The recent attack on the government’s e-Citizen portal is an eye opener. As we explore technological advancements and innovations, we will compete with a new level of dynamic and complex cybersecurity threats and attacks.
Among the major challenges is that institutions and professionals cannot keep pace with them. Government ICT platforms are often developed by different entities, using varied technologies, standards and protocols. That leads to a heightened risk of unauthorised access, data breaches, cyberattacks and other security incidents that compromise privacy.
Privileged individuals may facilitate cyberattacks for external invaders. These attackers may manipulate or bribe privileged employees to gain access to networks or systems, bypassing perimeter defences. Data manipulation, installation of malicious software and policy non-compliance by such trusted individuals could pose the worst form of cyberattacks.
Managing the challenges and risks associated with complex government digital and ICT platforms requires a comprehensive approach that considers various aspects of governance, technology, security and collaboration to achieve secure private communication in a public environment.
It’s impossible to achieve tamper-proof systems. Consider multiple approaches to make ICT platforms tamper-evident in realtime mode so that imminent cybersecurity attacks don’t cause damage. Collaboration and partnerships with relevant stakeholders such as technology vendors, industry experts, academia and other government agencies can also create awareness. But we cannot stop criminals from attacking or destroying our platforms from mere distributed denial-of-service (DDoS).
With the field of cybersecurity continuously evolving, to keep up with the ever-changing landscape of cyberthreats, let’s deploy artificial intelligence (AI) and Machine learning (ML) technologies to contain cybersecurity threats and attacks on our platforms.
Artificial intelligence (AI) and machine learning (ML) technologies are being applied to cybersecurity to enhance threat detection and response. AI systems using ML algorithms can analyse vast amounts of data and learn patterns of normal and abnormal behaviour, can identify anomalies, detect known attack patterns and adapt to new, previously unseen threats over time in a significantly shorter period than the traditional cyberattack detections.
The techniques can detect cyberthreats before they occur and prevent them by analysing historical data. They can identify indicators of compromise (IOCs) and predict potential attack vectors, allowing proactive defence measures to be put in place before an attacker causes damage. This is besides automating threat response and remediation actions, including blocking suspicious network traffic, isolating a compromised device or user.
In addition, proper legislation in ICT will help to establish clear rules, responsibilities and safeguards in areas such as cybersecurity, data protection, privacy, intellectual property, digital rights and ethical AI.
