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The Ministry of Electronics and Information Technology released the Draft Digital Personal Data Protection Rules, 2025 on January 3, 2025, inviting public feedback to refine India’s data protection framework under the DPDPA, 2023.

These rules provide detailed guidance on implementing core provisions of the Act—defining how organisations must collect, process, and protect personal data while upholding individual rights.

📌 What's Covered in the Draft DPDP Rules?

Consent Architecture: Structure and requirements for valid, informed consent

Children’s Data: Special obligations around age verification and parental approval

Consent Managers: Eligibility, duties, and registration process

Data Protection Board: Powers, functions, and procedure for inquiries

Notices & Disclosures: Mandatory elements in privacy notices

Security & Safeguards: Technical and organisational measures for data safety

Grievance Redressal: Complaint resolution procedures and timelines

🎯 Why These Rules Matter

The Draft DPDP Rules are critical in operationalising India’s data privacy law. They offer much-needed clarity to organisations navigating compliance and help ensure responsible data handling that respects individual privacy.

Whether you’re a data fiduciary, data processor, or privacy professional, these rules set the direction for practical implementation of the DPDPA, 2023.

🔎 Want to Know More About the Draft DPDPA Rules?

Visit our detailed breakdown covering all major provisions, compliance insights, and what to expect next in India’s data protection journey.

Data about the individual needs protection in the present digital world. With this in view, India has enacted its Digital Personal Data Protection Act 2023 for regulating the process of personal data as well as for ensuring the private rights of a person. This article will shed light on how these new regulations implemented by the DPDPA would impact the organization and also furnish compliance checklists that may guide the business enterprises in being compliant with the mandates of the new statute.

Overview of the Digital Personal Data Protection Act (DPDPA) 2023 The DPDPA 2023 is an overarching harmonized framework for the protection of personal data in India. It defines what Data Fiduciaries do, who a Data Principal is, and the principles guiding dealings on personal data in any data processing exercise, which include transparency, purpose limitation, data minimization, and accountability.

Key Impact of DPDPA on Organizations

In doing business or selling to or targeting Indians, the provisions of the DPDPA should be complied with. Key implications include:

Increased Responsibility: Organizations must implement proper data protection and can demonstrate compliance under the Act.

Consent Management: Collection and processing of the data subjects’ personal data necessitate explicit consent of the Data Principals, except in specific exceptions.

Data Principals’ Rights: Organizations should provide and respect data subjects’ rights, including access, correction, erasure, and portability. Data Breach Notifications: Notice of a data breach shall be given to the Data Protection Board and affected Data Principals within a reasonable time frame.

Cross-Border Data Transfers: Personal data shall be permitted to flow across borders provided conditions in the form of orders issued by the central government guarantee data protection.

Comprehensive Checklist for Organizations’ Compliance

All these steps will prove helpful for any organization regarding the nuances of the DPDPA in ensuring compliance with it:

1. Determine Applicability and Liabilities

Determine Applicability: Check whether the data processing of your organization falls within the scope of DPDPA. The Act will apply to digital personal data processing in India, as well as to entities that offer goods or services to Indians, regardless of their location.

2. Create and Implement Data Protection Policies

Set policies and procedures. Develop robust data protection policies describing the organization approach in processing, security, and compliance.

Review periods: These are reviewed periodically and must reflect changes made in the external regulatory environment and to organisational practices.

3. Data Mapping and Inventory

Inventory of Data. Developing an inventory showing all personal data processed by any organization, where sources, location, purposes, and sharing should be indicated.

Data Flow Mapping: Mapping of data flows within the organization to identify risks and ensure it aligns to the DPDPA principles.

4. Mechanisms for Consent Management

Informed Consent: Put mechanisms in place on explicit and informed consent from Data Principals prior to collecting or processing personal data.

Withdrawing Consent: Develop simple means through which Data Principals can withdraw their consent at any time, and such requests are promptly responded to

5. Respect Data Principal Rights

Access and Correction: Develop means through which Data Principals may access their personal data and have them corrected as appropriate.

Data Portability and Erasure: Ensure provision for data portability requests and that mechanisms are available for erasure of personal data upon request, taking into account appropriate legal or official requirements.

