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It is interesting to observe how DGPSI (Data Governance and Protection Standard of India) has emerged as an innovative “Standard” for DPDPA compliance distinguishing itself from any other available compliance frameworks.

One of the Cardinal Principles that has driven DGPSI to the front is the concept is a recognition that though DPDPA is about compliance of a Binary system of Data representation. The statutory definition of personal data appears binary, but the operational reality of data processing is dynamic and contextual. Many of the compliance activities appear to be more aligned to an “Analog” way of thinking where data does not remain a particular type of data and exhibits a transformation over a life cycle. This was first captured by the undersigned in “Naavi’s Theory of Data” under the second hypothesis which stated  that data life cycle is a “Reversible Lifecycle”.

In this hypothesis, data is recognized as a state which is a function of context, purpose, observer etc and can be mathematically expressed as

DATA STATE = f(data + context + purpose + processing + observer + available knowledge)

[Also refer:  “The New Theory of Data”: October 7 2019 ]

What this theory suggested was that Data Can be non personal to start with and during its lifecycle, may become identifiable, become sensitive, become non identifiable again etc. The “identiifiablity” may be because of the “Processing” or the “Processor” whose prior knowledge may make it identifiable.

The “Identifiability” is therefore a quality that gets assigned either because the data comes as a set of multiple data parameters which together make it identifiable to a particular person or the data element is being observed by a person who with his prior knowledge can identify that the data belongs to a specific person. Hence “Processing” or the “Context of processing” or the “Observer’s knowledge” determines whether a data is  personal or not.

Hence the status of data is not a “Binary” status that it is “Personal” or “Not personal”. It is driven by the Quantum principle of probability that it may be personal or not personal depending on the environment in which it is observed.

The principles of Physics namely the Debroglie principle of matter-wave duality and Heisenberg principle of uncertainty that the act of measurement of one parameter may change another parameter so that position and velocity of a particle cannot be simultanewously determined, aptly represent this status of personal data.

Under the principles of Quantum Physics again applied to this scenario, we can consider that data moves from one qualtum state to another (non-personal, personal, higher-risk or otherwise specially regulated states, and ultimately anonymised/non-personal states) and exists in a continuum of these multiple states which looks like a continuous anolog status.

When we apply “Compliance Controls”, some of which are applicable to personal data and not applicable to non personal data etc., there is a need for the Controls to also adopt to the changing status of the data. Here in lies the challenge of DPDPA Compliance.

This “Continuum” of data status also extends to the status of an organization such as “Data Fiduciary”. The same organisation may occupy different regulatory roles in different processing relationships: it may be a Data Fiduciary for one processing activity and a Data Processor for another. A Data Fiduciary may additionally fall within the Significant Data Fiduciary regime when notified by the Central Government..

Yet another area where this “Naavi’s Quantum Theory of DPDPA Compliance” becomes visible is in the transformaion of Legal Basis of processing as well as Data Valuation.

The legal basis of processing recogniszed is Consent or Legitimate use or Exemption. Hence a Data fiduciary has to first check if the data or its processing is exempt, if not is it covered by legitimate use and if not obtain an appropriate consent. But having determined the purpose as being based on one of these three “Legal Basis”, the data fiduciary cannot consider it as a pemanent tag on the data processing  as the legal basis can transform during the processing.

One example is the data of a person brought to a hospital in an unconcious state by a stranger. At this stage the processing of the data is covered by a “Medical Emergency” which may be a legitimate use. Once the emergency situation ends, the legal basis for subsequent processing must be reassessed. Where no other applicable legitimate use or exemption exists, consent may become necessary for the relevant processing. After a while the hospital may realize that the patient is an accident victim or a terrorist or has a notified decease which requires disclosure to specified authorities. At this stage the processing related to “Disclosure” becomes “Legitimate use” once again.

For compliance, we say every process is to be supported by a policy which states whether the legal basis is either legitimate use or consent or exemption. But this policy support needs to change dynamically during the processsing of the patient data in the above scenario. The legitimate use policy is applicable to the emergency casualty ward but not for the inpatient during a concious state but becomes applicable if the context demands.

Similar changes also affect “Data Valaution” which may be “x” at the time of cretion, “y” after a processing stage and “z” after another processing stage.

In an educational environment data of a Person at the stage of application, admission, examination, qualification, alumni etc is all personal data of one person but at different points of time, it has different purpose of use and is supported by different legal basis.

