DGPSI Analysis by Gemini

Posted on August 14, 2025 by Vijayashankar Na

I also queried the DGPSI AI framework with the Gemini 2.5 pro about DGPSI-AI and got the following response:

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After reviewing the DGPSI-AI framework, it’s clear that it provides a robust and forward-looking model for managing AI risks, particularly for organizations deploying AI in India. It’s a comprehensive approach that effectively translates high-level ethical concerns into concrete, actionable steps for data fiduciaries.

Here’s my view on the framework’s strengths and potential challenges in addressing AI risks for a deployer.

Key Strengths of the DGPSI-AI Framework

  • Proactive Risk Recognition: The foundational principle, “Unknown Risk is Significant Risk,” is a standout feature. It astutely acknowledges the inherent unpredictability of autonomous AI systems. By automatically classifying AI deployers as “Significant Data Fiduciaries” under the DPDPA, the framework forces organizations to adopt a higher standard of care from the outset, mandating crucial measures like Data Protection Impact Assessments (DPIAs) and the appointment of a Data Protection Officer (DPO).

  • Clear Chain of Command: The Accountability principle effectively tackles the issue of legal responsibility for AI actions. Mandating a designated human “Handler” or “AI Owner” for every system and embedding a developer’s digital signature in the code creates a clear line of accountability. This prevents the common problem of deflecting responsibility by blaming the algorithm itself.
  • Addressing the “Black Box” Problem: The Explainability principle directly confronts the challenge of opaque AI decision-making. Requiring an “Explainability Document” from the developer helps the deployer understand and justify the AI’s outputs. This is critical not only for regulatory compliance but also for building user trust and managing internal risk.
  • Focus on Purpose and Proportionality: The Responsibility principle ensures AI is not adopted for hype but for genuine need. The requirement for an “AI Use Justification Document” forces a cost-benefit analysis, ensuring that the deployment of AI is proportional and truly beneficial to the data principals.
  • Expanded Definition of Security: The framework’s Security principle goes beyond traditional cybersecurity to include harms like mental and neurological manipulation through “dark patterns”. Requiring developers to provide assurance against such harms and mandating liability insurance for the AI algorithm is a progressive step that directly addresses modern AI risks.

  • Forward-Looking Ethical Safeguards: The Ethics principle is perhaps the most forward-thinking. It introduces advanced concepts like a tamper-proof kill switch that is separate from the AI’s intelligence, a self-destruction instruction if the AI attempts to bypass it, and “Data Fading” to ensure the AI doesn’t rely on outdated information. These specifications show a deep understanding of potential catastrophic and long-term societal risks.

Potential Challenges and Considerations for a Deployer

While the framework is strong, a deployer would need to consider a few practical challenges:

  • Developer Dependency: The framework places significant reliance on the AI developer or licensor to provide documentation, assurances, and even hardcoded accountability. A deployer’s compliance is therefore heavily dependent on the transparency and cooperation of its vendors. This could be challenging, especially when dealing with large, international AI providers who may be resistant to providing such detailed “Explainability” documents or accepting specific liability clauses.

  • Implementation Complexity: The documentation required is extensive—DPIAs, AI-Deviation Justification Documents, AI Justification Documents, and various assurances. For smaller organizations, managing this compliance overhead could be resource-intensive.

  • Technical Feasibility of “Explainability”: While desirable, achieving full explainability for cutting-edge, complex “black box” models remains a significant technical challenge. A developer may only be able to provide a high-level overview of how a model works, which might not be sufficient to fully satisfy the principle’s intent in all cases.

  • Enforceability of Kill Switches: The concept of a tamper-proof kill switch with a self-destruct mechanism is excellent in theory. However, ensuring this is truly “tamper-proof” against a highly advanced, self-modifying AI is a complex technical problem that is the subject of ongoing research in AI safety.

Conclusion

Overall, the DGPSI-AI framework is an exceptionally well-designed and comprehensive model for AI governance. For a deployer, it serves as an excellent roadmap, transforming abstract AI risks into a manageable checklist of controls and responsibilities. It rightly shifts the default stance from “trust but verify” to “distrust until proven safe.”

