Network Security & AI Defence - Part of Post Diploma Certificate in Enterprise Infrastructure and Cybersecurity (SDACL)

Training Provider: Ngee Ann Polytechnic
Course Reference: TGS-2026066238
S$404
Original: S$1,348
Save S$944

About This Course

The Network Security & AI Defence module provides an in-depth understanding of network security from a defensive perspective, with an emphasis on integrating AI-driven techniques for threat detection and response. It covers traditional security technologies such as firewalls, Virtual Private Networks (VPNs), and Intrusion Detection/Prevention Systems (IDS/IPS), alongside emerging AI-powered solutions for anomaly detection, automated threat intelligence, and predictive security analytics. Students will gain hands-on experience configuring and deploying state-of-the-art networking devices in enterprise network environments. They will learn to identify and analyse both internal and external network threats, develop and implement effective security policies, and apply AI-driven tools to enhance monitoring, incident detection, and response. By the end of the module, students will be able to combine conventional network security strategies with AI-based defence mechanisms to proactively protect an organization’s information assets.

What You'll Learn

To develop students’ ability to design, implement, and manage secure network environments by combining traditional security technologies with AI-driven defence techniques, enabling them to detect, analyse, and respond to network threats while applying security policies and advanced monitoring solutions to protect organisational information assets.

Entry Requirements

•Local Polytechnic Diploma in any discipline
•Recognised University Degree in any discipline
•At least 1 year of relevant work experience

Applicants with other equivalent qualifications will be considered case-by-case based on relevant work experience.
Other requirements:
•IT literacy, including operating system navigation, digital file management (creating, saving, organizing), and the use of standard productivity software (e.g., Microsoft Office, Google Workspace), is expected of learners.

Course Details

Duration 40 hours
Language English
Training Commitment Part Time
Total Enrolled New course
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