The rising of digital connectivity and more advanced cyber-threats have promulgated the need for smart cyber defense. Smart cybersecurity is a logical reaction to learning to control risk by reducing security gaps that are often generated by relying on manual processes that are influenced by a continuous shortage of cybersecurity skills and the administrative burdens of data security management.
Despite the challenges, there is hope to reduce human dependency and to improve the potential for cybersecurity. A myriad evolving cognitive technology will help everyone to strengthen cybersecurity and manage the increasingly dangerous and disruptive world of cyber threats. They include:
- Artificial Intelligence
- Machine Learning
- Automated and Adaptive Networks
- Super-Computing
Artificial Intelligence: AI is a technology that usually emulates human performance through learning, drawing its own conclusions, pretending to understand complex information, engaging in natural dialogs with people, improving human cognitive capacity, or replacing people on non-routine tasks performance. Some of the underlying AI computer systems are now designed to include: recognition of speech, learning’s planning, and problem-solving. Computing systems that employ AI and ML are becoming more pervasive and critical to cyber operations. They have become a significant focus area of cybersecurity research development and investments in both the public and private sectors. The identification, categorization, and synthesizing of data are advantages in minimizing threats to cybersecurity. Holistically, AI technologies can help protect against ever more complex and malicious attacks on malware, ransomware, and social engineering. AI is not conscious (yet), but in anticipating and preventing cyber-attacks, there is potentially a future of AI cognitive autonomy.
Machine Learning: In basic terms, machine learning is getting a machine without programming to function. It often combines with AI and can be thought of as rapid predictive analytics automation. Machine learning will provide the quickest way to detect new cyber threats, draw statistical inferences, and transfer the knowledge to endpoint platforms for security. Threat Intelligence is a domain where AI and ML will provide an essential component of cybersecurity. It can be used to track and detect network anomalies and to recognize new threats without known signatures. This can also be used to compare silos data to understand the complexity of the threats and to determine vulnerabilities and risks associated with the network. AI and machine learning can aid in controlling identity by cross-checking the data veracity through multiple fragmented databases.
Automated and Adaptive Networks: Automation allows the horizon scanning and tracking of networks to report in real-time on deviations and anomalies. This allows automated updating of defense system layers (network, sender, endpoint, firewalls, and anti-virus) and cybersecurity diagnosis and forensics analysis. Both AI and machine learning can be central components of automated and adaptive networking applications. One area that can be assisted by AI and ML is robotic process automation (RPA), a technology that enables automated integration of any IT application or website to perform intricate rule-based work (such as clerical processes or transactions). AI and ML can also facilitate Security Orchestration Automation and Response (SOAR) products by integrating, within a single interface, orchestration processes, automation, incident management and collaboration, visualization, and reporting. SOAR can also provide a quicker, more reliable way for the security operations center (SoC) staff to process large quantities of data generated by cybersecurity systems and help detect and resolve attacks that may be imminent or underway.
Supercomputing: Supercomputers are distinguished from mainframe computers by their comprehensive data storage facilities and broad computational capabilities. Supercomputers are the motors for automation to follow. Super-computing is promising in cybersecurity. Detecting cyber threats can be significantly improved by providing a reliable model of regular background network activity. By allowing researchers to compare the Internet traffic data, they are examining with these models to more readily bring abnormal behavior to the surface. Smart cybersecurity plays a promising and significant role in identifying, filtering, neutralizing, and remedying cyber-threats. By harnessing 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.