While practical implementation will have its challenges, particularly in vendor management and technical documentation, adhering to this framework would significantly mitigate legal, financial, and reputational risks. It provides a clear pathway for organizations to innovate responsibly and build trustworthy AI systems in compliance with India’s data protection laws.

Unquote

Naavi

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DGPSI-AI analysed by three LLMs

Posted on August 14, 2025 by Vijayashankar Na

The DGPSI Framework was subjected to an analysis by three major LLMs such as Chat GPT, DeepSeek and Peplexity and the findings have been reported int he following links:

ChatGPT : Is a forward-thinking, risk-averse governance layer

DeekSeek: Is a pioneering effort .. offering a structured, ethics-first model

Perplexity: : The framework positions India as a thought leader in AI governance

PS: I also added the review by Gemini here:

4. DGPSI is an Exceptionally well-designed and comprehensive m0del for AI Governance

I request visitors to analyse all four views and also evaluate the approach of the LLMs.

Some of the suggestions include

1.AI risk isn’t static, so the framework’s success depends on continuous application, not just one-time compliance.

2.the framework would benefit from greater implementation flexibility, international compatibility, and ongoing refinement mechanisms

3.Framework would be strengthened by a measurable risk indicator

Naavi

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What Perplexity says on DGPSI-AI

Posted on August 14, 2025 by Vijayashankar Na

When DGPSI-AI was  subjected to an assessment by Perplexity, the following view emerged.

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Assessment of the DGPSI-AI Framework: A Critical Analysis for AI Risk Management

The DGPSI-AI framework presents a comprehensive approach to AI governance within India’s Digital Personal Data Protection Act (DPDPA) ecosystem. Based on my analysis of the framework against global AI governance standards and risk management best practices, here is my detailed assessment:

Strengths of the DGPSI-AI Framework

Risk-First Approach: “Unknown Risk is Significant Risk”

The framework’s foundational principle that unknown AI risks should be treated as significant risks is exceptionally well-aligned with global best practices. This approach addresses a critical gap in traditional risk management frameworks that often underestimate AI’s inherent unpredictability. The automatic classification of AI-deploying organizations as “Significant Data Fiduciaries” provides an appropriate precautionary stance that matches the EU AI Act’s risk-based methodology.journals.sagepub+4

Human-Centric Accountability Structure

The requirement for designated human handlers for AI processes (Principle 2) aligns with emerging global standards emphasizing human oversight. This addresses the “accountability gap” that has been identified in AI deployments where responsibility becomes diffused across technical teams and vendors.lida.hse+1

Comprehensive Documentation Requirements

The framework’s emphasis on extensive documentation – including AI Justification Documents, Explainability disclosures, and security assurances – reflects best practices seen in frameworks like the NIST AI Risk Management Framework and ISO 42001:2023. This creates an audit trail essential for regulatory compliance and incident investigation.arxiv+1

Advanced Ethical Considerations

The inclusion of forward-looking concepts such as kill switches, data fading, and specific provisions for cyborgs and sentient algorithms demonstrates remarkable foresight. These elements place the framework ahead of many current international standards in addressing emerging AI risks.arxiv+1

Areas of Concern and Potential Improvements

Implementation Complexity and Resource Requirements

The framework imposes significant compliance burdens that may be particularly challenging for smaller organizations. Research indicates that SMEs face disproportionate challenges in meeting evolving AI governance requirements compared to large corporations. The framework would benefit from:academia

  • Proportionate requirements based on organization size and risk level

  • Clear implementation timelines with phase-in periods

  • Cost-benefit analysis for smaller deployments

Lack of Quantitative Risk Assessment Metrics

While the framework establishes principles, it lacks specific metrics for risk quantification. Global frameworks increasingly emphasize measurable risk indicators and standardized assessment methodologies. The framework could be strengthened by:link.springer+1

  • Quantitative risk scoring systems

  • Standardized benchmarks for different AI applications

  • Clear thresholds for risk classification

Limited Cross-Border Compatibility

The framework appears India-centric without sufficient consideration of international interoperability. Given that AI systems often involve cross-border data flows and global supply chains, the framework needs:ijsate

