Cybersecurity & Digital Solutions Intern

Cyshield · · Cairo, Egypt

The four-week Cyshield Egypt internship split each day between cybersecurity and AI & digital solutions. On the cybersecurity side I moved from governance and risk into secure infrastructure, digital forensics, and hands-on penetration testing. On the AI side I covered full-stack development, cloud-native tooling, and applied machine learning, building an end-to-end Whisper-to-GPT-2-to-gTTS speech pipeline as my main project.

Internship cohort celebration

Production Design

The speech pipeline design also considered two production requirements beyond the delivered demo: multilingual interaction and real-time streaming.

For multilingual support, the approach would be a multilingual Whisper variant, language detection on the ASR output, and routing text to an LLM trained for that language, using translation as an intermediate step (ASR, then translate to English, then generate, then translate back) for languages without a strong native-language LLM available.

For low-latency, real-time use, the sequential ASR-then-LLM-then-TTS structure would need to become a streaming one: chunked ASR emitting partial transcriptions as audio arrives, the LLM pre-generating against those partial transcripts, and incremental TTS starting to speak before the LLM finishes its full response, all coordinated through async queues instead of blocking stages.

Four Weeks:

Cybersecurity track

  • Governance, Risk, and Compliance (GRC) foundations: security policies, frameworks, controls, risk management, and compliance reporting
  • Digital Forensics and Incident Response (DFIR): evidence acquisition with KAPE and forensic analysis aligned to GIAC GCFA concepts, alongside network and web security fundamentals
  • Web application penetration testing through PortSwigger Academy and Burp Suite, covering fuzzing, stored XSS exploitation, and webhook-based attack surfaces
  • Network reconnaissance and traffic analysis with Wireshark, plus broader network scanning, vulnerability detection, and exploitation-technique practice
  • OverTheWire war games, regex-driven log analysis, and OSP/CH cyber-range exercises for applied investigative problem-solving
  • Security Operations Center (SOC) workflow simulations, vulnerability scanning, incident-response procedures, and secure coding practices for identifying and patching flaws

AI & digital-solutions track

  • Full-stack development with the MERN stack, alongside mobile prototyping in DartPad (Flutter), Kotlin, and Swift, and exposure to ERP platforms
  • Cloud-native fundamentals through Kubernetes (CKA/CKAD-aligned material) and DevOps workflows, including container orchestration and deployment pipelines
  • NLP, speech-processing, and computer-vision fundamentals, applied directly to NLP/CV model-building and data-engineering tasks
  • Big data and business-intelligence concepts alongside embedded-systems topics: ORAN software development lifecycle, LTE air-interface protocols, and IoT/smart-systems integration
  • Rapid prototyping on low-code/no-code platforms (Odoo, Webflow, Squarespace) for portfolio sites and quick builds

Completed Work

Speech-to-speech pipeline

A complete voice-interaction system chaining three open-source models end to end inside Google Colab: OpenAI's Whisper (whisper-tiny.en) for automatic speech recognition, GPT-2 for generating a conversational text response, and Google's gTTS for converting that response back into speech. Built and wired the full ASR-to-LLM-to-TTS sequence, plus the surrounding model loading, GPU/CPU device configuration, sample-data preparation through Hugging Face's datasets library, and debugging of dependency and memory issues along the way.

Built with
Whisper (whisper-tiny.en), GPT-2, gTTS, Hugging Face Transformers, Google Colab
Contribution
Built and integrated all three pipeline stages, including the surrounding device-configuration and debugging work.
Outcome
A working speech-in, speech-out conversational demo running entirely on free-tier Colab compute.

Technical Stack

Cybersecurity skills

Digital forensics (KAPE, GIAC GCFA concepts), Web application penetration testing, Network traffic analysis, GRC and SOC workflows, Secure coding practices

AI and software-engineering skills

MERN stack development, Mobile prototyping (Flutter, Kotlin, Swift), Kubernetes and container orchestration, NLP, speech processing, and computer vision, Speech pipeline engineering

Tools and platforms

KAPE, Wireshark, PortSwigger Academy, Burp Suite, OverTheWire, Whisper, GPT-2, gTTS, Hugging Face Transformers, Kubernetes, Docker, Odoo, Webflow, Squarespace

Learning Path

  1. Governance and risk foundations

    GRC concepts, security policies, and compliance reporting came first, as the base for every later security exercise.

  2. Secure infrastructure and forensics

    Network and web security fundamentals, incident response, and KAPE-based digital forensics work followed, aligned to GIAC GCFA concepts.

  3. Applied penetration testing

    PortSwigger Academy and Burp Suite modules covering fuzzing, stored XSS, and webhooks, alongside Wireshark reconnaissance, OverTheWire war games, and OSP/CH cyber-range exercises.

  4. Building the speech pipeline

    The AI and digital-solutions track integrated Whisper, GPT-2, and gTTS in Colab alongside its broader MERN, Kubernetes, and applied machine learning material.

Problems & Handling

Whisper-tiny.en was selected for its speed and low resource usage, but its lower accuracy creates risk in production settings where transcription errors are costly.

The appropriate model size depends on the use case's accuracy requirement and available compute, rather than defaulting to the largest option.

Whisper-tiny.en remains appropriate for a proof of concept or low-resource environment. Production would use whisper-small or larger with quantization, batching, and mixed precision, or a hybrid setup that reprocesses uncertain cases with a larger model.

A defensible path from a Colab demo to a production-accuracy pipeline, without needing to redesign the pipeline's structure.

The pipeline broke when importing Conversation from transformers, because the class had been moved or deprecated in a newer library version than the one the code was written against.

Colab's package versions can drift without warning between sessions, so a notebook that works today isn't guaranteed to still import cleanly tomorrow.

Pin dependencies in a requirements.txt with exact versions and test inside a reproducible Docker environment, so breaking API changes in a library like transformers show up before a demo or deployment, not during one.

A clear practice for keeping the pipeline's dependencies from becoming a surprise failure point.

The pipeline hit CUDA out-of-memory errors during inference, and torch.cuda.empty_cache() only ever bought short-term relief rather than fixing the underlying pressure.

Sustainable memory headroom needs to come from the model and infrastructure choices themselves, not from clearing the cache after the fact.

Lower-parameter model variants, int8/fp16 quantization, gradient checkpointing for training scenarios, workload partitioning across multiple GPUs, and inference servers like Triton that handle memory reuse natively.

A scaling plan designed for sustained inference load rather than short-term memory recovery.

Summary

Programme length
4 weeks (Aug 2025)
Tracks completed
Cybersecurity, AI & digital solutions
Pipeline models chained
3 (Whisper, GPT-2, gTTS)