may be able to predict and mitigate cyberattacks in the future.
Discovering, categorizing, and synthesizing data is undoubtedly advantageous when reducing cybersecurity threats. A holistic approach to AI can provide a comprehensive defense against malware, ransomware, and phishing attempts that have become increasingly sophisticated and malicious.
Machine Learning (ML)
In basic terms, ML refers to the process of making a device act autonomously. It is frequently combined with artificial intelligence as a rapid approach to predictive analytics. With machine learning, cyber-attacks can be identified faster, statistical inferences can be drawn, and the information can be pushed to endpoint security platforms for immediate response.
Threat intelligence is one area in which AI and ML can undoubtedly contribute significantly to cybersecurity. With this method, it is possible to monitor and detect anomalies in the network and discover new threats with no known signatures.
Additionally, it can be employed to correlate data from different silos to understand the nature of attacks. This will enable us to assess potential vulnerabilities and risks related to the network. Using AI and machine learning to cross-check data integrity across multiple fragmented databases could also help with identity management.
Supercomputing
Known as the “father of supercomputing,” Seymour Cray is still leading the industry through his company, Cray Computing.
The future speaks well for supercomputers, viewing them as automation engines. In contrast to mainframe computers, supercomputers have vast data storage capacities and extensive computation capabilities. According to a study by the National Academy of Sciences, investments in supercomputing are incredibly beneficial and play a crucial role in protecting national security and advancing scientific discovery.
The use of supercomputing in cybersecurity is a promising one. With an accurate representation of internet traffic, cyber threats can be detected more quickly, and data on internet traffic can be compared with these models to identify anomalies rapidly.
In a DARPA-sponsored conference, supercomputers sans humans encountered bugs attack, which the computers detected and quickly fixed.
One of the vulnerabilities of cybersecurity has been the fallibility of the human factor. With the increase of digital interconnectedness (e.g., IoT, Smart Cities, Remote Work) and the realization that we will be exposed to a more significant number of cyber-attacks in the future, this will likely become even more so.
Internet of Things Connectivity
The increasing connectivity of everyday objects positively and negatively impacts security. One of the benefits of this technology is that a great deal of information can be gathered and processed in a short period. Furthermore, increased data exchange between devices opens up new opportunities for interception and presents new threats in the process.
Strong encryption should ensure a secure connection between devices and the systems they operate on, in addition to hardware and software security measures integrated into machines at the design stage.
Conclusion
Cybersecurity is affected more profoundly than most people realize in the ever-evolving digital age. Technological advancement has contributed to an exponential growth in cybercrime. It has been noted that as technology advances, more cybercrimes are committed. The good thing is that with this technological expansion, the ability to seek out cybercrimes before they happen has also increased, ensuring the safety of people.
Cyber threats can be identified, filtered, neutralized, and remedied with smart cybersecurity. With the help of advanced enterprise technologies such as automated and adaptive networks, artificial intelligence, machine learning, supercomputing, and the internet of things, enterprises will meet future challenges better.