DGPSI recommends the use of an SSOT (Single Source of Truth) based data inventory and process based system of compliance management both of which are “Quantum Principles”. The Data Inventory consists of one data set for a Data Principal but has multiple groups of data elements linked to different processes. The different processes are themselves part of an Inventory of processes which is a quantum continuum of processes that aggregate to the enterprise processing.

Summarizing, we may state

“The legal status and compliance significance of data cannot always be managed as a static attribute of a data field; they have to be evaluated in relation to the data, purpose, processing operation, context, actor and stage of the data lifecycle.”

Probably this discussion is not meant for every Data Protection Officer for whom Data status is binary and controls are applied either one way or the other. But for those Data Protection Professionals who can think beyond the obvious, this presents an opportunity to find innovative ways of data processing which is both compliant to the DPDPA and also functionally optimal.

Just as the gear systems of Cars evolved from the manual step based system to a continuous variable transmission system, the DPDPA Control mechanism has to also evolve from the current “Binary” system to a “Continuosly Variable” system based on the concept of “Continuum of Quantum States”.

I am aware that I am mixing up the concept of Physics with the Data Protection compliance and probably confusing both audiences. But this confusion would be temporary. From this discussion will emerge a new “Theory of Compliance” that is compatible to the concept of Quantum theory of “State of Matter”.

If we further summarize the concepts for simplification, we can state:

Quantum Principle 1 — State

A data element does not have one immutable compliance identity. Its state depends upon:

Data + Context + Processing + Knowledge + Purpose

Quantum Principle 2 — Transition

A processing operation can change the compliance state.

For example:

Collection → enrichment → profiling → pseudonymisation → disclosure → anonymisation

Each transition can alter the applicable controls.

Quantum Principle 3 — Observation

The ability of an observer/processor to identify or derive information from data can alter its practical significance. That connects directly to the first hypothesis of Naavi’s Theory of Data,  “data is in the beholder’s eyes” proposition in your Theory of Data.

P.S: The reference to Quantum Theory in this article is an analogy for state-dependent and context-dependent compliance, and is not intended as a claim that data protection follows the laws of quantum physics

A Word about DGPSI

DGPSI does not merely ask “What data do you have?” It asks “What processing is being performed, for what purpose, under what legal basis, on what data state, by whom, with what controls?”

It also explains why the SSOT + Process Inventory + Data Inventory architecture becomes important.

Instead of:

Employee Data = Personal Data = Apply Controls A, B, C

DGPSI effectively asks:

Employee Data
↓
Recruitment process
↓
Employment process
↓
Payroll process
↓
Benefits process
↓
Performance process
↓
Exit process
↓
Archival/retention
↓
Deletion

The same individual and much of the same dataset can pass through different purposes, processes, users, systems and legal bases.

That is the operational meaning of “continuum”.

AI Challenge

The above discussion presents a real challenge to AI. Because AI systems can dynamically:

• infer new attributes;
• combine datasets;
• create new relationships;
• identify previously unidentified individuals;
• generate profiles;
• transform data;
• produce new derived data;
• change the risk associated with an existing dataset.

Therefore, AI can cause state transitions in data without the underlying database being materially changed.

This represents a bridge from

Theory of Data → DGPSI → DGPSI-AI (AIGSI)

Let us expand this thought further in a different article.

To conclude:

The future of DPDPA compliance  cannot be a static checklist applied to static categories of data. It has to become a continuously adaptive control system that recognises changes in data state, processing purpose, legal basis, organisational role and risk.

This is the direction in which DGPSI seeks to take compliance—from a binary, checklist-oriented model towards a process-driven, state-sensitive and continuously variable compliance architecture.

And this is precisely why AI makes the challenge more difficult: AI itself can become the mechanism through which data changes state.

Comments are welcome.

Naavi

Video Overview

Audio Overview (21 mts)

On 1st September 2026, FDPPI in association with Consentera, Delhi conducted a one day event at Constitutional Club of India, Delhi as part of the IDPS-2026, (Delhi Leg). IDPS the flagship event of FDPPI was conducted last year in Bengaluru and Chennai. This year it is beign conducted in Delhi and Bengaluru (November 21).

During this event, Naavi presented an Introduction to DGPSI. It was proposed to further discuss AI in Data Privacy which due to paucity of time could not be conducted.

This presentation is discussed here along with AIGSI (AI Governance Standard of India) which is now under public discussion.

Comments are welcome.