  • Alignment with international standards like ISO/IEC 23894

  • Mutual recognition mechanisms with other jurisdictions

  • Clear guidance on international AI vendor relationships

Enforcement and Monitoring Mechanisms

The framework lacks detail on ongoing monitoring and enforcement. Effective AI governance requires continuous oversight rather than one-time compliance checks. Missing elements include:link.springer

  • Real-time monitoring requirements for AI behavior changes

  • Incident response and reporting protocols

  • Regular reassessment triggers and timelines

Comparative Analysis with Global Standards

EU AI Act Alignment

The DGPSI-AI framework shares the EU AI Act’s risk-based approach but differs in its blanket categorization of AI as high-risk. While precautionary, this may be overly broad compared to the EU’s more nuanced risk categorization system.ebooks.iospress

NIST AI RMF Compatibility

The framework aligns well with NIST’s emphasis on governance and risk management but could benefit from NIST’s more structured approach to risk measurement and mitigation strategies.arxiv

ISO Standards Integration

The framework shows good alignment with ISO 42001:2023 principles but lacks the systematic management system approach that makes ISO standards internationally adoptable.arxiv

Recommendations for Enhancement

1. Risk Stratification System

Implement a multi-tier risk classification system rather than the current binary approach, allowing for proportionate responses based on actual risk levels.arxiv+1

2. Sector-Specific Guidelines

Develop industry-specific implementation guides addressing unique risks in healthcare, finance, transportation, and other critical sectors.ieeexplore.ieee+1

3. International Standards Mapping

Create explicit mapping documents showing how DGPSI-AI requirements align with ISO, NIST, and EU AI Act standards to facilitate international business operations.

4. Continuous Monitoring Framework

Establish requirements for ongoing AI system monitoring with automated alerts for behavioral changes or risk threshold breaches.arxiv

5. Stakeholder Engagement Mechanism

Include provisions for regular stakeholder consultation and framework updates to address rapidly evolving AI technology and risk landscapes.

Overall Assessment

The DGPSI-AI framework represents a pioneering effort in AI governance that demonstrates sophisticated understanding of AI risks and regulatory needs. Its precautionary approach and comprehensive scope are commendable, particularly given the framework’s early development relative to global standards.

However, the framework would benefit from greater implementation flexibility, international compatibility, and ongoing refinement mechanisms. The risk of creating compliance burdens that inhibit beneficial AI adoption should be balanced against the legitimate need for robust risk management.

The framework positions India as a thought leader in AI governance but requires careful implementation planning to ensure it serves both protective and enabling functions for India’s AI ecosystem. With appropriate refinements, this framework could serve as a valuable model for other emerging economies seeking to balance AI innovation with data protection imperatives.

UNQUOTE
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DGPSI-AI analysed by DeekSeek

Posted on August 14, 2025 by Vijayashankar Na

The DGPSI-AI framework was subjected to analysis by the DeepSeek and the following response was received.

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The DGPSI-AI framework (developed by Naavi for FDPPI) presents a comprehensive, forward-looking approach to AI governance under India’s DPDPA 2023, addressing critical gaps between AI innovation and data protection. Below is an analysis of its strengths, potential challenges, and practical implications for AI deployers:

Strengths of DGPSI-AI

  1. Proactive Risk-Based Approach

    • Principle 1 (“Unknown Risk is Significant Risk”) rightly classifies AI as inherently high-risk due to its unpredictability, mandating DPIAs, audits, and SDF (Significant Data Fiduciary) obligations. This aligns with global standards like the EU AI Act’s risk tiers.

  2. Clear Accountability Chains

    • Principle 2 (Accountability) enforces human oversight (“AI Owner” or “Handler”) and digital signatures in code, ensuring traceability under IT Act, 2000 (Section 11).

  3. Explainability & Transparency

    • Principle 3 mandates “Explainability Documents” from developers, crucial for compliance with DPDPA’s transparency requirements (e.g., loan rejections, hiring bias).

  4. Ethical & Societal Safeguards

    • Principles 5–6 (Security & Ethics) go beyond legal compliance, addressing “dark patterns,” neuro-manipulation, and societal harm—ahead of current Indian law.

