How Masai School is securing learning in the AI era

From self-hosted models to strict governance, Masai School outlines a blueprint for secure, ethical, and scalable AI in education.

As AI reshapes the future of education, cybersecurity has become as vital as innovation. In an exclusive interaction with Caroline Diana of DH, Nrupul Dev, Co-Founder and CTO of Masai School, discusses how the AI-first edtech platform safeguards data, ensures algorithmic fairness, and prepares learners for an era where technology and trust must grow together.

Masai School brands itself as an AI-powered learning platform. How are you integrating AI responsibly while ensuring that data privacy and platform security are not compromised?

At Masai, we follow a two-layer AI architecture. There’s a centralised AI layer that manages routing and governance, and then we use self-hosted open-source models for most of our major internal tasks. This set-up ensures sensitive data never leaves our secure environment. Every AI interaction is designed with context minimisation in mind, and we have provider-level guardrails to maintain data privacy and compliance with leading security standards. We focus on using AI responsibly, without compromising on safety and privacy.

Edtech platforms hold sensitive information—from student performance to hiring partner data. What are the key cybersecurity challenges you face, and how are you addressing them?

You are absolutely right! Edtech companies are custodians of sensitive information. Our biggest challenge lies in protecting multiple data types — academic, behavioural, and professional — from diverse sources. We address this through a three-tier approach: In-house cybersecurity experts overseeing data protection, external third-party audits to validate our defences and bug bounty programmes that invite ethical hackers to identify vulnerabilities proactively.

With cyberattacks rising across industries, how are you equipping learners with skills relevant to cybersecurity or secure coding practices, even if they’re not training as security specialists?

Security awareness is everyone’s responsibility, whether or not you are a cybersecurity professional. That’s why every Masai programme includes a core cybersecurity module covering secure coding, data protection principles, and real-world vulnerabilities. We also host industry-led sessions where cybersecurity professionals share current threat trends and hands-on defence practices, helping learners develop a security-first mindset regardless of their specialisation. The idea is to help every learner develop a security-first mindset.

As AI begins to influence student evaluation, mentoring, and career matching, what safeguards do you have to ensure fairness, transparency, and accountability in those algorithms?

That’s a key area of focus for us. All our AI-driven processes — from assessment to recommendation — go through multiple model validations. Any discrepancy between outputs triggers a human review layer. We maintain clear audit trails and regularly assess our models for bias, consistency, and explainability, ensuring fairness and accountability in every decision. This layered approach ensures that our decisions are efficient and fair.

Masai has scaled rapidly over the past few years. How do you balance speed of growth and product innovation with the discipline that cybersecurity demands?

It’s definitely a balancing act, and we take it very seriously. We have built a centralised service architecture with robust monitoring, logging, and version control baked in. Every new product or feature must pass through security gates before deployment. Additionally, independent audits ensure that while innovation moves fast, our compliance and data protection remain uncompromised.

Do you collaborate with cybersecurity firms or technology partners to expose students to real-world cyber defense scenarios or emerging AI security intersections?

Yes, our learners actively participate in industry hackathons and penetration testing exercises in collaboration with partner companies. These engagements give them hands-on exposure to live cybersecurity challenges and practical insights into how AI and security intersect in real-world systems. It’s where theory meets practice.

As AI and cybersecurity converge—attackers using AI as much as defenders—what trends do you see shaping the future of secure learning ecosystems?

The future of secure learning lies in human-AI collaboration. While AI enhances detection and response, human judgment and ethical reasoning will remain critical in defining boundaries. We’re also seeing a shift toward multi-model security frameworks, where several AI systems cross-validate decisions to reduce the risk of manipulation or bias. The future of secure learning will depend on this balance between intelligence and integrity.