A Remote Group Internship Project – Stratigus Consulting
This repository documents a remote group internship project conducted in collaboration with Stratigus Consulting, focused on exploring the cybersecurity and privacy challenges of generative AI and the emerging opportunity for Private AI solutions.
As part of this project, the team ultimately developed a prototype Private AI system to demonstrate how AI-powered analysis can be performed locally, without sending sensitive data to cloud-based large language models. The prototype serves as a practical illustration of the research findings rather than the starting point of the project.
The internship was completed over three months by a team of four students, following a structured, milestone-driven approach. One team member acted as the team lead, responsible for coordinating tasks, managing weekly deliverables, and integrating research, technical, and documentation outputs.
The final prototype developed during the later stages of the internship demonstrates a local-first AI analysis workflow designed with privacy and security in mind:
- Users submit a URL or query via a web interface or desktop application.
- Web content is retrieved through a controlled tool rather than direct browsing by the AI model.
- Analysis is performed by a locally deployed large language model using LM Studio, ensuring data remains within a trusted environment.
- Privacy safeguards, such as detection of potentially sensitive input and optional “no-store” execution, are applied before processing.
- Results are returned to the user interface for review or demonstration.
This system was built incrementally to support research, testing, and education, and is not intended to represent a production-ready AI platform.
Stratigus Consulting is a strategy and consulting firm dedicated to solving complex challenges across industries by leveraging innovation, technology, and evidence-based insights. Stratigus supports organisations in navigating disruption, designing scalable solutions, and building resilience in a rapidly changing business environment.
With a focus on future-oriented domains including cybersecurity, artificial intelligence, digital transformation, and sustainability, Stratigus combines strategic thinking with hands-on problem solving to deliver measurable outcomes.
Private AI: Cybersecurity and Privacy Challenges in the Age of Generative AI
Generative AI tools such as OpenAI’s ChatGPT, Microsoft Copilot, and similar cloud-based platforms have become mainstream across industries. While these tools deliver significant productivity gains, they introduce serious cybersecurity and privacy concerns, particularly because sensitive data is often processed and stored in the cloud.
For sectors such as:
- Law firms
- Healthcare providers
- Financial services
- Government organisations
the exposure of confidential or regulated data to cloud-based AI systems presents unacceptable risk.
This project responds to growing interest in Private AI — powerful AI systems that run locally, on controlled infrastructure, reducing external data exposure. While platforms such as LM Studio enable local AI deployment, many organisations lack clear guidance on:
- Risks and limitations
- Technical requirements
- Secure deployment practices
- Appropriate use cases
The internship project was designed to achieve the following objectives:
- Research and analyse cybersecurity and privacy risks associated with mainstream AI platforms.
- Investigate market demand for Private AI across industries and regions.
- Explore and test Private AI platforms, including local LLM deployments.
- Compare cloud-based AI and local AI from a security and privacy perspective.
- Develop educational and strategic resources to support responsible Private AI adoption.
- Provide Stratigus with market-informed insights and recommendations in the Private AI space.
As the project progressed from research into technical exploration, a prototype Private AI system was developed to demonstrate key concepts:
-
User Interaction
Users interact with the system via a web interface or desktop application to submit URLs or analysis queries. -
Controlled Web Content Retrieval
Web content is retrieved through a controlled tool function, limiting the AI model’s direct exposure to external inputs. -
Local AI Processing
Analysis is performed by a locally deployed LLM using LM Studio, ensuring data remains on the user’s machine or controlled environment. -
Security and Privacy Safeguards
The system includes safeguards such as:- Prompt constraints to mitigate prompt injection risks
- Detection of potentially sensitive input
- Optional redaction and “no-store” (ephemeral) execution modes
-
Results and Demonstration
Outputs are returned to the user interface and can optionally be logged for demonstration and audit purposes.
This prototype serves as a technical proof of concept, supporting the broader research and educational goals of the project.
Weeks 1–4
- Introduction to Stratigus and project objectives
- Review of the current AI landscape, focusing on cloud-based tools
- Identification of cybersecurity and privacy risks through literature and case studies
- Mapping of high-risk sectors (law, healthcare, finance, government)
- Design and distribution of surveys to understand market attitudes
- Analysis of survey responses to refine project scope
Deliverable: Interim Market Research Report
Weeks 5–7
- Exploration of Private AI tools such as LM Studio
- Comparison of cloud-based AI vs local AI from a security perspective
- Testing local LLM capabilities and limitations
- Examination of prompt injection and indirect prompt risks
- Documentation of hardware, software, and operational requirements
- Development of conceptual data-flow diagrams (cloud vs local)
Deliverables:
- Technical Comparison Report
- Draft Startup Guide (early version)
Weeks 8–9
- Drafting educational materials for non-technical audiences
- Refinement of startup guidance for deploying Private AI locally
- Design of explainer content and supporting diagrams
- Early development of user interaction concepts (web and desktop)
Deliverable: Draft Toolkit (Guide + Content Drafts)
Weeks 10–11
- Development of a simple digital platform to host resources
- Introduction of backend structure to support demonstrations
- Implementation of basic privacy and security controls
- Containerisation and environment-based configuration
- Drafting a communications plan for Private AI education
Deliverables:
- Website / Platform Prototype
- Draft Communications Plan
Week 12
- Integration of research, technical findings, and educational content
- Refinement of prototype system and user interfaces
- Final documentation and reporting
- Preparation of final presentation for Stratigus Consulting
Deliverables:
- Final Report
- Educational Video
- Website / Toolkit
- Final Presentation
DevOps practices were introduced after research and feasibility were validated, including:
- Git-based collaboration and version control
- Environment-driven configuration
- Docker and Docker Compose for reproducible deployment
- Separation of frontend, backend, and AI processing
- Basic health checks and service orchestration
These practices support scalability and demonstrate practical deployment considerations.
By the end of the internship, the team delivered:
- A comprehensive analysis of AI cybersecurity and privacy risks
- Market insights into Private AI demand
- A technical and startup guide for local AI deployment
- Educational content for business audiences
- A prototype digital platform demonstrating Private AI concepts
- Strategic recommendations for Stratigus Consulting
This project provided hands-on experience in:
- Cybersecurity and privacy analysis
- AI risk assessment and mitigation
- Consulting-style research and reporting
- Technical prototyping of AI systems
- Team collaboration and leadership
- DevOps foundations and deployment practices
This repository contains a research-driven prototype developed for educational and internship purposes. It is not intended for production deployment without further security review, governance controls, and operational hardening.
The Private AI internship project demonstrates how organisations can move from problem identification and research to practical, privacy-aware AI solutions. By combining market analysis, technical experimentation, and educational resource development, the project highlights both the risks of mainstream AI platforms and the opportunities presented by Private AI.