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Thursday, February 26, 2026

In this episode of The AI Space Podcast, host Sanjay Kalluvilayil sits down with Siyethaba Nxumalo, Founder & COO, of CipherGenix to explore how this 22-year-old founder from Johannesburg, South Africa is building AI-specific cybersecurity for the healthcare industry , protecting not just data, but patient lives.

Ethics in healthcare AI is paramount, as a compromised AI model can lead to dire consequences for patient safety. In healthcare, ethical AI is not optional. A poisoned diagnostic model can produce false results and put real lives at risk. We discuss why traditional cybersecurity falls short against threats like data poisoning and model theft, the importance of explainability and responsible AI, and how robust security measures give healthcare AI companies the foundation to innovate with confidence.

Why Healthcare AI Needs Its Own Security Layer

Traditional cybersecurity can't detect when AI models are being poisoned, stolen, or manipulated. CipherGenix is building data poisoning detection, model theft prevention, and real-time adversarial attack monitoring specifically for healthcare.

Customer Discovery Before Product: The Lesson Every Founder Needs

After a failed first startup, Siyethaba learned to fall in love with the problem, not the solution. CipherGenix is conducting 50+ interviews with healthcare leaders before launching, targeting mid-size digital AI clinics first to build case studies before scaling to enterprise.

Building a Global Startup from Africa: Mission Over Money

With co-founders across South Africa, India, and Houston, CipherGenix turns its distributed team into a 24-hour productivity advantage, raising a $1M pre-seed in milestone-based tranches while staying lean pre-revenue.

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AI Explainability
The ability to understand and interpret how an AI system arrives at its outputs or decisions. It involves making the model's reasoning, inputs, and logic transparent enough that humans can follow why a particular result was produced. This is especially important in high-stakes domains like healthcare, finance, and law, where accountability and trust in AI decisions matter greatly.

Data Poisoning Detection
Methods used to identify and mitigate malicious or corrupted data that has been intentionally introduced into training datasets to manipulate an AI model’s behavior. It combines statistical analysis, anomaly detection, and data validation techniques to preserve model integrity, reliability, and trustworthiness.

Fat Finger
In a data entry context is an American English idiom that refers to an accidental input error caused by mistyping or pressing the wrong key, typically due to human slip rather than misunderstanding. The term originated in trading and finance and is now widely used in tech and data entry to describe simple typing mistakes that can propagate errors if not caught by validation.

Machine Learning
Core part of AI in which systems learn patterns from data to make predictions or decisions without being explicitly programmed for every task. Instead of following fixed rules, AI models improve their performance over time as they are exposed to more data.

Model Theft Prevention
Techniques and safeguards designed to prevent unauthorized copying, extraction, or replication of proprietary models through attacks like model stealing, API scraping, or reverse engineering. It combines technical controls, monitoring, and legal protections to preserve the intellectual property, competitive advantage, and safety of deployed AI systems.

Principle of Least privilege
A security concept where users, systems, and applications are given only the minimum level of access needed to perform their required tasks, and nothing more. By limiting permissions, it reduces the risk of accidental misuse, insider threats, and damage from compromised accounts.

Glad to collaborate with Akshay Mittal sand showcase this event with our Community Partner, ACM Austin.

Glad to collaborate with Derrick Bozkurt and showcase this event with our Community Partner, Plano Prompt Engineers.

I have been invited to be the moderator for a panel discussion at the Mind & Machine x Mythworx event: Beyond the Model | The AGI Moment. Please join me in attending. Great to partner with Tori Begg and Tim Mata on future events.

I have been invited to be the moderator for a panel discussion at the Dallas Physical AI event. Please join me in attending. Great to partner with David Cao.

I have been invited to be the moderator for a panel discussion to kick off this event and help plan it. Please join me in attending. Great to partner with Derrick Bozkurt

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We will share the best responses in the next issue.

Where would AI create the most value in your workforce today?

⬜️⬜️⬜️⬜️⬜️⬜️ Improving demand forecasting accuracy (0)
🟩🟩🟩🟩🟩🟩 Automating shift swaps and scheduling (2)
🟨🟨🟨⬜️⬜️⬜️ Reducing burnout with fatigue scoring (1)
⬜️⬜️⬜️⬜️⬜️⬜️ Ensuring compliance and rule enforcement (0)
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The AI Space Podcast