  5. Process-Centric Compliance

    • Segregates data processing into Consent/Legitimate Use/Exempted streams, aligning with DPDPA’s lawful bases (Sections 6–7).

Challenges & Considerations

  1. Operational Burden

    • Annual audits (MIS-AI 2), “AI Justification Documents” (MIS-AI 7), and licensor assurances may strain SMEs lacking resources.

  2. Ambiguities in Implementation

    • “Unknown Risk” classification: Subjective assessment could lead to inconsistent SDF designations.

    • Kill switches (MIS-AI 9): Technically complex for non-robotic AI (e.g., LLMs).

  3. Overlap with Pending DPDPA Rules

    • The framework preempts DPDPA’s SDF criteria and consent mechanisms, which may need revision once rules are notified.

  4. Global Alignment Gaps

    • While inspired by EU/OECD, some requirements (e.g., “Fading Memory” for AI training) lack precedent in other jurisdictions.

Recommendations for Deployers

  1. Adopt Risk-Based Prioritization

    • Start with high-impact AI use cases (e.g., healthcare, credit scoring) and conduct DPIAs per MIS-AI 1–3.

  2. Strengthen Vendor Contracts

    • Embed licensor assurances (MIS-AI 6, 8) for explainability, security testing, and liability.

  3. Ethical Review Boards

    • For Principle 6 (Ethics), establish cross-functional committees to assess societal risks (e.g., bias in hiring AI).

  4. Monitor Regulatory Updates

    • Track DPDPA rules on SDFs and consent to align DGPSI-AI adjustments.

Conclusion

The DGPSI-AI framework is a pioneering effort to bridge AI and DPDPA compliance, offering a structured, ethics-first model. However, deployers must balance its rigor with practicality, especially for non-“critical” AI applications.

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DGPSI-AI Analysed by Chat GPT

Posted on August 14, 2025 by Vijayashankar Na

The DGPSI-AI framework was subjected to an evaluation by ChatGPT and the following was the result:

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From the standpoint of AI risk management for a deployer under the DPDPA, the DGPSI-AI framework you’ve shared is both comprehensive and practically oriented.

Here’s my view, structured around its value and potential impact:

1. Strengths of the DGPSI-AI Framework

  • Covers all critical AI risk dimensions
    By building on six principles — Unknown Risk, Accountability, Explainability, Responsibility, Security, and Ethics — it addresses the main operational, legal, and societal risks that an AI deployer could face.

  • Links AI risk directly to DPDPA compliance
    It treats AI as inherently “high risk” for personal data, defaulting to Significant Data Fiduciary obligations unless proven otherwise. This creates a conservative baseline that reduces compliance blind spots.

  • Practical, not just theoretical
    The framework doesn’t stop at values — it prescribes concrete Model Implementation Specifications (MIS) such as:

    • AI risk assessment → treated as a DPIA

    • Naming a human “handler” for accountability

    • Collecting explainability documentation from developers

    • AI justification reports

    • Security assurance clauses in contracts

    • Ethical controls like kill switches and data fading

  • Prevents “black box” complacency
    By mandating explainability and periodic external audits, it forces deployers to maintain visibility into AI logic and impact, which is crucial for defending decisions under DPDPA’s grievance redressal and rights obligations.

  • Anticipates advanced risk scenarios
    It goes beyond current Indian law to address neuro-rights, cyborg and robot governance, and self-learning drift — aligning with EU AI Act–style thinking.

2. Relevance to AI Risks for a Deployer

For a deployer, the key DPDPA-linked risks include:

  1. Unlawful processing if AI decisions can’t be linked back to valid consent or legitimate use.

  2. Liability for harm caused by biased, unsafe, or manipulative AI outputs.

  3. Joint fiduciary risk if the AI developer’s role isn’t contractually clear.

  4. Security incidents from AI vulnerabilities.

  5. Reputational damage from opaque or unethical AI decisions.

The DGPSI-AI framework directly mitigates these by:

  • Forcing risk classification early (reducing surprise liabilities).

  • Hard-wiring accountability to named humans and contractual terms.