The discussion on DGPSI presented at Chennai event on August 28, where FDPPI and MMA conducted an one day workshop on “Beyond the Frontiers of DPDPA” is also available below a.

Naavi

The war of Kurukshetra was a war to establish  “Dharma” in the country. If we consider that Building the culture of Data Protection in India is a “Dharma”, FDPPI is in the forefront of establishing the DPDPA Dharma in India.

The chariot of FDPPI driven by the AIDAI carries the flag of DPDPA across the country to build an ecosystem that creates skill sets, establishes standards and provides the manpwer required tobe data auditors in India.

For those of us dedicated to Information Security, Cyber Law, and Data Protection in India, the image of Krishna and Arjuna at Kurukshetra is not merely art; it is a strategic blueprint. It illustrates how various regulations, frameworks, and audits must synchronize to achieve “Victory” (Compliance and Security) in the digital age.

Let us decode this “compliance chariot” to understand the roadmap for Indian organizations.

The Chariot: Powered by FDPPI

At the very foundation of this entire ecosystem is the chariot itself. and it is FDPPI (Foundation of Data Protection Professionals in India).

Before a data fiduciary can dream of galloping toward growth or facing the “battle” of market competition, it must have a robust, legally sound, and technologically secure vehicle. FDPPI represents the foundational capacity building, education, and professional expertise that organizations need. Without the structural integrity provided by FDPPI-certified professionals and methodologies, the entire compliance mechanism is prone to collapse under pressure.

The Flag: Flying High with DPDPA

At the highest point of the chariot, signaling its allegiance and purpose, is the flag flying labeled DPDPA (Digital Personal Data Protection Act).

This symbolizes that the ultimate mandate guiding the organization is the Data Protection law of the land. Every movement, every strategic decision, and every process must serve the ultimate goal flying from the flagpole: compliance with the DPDPA, protection of Data Principal rights, and adherence to obligations of the Data Fiduciary. The DPDPA is the “Dhruva Nakshatra” that defines the direction of the journey.

Arjuna: Armed with DGPSI

Seated within the chariot, poised to act but requiring guidance, is Arjuna. 

Arjuna represents the organizational leadership or the key decision-makers who must fight the daily operational battles. However, a warrior without a reliable guide to the terrain is ineffective. DGPSI (Data Governance and Protection Standard of India) acts as Arjuna’s compass and shield. It is the framework that measures, audits, and guides the organization’s data protection posture. It transforms leadership intent into measurable governance. 

The White Horses: Driven by AIDAI

Finally, we come to the power source—the magnificent four white horses pulling the entire apparatus forward at breakneck speed. They represent AIDAI (Association of Independent Data Auditors of India).

If DPDPA sets the rules, and FDPPI builds the vehicle, AIDAI is the sheer engine power driving modern business. 

Synchronized Dharma

The true genius of this visual is that it demonstrates that compliance is not a static checklist; it is a dynamic, coordinated act.

In this digital Kurukshetra, victory belongs to the organizations that can synchronize these elements into one unified “Digital Dharma.“

At Naavi.org, we have always championed this holistic view. Whether you are looking to build your chariot, understand your flag, arm your warriors, or train your steeds, the path to compliance starts with recognizing how these forces intersect.

While FDPPI endeavours to install the DPDPA culture across the county, we rememebr that it is our duty to fulfill our obligations. Success is left to the almighty with power. 

Naavi

The siren song of “AI-First” is intoxicating. Corporate boardrooms across India are demanding rapid integration of Generative AI (GenAI) and automated systems to enhance efficiency and competitive edge. However, a stark visual reality check is required for every Indian CEO, CIO, and Legal Counsel. As the infographic accompanying this post illustrates so powerfully, the push for AI integration must be balanced against an equally powerful gravity: Total Legal Liability and Existential Privacy Risks.

At Naavi.org, we have long advocated that technology is a magnificent servant but a dangerous master. This has never been truer than with Artificial Intelligence.

The Chained Giant: The Myth of “AI Liability Immunity”

Let us be absolutely clear, as visualised on the left side of our guide:

“Under Indian law (ITA 2000), the legal liability for AI-driven actions rests solely with the system owner/deployer.”

There is a pervasive myth among less tech-legal-savvy organizations that if an “AI made a mistake,” it’s an unforeseeable event beyond human control. This is false.

We draw your direct attention to ITA 2000, Section 11. The statute does not recognize algorithms as sentient legal persons. When an AI processes data, generates a decision, or takes action on behalf of an organization, it is considered, for legal purposes, an extension of the data fiduciary or the system owner.

