3. Integration of Artificial Intelligence and Machine Learning
Complete protection against ever-evolving, sophisticated threats, technologies and devices that bypass standard security measures is manually impossible. Therefore, the demand for complex security automation has expanded with the proliferation of cyber threats, IoT, connected devices and WiFi network vulnerability to security threats.
According to Venturebeats, enterprises view AI and machine learning as a support for their cybersecurity administrators. Organizations such as CISCO claim that attacks against machine based transactions are especially difficult for employees to tackle. Security professionals are using AI and machine learning models to combat malicious attacks. These advanced algorithms can boost early detection capabilities using threat intelligence. AI can identify potential attack variants, and machine learning can determine attack classes and detect threats.
Attacks meant to evade these technologies also continue to evolve with them. Cybercriminals harness the power of AI and machine learning tools to orchestrate multiple cyberattacks by identifying network defenses and simulating behavior patterns to bypass security controls. These tactics call for increases in the deployment of advanced heuristic solutions according to the scope and severity of threats.
4. Zero Trust Cyber Security
Business models and workforce dynamics continue to develop with the shift to cloud and hybrid IT environments, increasing the presence of corporate assets outside the traditional security perimeter. These exposed assets demand centralized policy orchestration and distributed policy enforcement for more responsive security control to shield them.
Zero trust security architecture facilitates effective authentication and authorization, ensuring that legitimate users and applications gain access to the protection surface. It ensures continuous trust evaluation by leveraging network segmentation, multi-layered threat prevention, lateral movement restriction and granular user access control.
The COVID-19 pandemic has further accentuated interest in zero trust cyber security with employees shifting to remote work. According to a research by marketsandmarkets, the post pandemic cybersecurity market is expected to reach $51.6 billion by 2026. Thanks to a shift in the working environment, government authorities mandate new regulations for private and public enterprises. Targeted attacks result in business downtime, loss of intellectual property and revenue loss. Despite the surge in its demand, it is not easy to integrate in an existing system – networks are rarely designed to accommodate zero trust models.
5. Privacy Enhancing Computation
The growth of digital technology and data utilization amplifies data privacy concerns as organizations base their structures around data, forcing them to maintain data privacy. Data processing activities that involve personal data transfers, fraud analytics, data monetization and more require in-depth assessment. Privacy-enhancing computation can help organizations maintain privacy and security by ensuring safe data-sharing and secure collaboration across regions.
Gartner’s top strategic technology trends of 2022 mentions using privacy-enhancing computation to protect data in use while maintaining confidentiality. It also estimates that by 2025, half of all organizations will implement privacy enhancing computing to process sensitive data in untrusted environments and multi-party analytics use cases to meet the growing need for sensitive data sharing.
Modern privacy regulations will cover the private data of 75% of the world’s population by the end of 2023. Also, with the rising number of security breaches, regulatory bodies will increase their efforts and expectations by creating new privacy regulators and closely monitoring cybersecurity preparedness.