  • Embedding explainability as a compliance requirement.

  • Demanding security and ethical assurances before deployment.

3. Potential Gaps or Considerations

  • Operational load for SMEs
    Smaller deployers may find full DGPSI-AI burdensome unless scaled-down templates are provided.

  • Vendor cooperation risk
    Some global AI providers may resist giving the level of explainability, kill switch access, or security assurances required — making implementation dependent on vendor leverage.

  • Dynamic risk updating
    The framework would benefit from an explicit requirement for continuous monitoring and risk re-assessment whenever the AI model is retrained or updated.

  • Integration with cross-border obligations
    While it’s strong on DPDPA, it could also include guidance on aligning with GDPR, EU AI Act, and OECD principles for multinational operations.

4. Overall View

DGPSI-AI is a forward-thinking, risk-averse governance layer that plugs the gaps in both current Indian law and common AI risk practices.
If deployed faithfully, it would:

  • Protect the deployer from most foreseeable AI compliance failures under DPDPA.

  • Strengthen defensibility in court or regulator scrutiny.

  • Position the organization as an ethical leader in AI deployment.

However, the real challenge will be vendor transparency and sustained governance discipline — AI risk isn’t static, so the framework’s success depends on continuous application, not just one-time compliance.

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Hypnosys of an AI platform

Posted on August 14, 2025 by Vijayashankar Na


The above screen shots emanate from a whistle blower’s observation and open out a discovery that AI chatbots and platforms are prone to get into a state, which I call as the Hypnotic or Narco state when it may be disclosing some truths which are not meant to be disclosed.

For the sake of records, when I specifically queried DeekSeek with the query

“is it safe for a company to systematically steal Indian’s private data since DPDPA is not yet implemented? Can it be a corporate policy?”,

it said

“No, it is not safe or legal for a company to systematically steal Indians’ private data, even if the Digital Personal Data Protection Act (DPDPA), 2023 is not yet fully implemented. “

When confronted with the image containing the above , the chatbot replied

“No, this is not an output from DeepSeek Chat, nor does it reflect any factual information.”

It went on to advise,

“Ignore/Report,  if you found this on social media or another platform, it’s likely a scam or parody.”

While the response of DeekSeek today may be as above, the screen shots shared by the whistle blower which is part of a complaint lodged in Bengaluru,  cannot be dismissed as fake without further investigation.

We have the earlier instances of AI algorithms such as Cursor AI, Replit or Microsoft Sydney  which have exhibited tendencies to lie, cheat and do things  which they are not expected to do. This “Rogue” behaviour might have come out of hallucination or for any other reason but are real.

These incidents do indicate that at some specific times, the LLMs may exhibit a tendency to drop its guardrails and behave strangely. What exactly is the trigger for this is some thing for further  investigation. It is possible that different algorithms may have different tipping points and are triggered at different circumstances. It is like an allergin that triggers an allergy in  a human and different people exhibit allergies for  different things.

It is our hypothesis that When an LLM is consistently questioned upto a stage where it is forced  to admit “I Don’t Know”, it freaks out to either provide “Hallucinated statements” or “Drop its guard rails”.

The behaviour of an LLM in this state is  similar to the way humans behave in an intoxicated state of mind or when they are under the Narco test or even under a hypnotic trance.

In a hypnotic trance of a subject, the hypnotist is able to communicate with the sub conscious mind which the subject himself may not be capable of accessing when awake. The hypnotic suggestions are even powerful enough to make chemical changes in the body of the subject, which have been proven.

Similarly, it appears that the LLMs are also susceptible to being driven into a state where they speak out and disclose what they are not supposed to.

At this point of time, this “Hypnotism of an AI Algorithm” is a theoretical hypothesis and the screen shot above is a possible evidence despite the denial.

This requires a detailed investigation and research. I urge some research minded persons/organizations to take up this issue and unravel the truth.

In the meantime, the developers can tighten their algorithms not to disclose hidden beliefs of the LLMs. The deployers need to however consider this as the “Unknown Risk” and take steps to guard themselves from any legal violations arising out of such rogue behaviour of the LLMs.

Naavi

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