Launching into Computing

This module introduced the fundamental disciplines that underpin modern enterprise computing: Software Engineering, Artificial Intelligence, Cybersecurity, Data Science, and Enterprise IT Management. It connected technical principles to ethical, legal, and social considerations—particularly the role of computing in promoting equality, diversity, and inclusion (EDI). We examined how software design, security controls, AI adoption, data governance, and strategic IT management interact, and applied critical, evidence-based thinking to real-world enterprise challenges.

• Introduce key concepts across SE/AI/DS/Cyber.
• Build understanding of software design & implementation methods.
• Map functional/non-functional requirements to cybersecurity controls.
• Explain core AI principles and enabling technologies.
• Develop the ability to present critical, evidence-based arguments to varied audiences.
• Explore how computing can reduce barriers and support EDI.
• Encourage reflection and evaluation of personal development.

• Identify and critically analyse computing challenges and processes in business systems.
• Gather and synthesise information from diverse sources to design/analyse solutions.
• Evaluate methodologies, tools, and techniques to mitigate/solve computing issues and their business impact.
• Articulate legal, social, ethical, and professional issues faced by computing professionals.

Theme: The Evolution of Computing and Its Impact on Business and Society (4-week forum)

My initial post: Bridging the Awareness Gap: Cybersecurity Challenges in the Age of Digital Banking (5 May 2025)

• Argued that rapid digital banking adoption outpaced user awareness, increasing exposure to phishing, credential theft, fake apps, and QR scams.
• Proposed culturally relevant, accessible awareness programmes and digital literacy initiatives; cited Google DigiKavach as a scalable model.
• Supported by academic/industry sources on trust, adoption, and limitations of generic awareness campaigns.

Peer responses (highlights):

• Raised AI-driven threats (e.g., deepfakes) and parallels in other sectors (e.g., healthcare).
• Reinforced need for community-centric, culturally aware interventions and cross-stakeholder collaboration.

My summary (week 4): Synthesised class feedback, broadened beyond banking, and argued for gamified, role-specific training that respects context and addresses inequalities. Conclusion: Trust + collaboration + relevance are key to secure digital adoption.

Discussion feedback: “Excellent contributions across all areas… evidence-based, well-referenced; timely and constructive; reflective summary that linked back to class discussions.”

Part A — Individual Programming Exercise

Title: Evaluating the Development Models of Two Programming Languages

Task: Build two utilities (Python and JavaScript/Node.js) performing the same function and compare development model, readability, performance, scalability/maintainability, and security.

• Use-case: Scheduled ingestion of Microsoft Graph (Entra ID) users, sign-in logs, and audit logs into MongoDB for retention, analytics, and monitoring.
• Architecture: App registration + OAuth; batched Graph queries with pagination; idempotent upsert; counters & checkpoints; Dockerised.
• Dev models: Python (asyncio, readable, straightforward deps) vs Node.js (event-driven, async-first, rich npm ecosystem).
• Performance: Node.js had steadier/lower CPU (peaks ~16%, avg <6%) and finished ~1 minute faster; memory similar; I/O/network comparable.
• Security: No secrets in code; environment variables at runtime; container best practices.

Part B — Individual Presentation & Transcript

Title: AI-Driven Cybersecurity: Balancing Innovation with Ethical and Security Challenges

• Adoption & impact: SOC efficiency gains, Secure Score uplift, enterprise investment trends.
• Capabilities: Anomaly detection, SOC automation/XDR, phishing detection (Entra ID Protection, Defender XDR, Sentinel, CrowdStrike).
• Risks: Over-reliance, false positives, privacy & profiling, bias, explainability, adversarial ML (evasion, poisoning, inversion, drift).
• Governance: NIST AI RMF, EU AI Act, ISO 42001; alignment with SOC 2, GDPR, Zero Trust.
• Recommendations: Transparency, AI red teaming, human-in-the-loop, ethics boards, continuous model monitoring.

• Research & synthesis: Integrating academic, industry, and standards sources into applied designs.
• Programming practice: Async ingestion pipelines; Graph API; MongoDB; Dockerisation; operational logging.
• Evaluation & comparison: Runtime profiling; language trade-off analysis; operational/security implications.
• Ethics & governance: Applying AI RMF/ISO 42001; bias/privacy/explainability awareness; risk-based controls.
• Communication: Forum engagement, professional presentation, critical summaries.

Although much of the content aligned with my 20+ years of professional computing experience, the module reshaped how I think—especially around equality, diversity, and inclusion (EDI). My learning to date was largely on-the-job and implementation-focused; this academic approach encouraged me to step back, apply critical thinking, and ground decisions in structured research rather than purely operational intuition. It broadened my horizon, strengthened my ability to evaluate solutions against technical, ethical, and societal dimensions, and refined how I justify choices for enterprise stakeholders.