This foundation module covers core data protection principles, asset tracking, and threat modelling techniques using structural STRIDE methods. Students write clean code to configure secure operating system baselines and build secure design patterns. The course focuses majorly on handling privilege levels, applying system access controls, and reviewing architectural risk.

Students look into network layer models, protocol encapsulation rules, and layered architecture vulnerabilities across corporate systems. The module guides you through configuring firewall rules, setting up virtual private networks, and analysing live packet streams. Every week focuses on identifying routing threats, managing TLS handshakes, and deploying network telemetry monitoring.

Developing deep operating system architecture expertise forms the main goal, starting with binary arithmetic straight into memory segments of the program. Students examine running process layouts, explore kernel privilege level boundaries, and track system calls in virtual environments. This module enables students to detect memory corruption errors and allow platform mitigation such as secure boot.

This module covers symmetric and asymmetric primitives, encryption mechanisms, and key derivation functions. Students practise in running a certificate authority, performing public-key configurations, and handling full lifecycle of keys. Subsequent weeks focus on post-quantum cryptography threats, post-quantum cryptography algorithms benchmarking, and designing corporate infrastructure for flexible cryptography migration.

In this module, students focus on learning shared responsibility models, cloud identity access management policies, and zero-trust identity architecture. Here, students get practical experience in auditing permission sprawl, securing container images, and developing guardrails using policy-as-code. The course ends with multi-account landing zone governance, software bill of material generation, and protection of serverless infrastructures.

The module begins with information security management systems, corporate risk frameworks, and steps for drafting policy hierarchies. Here, students will learn about ISO 27001 requirements, NIST Framework mappings, and global data privacy regulations. In the later stages of the module, the emphasis is on assessing supply chain risks and conducting risk-based audit planning and reporting.

This module introduces defensive programming foundations using the Java runtime environment model to validate untrusted input streams. You will practise object-oriented design isolation, safe data serialisation, and secure relational database schema design. The course finishes by exploring parameterised SQL queries, database role access controls, and automated software testing tools.

Through this module, students learn to map attack surfaces using passive reconnaissance tools, network scanning, and vulnerability enumeration techniques. Students use authorised hacking frameworks to gain initial access, escalate local privileges, and pivot through target networks. The module covers web application API attacks, persistence tracking, and structuring professional penetration test reports.

This operational module builds capabilities in SIEM platform query development, log data normalisation pipelines, and threat intelligence tracking. Students map malicious intrusions to the MITRE ATT&CK framework and author automated detection engineering rules. The training path finishes by executing incident response playbooks and managing crisis communications during simulated breaches.

Students learn to triage harmful computer bugs inside isolated sandbox lab rooms using static and dynamic analysis tools. The course covers file header inspection, YARA rule signature authoring, code disassembly, and anti-analysis evasion bypass steps. Students also master volatile memory acquisition, forensic disk imaging, and reconstructing timeline artefacts for expert testimony reports.

This cutting-edge module covers machine learning attack surfaces, adversarial evasion tactics, and training dataset poisoning protections. Students demonstrate model extraction flaws and mitigate prompt injection bugs inside retrieval-augmented generation tools. The curriculum integrates a strong focus on agentic tool constraints, model provenance verification, and structured AI red teaming methodologies.

This module teaches students to frame clear computing research questions, conduct systematic literature reviews, and design empirical experiments. You will practise qualitative coding analysis, descriptive statistical math testing, and research data management plan writing. It ends with research ethics governance, coordinated vulnerability disclosure rules, and formal academic proposal development.