6. Strengthen Data Protection Controls

Security Controls: Implement adequate technical and organizational measures to ensure personal data is protected against unauthorized or unlawful processing, access, disclosure, alteration, or destruction.

Regular Audits: Regular security audits to evaluate the data protection mechanisms in place and recommend improvements in those areas.

7. Data Breach Response and Escalation

Breach Response Plan: There should be a perfect breach response plan indicating what to do in case of a data breach.

Notification Procedure: Standard notification procedures to the Data Protection Board and to Data Principals affected, as requires by DPDPA.

8. Regulation of Cross Border Data Transfers

Transfer Mechanisms: Identify the legal mechanisms by which personal data is allowed to leave India. Ensure that such transfers are in compliance with conditions granted by the government.

Application of Safeguards: Add contractual clauses and or other safeguards, as necessary, to ensure that appropriate protection is provided to the transferred data.

9. DPO

DPO Appointment: The DPO shall oversee data protection strategy, compliance efforts, and be a point of contact for data protection authorities and Data Principals.

10. Regular Training and Awareness Programs

Employee Training: The employees shall receive periodical training regarding DPDPA, data protection principles, and responsibilities of the employee related to personal data.

Awareness Campaigns: Develop a culture of data protection in the organization through constant awareness.

Conclusion

Artificial Intelligence (AI) is transforming the landscape of today’s business world. From predictive analytics and automated workflows to personalized customer experiences, AI is helping companies unlock new efficiencies and create smarter systems. But as with any powerful technology, AI comes with risks — particularly around how it handles personal data.

This blog delves into the crossroads of innovation and compliance, providing a practical roadmap for Indian businesses looking to leverage AI responsibly.

🤖 AI’s Growing Influence on Indian Businesses

AI is no longer limited to high-tech labs. It’s now embedded in:

Chatbots for customer service,

Fraud detection in banking,

Diagnostic tools in healthcare,

Smart farming systems in agriculture,

And recommendation engines in e-commerce.

These AI tools need data — lots of it. And not just anonymized data. AI often depends on personal information to function effectively. This is where the privacy challenge begins.

📜 DPDPA 2023: What Does It Say?

India’s DPDPA, 2023, is designed to protect the digital personal data of individuals (called data principals) and places responsibilities on entities processing that data (data fiduciaries). Key principles include:

Consent-first processing

Purpose limitation

Data minimization

Storage limitation

Right to access, correct, and erase personal data

However, the Act currently lacks AI-specific clauses. It doesn’t explicitly address automated decision-making, algorithmic bias, or AI explainability.

So where does that leave AI innovators?

🚨 The AI Privacy Problem: Risks to Watch Out For

1. Opaque Decision-Making

Many AI systems operate as “black boxes.” Even developers might struggle to explain why a model rejected a loan application or flagged a person for additional scrutiny. This violates the DPDPA’s spirit of informed consent and user rights.

2. Algorithmic Bias

If your AI model is trained on biased or incomplete data, it may reinforce discrimination — say, preferring one gender or community over another. This could amount to unfair processing under the DPDPA.

3. Excessive Data Collection

AI thrives on large datasets, but the DPDPA enforces data minimization — only collect what’s necessary. Over-collection can lead to legal trouble.

4. Lack of User Awareness

Often, users have no idea how their data is being used to train AI models. Without clear communication, companies risk violating the DPDPA’s transparency requirements.

✅ How Indian Companies Can Innovate Without Violating DPDPA

Here’s how to strike the right balance between AI-driven innovation and privacy compliance:

1. Design AI Systems with Privacy by Design

Before launching any AI initiative, ask:

Is personal data truly required?

Can anonymized or synthetic data be used instead?

Have privacy controls been built into the model?

Adopting a Privacy by Design approach involves integrating privacy safeguards into every stage of the process, from data collection to final deployment.

2. Ensure Algorithmic Transparency

While full explainability may not always be possible, companies must strive for transparency. Use tools and models that allow some level of interpretability.

Also, maintain clear documentation on:

How the model was trained

What data was used

Any known limitations or biases

3. Establish Data Minimization Practices

Instead of collecting data “just in case,” define the specific purpose for which personal data is needed.

