India's education sector has undergone rapid digitisation over the past decade. From homework assignment and daily class updates to consent forms for trips and activities, bus tracking and attendance tracking through mobile applications, everything is online and in real time. Learning management systems, record engagement patterns and behavioural monitoring tools generate risk flags.
What was once episodic and paper-bound, and merely administrative record-keeping has become persistent, granular and deeply embedded data processing, much of it involving children. Yet most institutions continue to rely on consent models designed for a paper-based era: a bundled declaration at admission, a static parental signature and terms buried inside multi-page forms.
Against this backdrop, the Digital Personal Data Protection Act, 2023, and the Digital Personal Data Protection Rules, 2025, mark a decisive regulatory shift. Compliance deadlines expiring in May 2027 signal that this is not a cosmetic update. For educational institutions, the shift is structural. It requires rethinking how student data is classified, collected, processed and governed.
The conversation must begin with three foundational questions:
- When is an institution a Data Fiduciary, and when is it merely a Data Processor?
- What is an "educational institution" and what are the exemptions for processing children's data?
- How must consent for adult students be restructured?
And from there, the organising principle becomes clear: consent architecture.
Why Data Fiduciary vs Data Processor Distinction Is Foundational
The DPDPA applies to "Data Fiduciaries" and "Data Processors." The distinction determines which entity will: (a) decide the purpose and means of processing; (b) obtain consent; (c) answer to regulators; and (d) bear liability.
A Data Fiduciary determines the purpose and means of processing personal data. A Data Processor processes personal data only on documented instructions of a Data Fiduciary and does not independently decide how or why the data is used.
In the education sector, this distinction is frequently misunderstood. In most operational contexts, educational institutions function as Data Fiduciaries. They determine:
- Why is student data collected?
- Which digital systems participation is mandatory in?
- How academic performance is assessed?
- How attendance is tracked
- How disciplinary records are maintained?
- How is welfare monitoring conducted?
Educational institutions act as Data Processors only in narrow circumstances. For example, where they process examination data strictly on behalf of a statutory board, or where they operate under contractual instructions without discretion. Such scenarios are limited.
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Consent as Organising Principle of Educational Data Processing
Consent in the educational context occupies a uniquely complex position. Unlike many commercial environments, participation in educational systems is rarely meaningfully optional. Students and parents often have no practical alternative but to engage with institutional digital infrastructure. This makes consent both central and structurally fragile.
Under the DPDPA, consent must be free, specific, informed, unambiguous and capable of withdrawal. Translating this into the educational context is operational rather than purely legal.
Educational institutions collect personal data across multiple systems and touchpoints, such as, admissions portals, onboarding forms, learning management systems, attendance tools, assessment platforms, communication portals, mobile applications, etc.
Consent is rarely collected once. It is fragmented across systems and often embedded within lengthy forms. This fragmentation creates risk, as data collected for admissions cannot later be repurposed for analytics, behavioural monitoring or new services unless clearly aligned with disclosed purposes or supported by lawful grounds.
Consent is not a document - it is an operational capability. To function effectively, institutions must design consent architecture around: (a) when is consent first obtained; (b) how is it recorded; (c) how is it linked to specific purposes; (d) how it travels across vendors and systems; (e) how are withdrawals operationalised; and (f) how are consent records audited? Therefore, since the purposes of data processing are varied, consent may also need to be specifically recorded at multiple touchpoints. Such as, consent for processing data at the admission enquiry stage is fundamentally different from consent at the admission stage, where significantly more data, including medical and health history of the student, may be collected.
What's Educational Institution? Exemptions for Processing Children's Personal Data
The Rules introduce a specific definition of "educational institution", an institute of learning that imparts education, including vocational education. While schools, colleges and universities fall squarely within this definition, kindergarten, vocational centres, and certain training schools may also be covered.
The Rules identify limited circumstances in which personal data of a child can be processed without obtaining verifiable parental consent for tracking and behavioural monitoring. These circumstances include: (a) provision of educational activities; and (b) safety of children enrolled with such educational institutions, including real-time location, necessary for protection or security. Thus, if institutions fall within the definition of educational institution, and are processing data for the said purposes, verifiable parental consent requirements may be exempted.
Apart from this above noted exemption, educational institutions need to obtain verifiable parental consent in line with DPDPA for all other processing of children's personal data. Additionally, educational institutions must also obtain consent from parents and guardians for processing their own personal data.
Historically, educational institutions would obtain consent at the time of admission and then rely on it for years. The DPDPA disrupts that model. Consent cannot be static, and therefore institutions, for non -exempted categories, must:
- Take verifiable parental consent for processing minor's personal data;
- Identify when a minor student crosses the age threshold;
- Trigger re-consent workflows for students who have attained majority; and
- Manage scenarios where consent is withdrawn without denying essential educational access.
Recently, the Madras High Court in Ameer Alam v. The Government of Tamil Nadu observed that indiscriminate data collection without adequate safeguards would violate the right to privacy. This highlights a principle that institutions cannot ignore - educational objectives do not justify limitless data extraction. This ruling captures the legislative intent of the DPDPA even before its compliance deadline, which is proportionality. For schools, therefore, children's data governance must be purpose-bound, minimal and demonstrably safeguarded.
That said, entities that do not fall within this definition of educational institution cannot assume the benefit of these limited exceptions and must structure children's data processing strictly in line with DPDPA requirements, including obtaining verifiable parental consent for all processing of children's personal data.
Students Above 18: Adults Within the Educational System
Higher education institutions face a different but equally complex challenge. Mixed cohorts are common. Some students are minors; others are adults. Uniform consent flows fail in such environments.
For students above 18, consent must be obtained directly from the individual. Reliance on parental authorisation becomes legally insufficient. Institutions must design systems capable of:
- Differentiating between minor and adult students
- Dynamically refreshing consent status;
- Obtaining re-consent as a minor attains majority; and
- Ensuring adult students exercise independent data rights.
Therefore, consent should be designed as a pathway aligned with institutional operations rather than treated as a one-time compliance artefact. From pre-admission counselling to graduation and alumni relations, data use evolves - so must consent.
Institutions that treat consent as an onboarding checkbox will struggle to meet the DPDPA's expectations - not because of bad intent, but because systems were not designed for legal adaptability.
Closing Perspective
For educational institutions, compliance with the DPDPA is not about drafting better privacy policies. It is about recognising that data processing sits at the core of how modern education architecture of admissions workflows, learning platforms, attendance systems, behavioural monitoring tools and vendor contracts. This is not an argument for over-compliance - it is an argument for thoughtful design. Moreover, educational institutions set the foundation of learning and play a critical role in shaping students into responsible citizens. They are therefore uniquely positioned to lead by example when it comes to protecting privacy.
Institutions that address these questions early, deliberately and structurally will navigate the transition with credibility. Those that defer them may find that retrofitting consent and accountability into existing systems is far more complex than anticipated. Further, the substantial penalties prescribed under the DPDPA underscore that data protection compliance is not merely a regulatory formality but a matter of significant institutional consequence that warrants immediate attention.
In the education sector, data protection is no longer peripheral to pedagogy. It is foundational to institutional legitimacy.
The article has been authored by Huzefa Tavawalla, partner (head-digital disruption); Aarushi Jain, partner (head-media, education & gaming); and Tannvi R, associate at Cyril Amarchand Mangaldas.
Disclaimer: The views expressed in this article are solely those of the author and do not necessarily reflect the opinion of NDTV Profit or its affiliates. Readers are advised to conduct their own research or consult a qualified professional before making any investment or business decisions. NDTV Profit does not guarantee the accuracy, completeness, or reliability of the information presented in this article.
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