If your AI leaks personal data, hallucinating nonsensical answers that defame a client, or—just as dangerously—reproduces historical biases that lead to discriminatory hiring or lending, the law does not sanction the algorithm. It sanctions the Board of Directors.

The Fundamental Failure: Breach of the “Duty of Explainability”

Your privacy notices are only as good as your ability to justify them. We see another critical visual here: Privacy notices fail if the Fiduciary cannot explain the AI algorithm’s data processing.

The “Duty of Explainability” is a core principle. If you cannot explain to a data principal (the individual whose data is processed) how the AI reached its conclusion—meaning you treat it as a “black box”—you have effectively failed to provide valid notice and have breached your transparency obligations. This is an immediate red flag for enforcement bodies.

The Strategic Shift: From “AI-First” to “Responsible AI”

How do organizations avoid being crushed by this liability anchor? You must enact a fundamental cultural and technological shift.

We endorse the framework presented on the right side of the visual: We must transition from an “AI-First” mentality to a “Responsible AI” framework.

A Roadmap to Resilience and Mitigation

Your organization’s survival in the AI age requires moving through a structured roadmap that prioritizes safety over speed.

1. The “Responsible Use” Lever: Halt the AI Rush Visualize this shift: You must firmly pull the lever from the impulsive “AI-FIRST” position down to the deliberate “RESPONSIBLE USE” position.

Core Instruction: Use AI only when required and maintain a written AI use justification document. Just as with data minimization principles, “AI use minimization” should become a strategic pillar. Don’t use AI just because you can. Only use it when the business justification outweighs the significant liability and privacy risks.

2. Implement Continuous Human Oversight (The “Hand-on-the-Lever” Principle) We must resist the urge to believe the AI is autonomous. Human oversight is not a single point in time; it is continuous.

Core Instruction: Human handlers must validate input assumptions and audit final AI-generated responses. This “human-in-the-loop” approach is non-negotiable. Humans must remain the masters, auditing inputs and verifying outputs.

The Practical Defense: A Structured Security Roadmap via CERT-In

A “Responsible AI” framework must be underpinned by a mature cyber security posture. Organizations cannot secure AI without securing the infrastructure it sits on.

To give organizations a clear, actionable path, the infographic integrates a vital framework: The CERT-In 60-Day Roadmap for Defending Digital Infrastructure. This roadmap should be adopted immediately as your baseline security validation for any AI system deployment.

Phase I: Immediate Risk Reduction (0-7 Days)

The focus must be on foundational control:

• Identity Security: Secure the credentials of users accessing and managing the AI.

• Monitoring Readiness: Ensure logging is enabled so you can audit how the AI is being used.

• Foundational Governance: Define who owns the liability of the system within the organization.

Phase II: Operational Strengthening (8-30 Days)

This moves into governance and risk visibility:

• AI Security Governance: Establish explicit policies for AI use and risk tolerance.

• Continuous Exposure Management: Regularly test the AI for vulnerabilities (like prompt injection attacks).

Phase III: Advanced Resilience (31-60 Days)

This is about continuous validation:

• Adversarial Validation: ACTUALLY attack your AI to find how it breaks (e.g., trying to force it to leak data or hallucinate harmful content).

• Automation-assisted Defense: Deploy advanced tools to help monitor the AI’s behavior in real-time.

In conclusion we can say that the weight of AI liability under Indian law is absolute. If you deploy AI, you cannot avoid the chains of accountability shown in our infographic. The only question is whether you let that weight crush you or build the resilient framework—prioritizing explainability, human oversight, and the structured CERT-In roadmap—that can turn AI into a manageable, albeit weighty, competitive advantage.

Listen more to this at the Delhi IDPS event on 1st September 2026 . Venue Constitutional Club of India.

enquiry@consentera.com

Naavi

FDPPI conducted a one day event in association with Madras Management Association on “Beyond the Frontiers of DPDPA”. Mr B Shankar, Executive Director of Karur Vysya bank, inaugurated the event.

Naavi

IDPS or Indian Data Protection Summit is an annual flagship event of FDPPI. IDPS 2026  this year’s version will be conducted as a two location event in Delhi and Bengaluru.

The Delhi leg of IDPS 2026 will be launched on 1st September 2026 and the Bengaluru leg will take place on November 21, 2026.

Contact: enquiry@consentera.in