Product · 6 min read · Jul 2026

AI, Profiling And DPDP

W2 B9.png AI, Profiling And DPDP

How To Build And Use AI Systems Without Forgetting People's Rights

Published by Vishwaas.ai | DPDP Series

** AI Is Here. DPDP Is The Guardrail.**

If your product uses AI today, it probably does at least one of three things: it predicts what users might like, it scores them for some decision, or it watches behavior to spot patterns. In all three cases, personal data is the fuel.

The DPDP Act is not an AI law, and it does not try to define every kind of algorithm. But it does put guardrails around how personal data can be collected, used, shared and protected - including when AI and profiling are involved.

That means AI teams cannot treat DPDP as someone else's problem. The way models are trained, deployed and monitored now sits inside a formal privacy framework.

** What DPDP Actually Says About AI And Profiling**

Unlike some international regimes, DPDP does not have a separate, detailed chapter on AI. Instead, AI and profiling live inside wider concepts in the law: lawful processing, purpose limitation, data minimisation, security safeguards, accuracy and accountability.

The Act and Rules also send clear signals in specific areas - for example, restrictions on profiling and targeted advertising directed at children, and heightened expectations for Significant Data Fiduciaries that use emerging technologies at scale.

In practical terms, if an AI system processes personal data, it is expected to respect the same privacy principles as any other processing - it is just that the risks and impact can be larger.

** Profiling, Scoring And Automated Decisions**

Profiling and automated decision-making show up in many products: recommendation engines, risk scores, credit models, fraud systems, dynamic pricing, eligibility checks and more.

Under DPDP, these kinds of systems need to be aligned with:

Clear purpose definitions - why is the profiling done, and is it necessary?
Legitimate grounds - is there valid consent or another lawful basis for this processing?
Fairness and proportionality - are the data categories and features reasonably linked to the purpose?
User expectations - would an ordinary user be surprised to learn their data is used in this way?

Even though the Act does not spell out every AI pattern, these questions sit at the heart of whether a profiling system feels defensible under DPDP.

** Significant Data Fiduciaries And Algorithmic Accountability**

For organisations notified as Significant Data Fiduciaries, the bar is higher. Among other things, they are expected to pay special attention to how their algorithms and automated systems affect individuals.

Commentary on the law highlights obligations for SDFs to undertake due diligence around algorithmic systems, assess risks to Data Principals, and, where necessary, adjust models, features or review mechanisms to reduce harm.

This means SDFs cannot treat their AI stack as a black box. It has to be part of their data protection impact assessments, governance conversations and audit readiness.

** Training Data: Consent, Purpose And Minimisation**

AI systems are only as good as the data they are trained on. Under DPDP, that training data cannot be treated as a free resource if it contains personal data.

Practical implications include:

Checking that personal data used in training was collected with a lawful basis for the relevant purposes.
Avoiding unnecessary attributes in training sets when they add risk but little value.
Understanding whether downstream models may be used for new purposes that go beyond the original data-collection context.
Documenting how personal data is anonymised or pseudonymised where appropriate.

In other words, AI pipelines must plug into the same consent and purpose logic as the rest of the product.

** Bias, Explainability And User Trust**

AI systems that use personal data can produce unfair or opaque outcomes if they are not checked. While DPDP does not provide a full algorithmic-fairness manual, it does create a strong incentive not to ignore bias and opacity.

From a trust and compliance angle, it is wise to:

Test models for outcomes that look discriminatory or systematically unfair.
Track which features are driving sensitive decisions, especially in credit, employment, access or benefits contexts.
Give users at least high-level explanations of how automated decisions are made when those decisions matter to them.
Provide a way to challenge or review important decisions rather than hiding everything behind 'the system'.

These practices will not only help from a DPDP perspective. They are also good product and ethics hygiene.

** Children, Vulnerable Users And High-Risk Cases**

When AI or profiling involves children or other vulnerable groups, the risks increase sharply. DPDP already restricts profiling and targeted advertising aimed at children, and expects extra care in how their data is processed.

For high-risk use cases - such as health-related predictions, credit scoring, behavioural tracking or eligibility decisions - organisations should treat risk assessment as a non-negotiable part of their AI design process.

In practice, that means more conservative data choices, simpler explanations, and a higher threshold for what is considered acceptable.

** Governance: Bringing AI Into Your Privacy Program**

Many organisations still have a gap between their privacy program and their AI program. DPDP makes that separation harder to justify, especially for companies using AI at scale.

A more mature approach is to:

Include key AI systems in data protection impact assessments.
Map which models use which categories of personal data.
Ensure your DPO or privacy lead has visibility into major AI deployments.
Treat AI incidents (for example, harmful outputs or misuse of training data) as part of your breach and grievance framework.

This turns AI from a separate innovation thread into a visible part of your overall data governance story.

** What Product And Engineering Teams Can Start Doing Today**

Waiting for a future 'AI law' is not a strategy. Under DPDP, there is already enough structure to start improving how you build and run AI systems that touch personal data.

Concrete steps include:

Label your AI use cases that involve personal data and classify their risk.
Make sure each use case is tied to a clear purpose and legal basis.
Tighten access to training and inference data, especially in non-production environments.
Create simple internal documentation for each important model: what data it uses, what decisions it influences and how it is monitored.

None of this stops innovation. It just ensures that innovation respects the people whose data makes it possible.

** Where Vishwaas.AI® Fits**

As AI moves deeper into products, privacy tooling needs to catch up. Organisations will increasingly need platforms that can link consent, purpose, data flows and model usage in one place.

That is where trust-focused platforms like Vishwaas.AI® can help - by mapping personal data pipelines, capturing the legal basis for key AI use cases, connecting grievance and breach workflows to AI incidents, and giving leadership a consolidated view of how AI interacts with DPDP obligations.

The goal is not to slow down AI. It is to make sure that as AI gets smarter, the way you handle people's data gets smarter too.

(c) Vishwaas.ai | DPDP Compliance Made Simple