Use data tagging and classification tools powered by AI itself to:

Flag sensitive personal data

Limit unnecessary storage

Automate data deletion after use

4. Set Up Ethical AI Governance

Form an internal AI Ethics Committee or appoint an AI Data Protection Officer to:

Evaluate privacy risks

Review use cases

Oversee compliance

This helps assign accountability, something that the DPDPA strongly implies but doesn’t always explicitly define in AI contexts.

5. Audit AI Models Regularly

Run bias and fairness audits on your AI systems.

Schedule periodic assessments to:

Detect new risks as models evolve

Ensure continued compliance

Maintain documentation for regulatory review

6. Be Transparent With Users

Inform users:

When AI is being used

How their data is being processed

What decisions it’s influencing

Offer opt-out options where possible. Also, create user-friendly privacy dashboards so users can access or delete their data.

🔍 Real-World Example: AI in Indian Healthcare

An Indian hospital uses AI to analyze patient scans and predict disease risks. To comply with DPDPA:

It uses consent forms that mention AI involvement.

It anonymizes data where possible.

Doctors review all AI-based recommendations before acting on them.

This hybrid approach — where AI supports, but doesn’t replace, human decision-making — can be a compliance-friendly model for other sectors too.

⚖️ Legal Grey Zones: Where DPDPA Falls Short

While DPDPA lays a strong foundation, it doesn’t:

Mandate explainability in automated decisions

Set boundaries for AI profiling

Require algorithm audits

Protect against AI-induced discrimination

This creates uncertainty, especially in high-stakes sectors like finance, hiring, and policing.

🔮 The Road Ahead: Policy, Innovation & Responsibility

India has the talent, infrastructure, and market to lead in ethical AI development. But innovation must go hand in hand with responsibility.

Until India introduces AI-specific legislation (like the EU’s AI Act), companies should voluntarily adopt global best practices:

OECD AI Principles

IEEE Ethically Aligned Design

EU AI Act risk-classification models

Doing so will not only keep them ahead of regulatory changes but also build public trust, which is invaluable in a privacy-conscious market.

🧩 Conclusion: Innovation Without Violation

AI doesn’t have to be the enemy of privacy. With the right mindset and safeguards, Indian companies can create transformative AI solutions and honor the principles of the DPDPA.

The key lies in:

Responsible data handling

Ethical model design

User empowerment

Transparent communication

“Third-party vendors can drive business growth — but they can also be the weakest link in your security chain.”

In today’s interconnected business environment, third-party relationships are essential for delivering exceptional customer service, expanding global operations, and improving internal efficiency. From CRM platforms to logistics providers, outsourcing is no longer a luxury — it’s a necessity.

However, when sensitive data changes hands or flows through a supply chain, it introduces a web of potential risks. A single vulnerability in one of your vendors can disrupt operations, cause regulatory fines, or even destroy customer trust.

So, how do you strike the right balance between leveraging third-party services and securing your business?

Let’s explore third-party data sharing, the associated supply chain risks, and proven strategies to mitigate them effectively.

🧾 What Is Third-Party Data Sharing?

✅ Common examples include:

CRM tools like HubSpot or Salesforce

Marketing automation platforms

Cloud-based data storage providers

Recruiting firms and outsourcing agencies

Logistics and supply chain vendors

While this data exchange streamlines operations, it also exposes businesses to risks — especially when those third parties are not held to the same data security and compliance standards.

⚠️ Major Risks in Third-Party Data Sharing

1. 💡 Competency Risk

Not all vendors are created equal. If a recruitment agency, for example, does not align with your hiring standards, it might deliver misaligned candidate profiles. While technically considered “data,” this information could be inaccurate or unusable — resulting in wasted time, poor decisions, and downstream inefficiencies.

2. 🔐 Data Breaches

The most notorious third-party risk. If a vendor is hacked, your data is compromised — even if your internal systems are secure.

Case in point: In 2021, Volkswagen Group of America suffered a massive breach due to a vendor storing unencrypted customer data online. This leak exposed loan numbers, emails, and even social security numbers of 97% of Audi customers.

Impact:

Reputational damage

Legal repercussions

Loss of customer trust

Regulatory penalties

3. 🛠️ Loss of Data Control

Vendors serving hundreds of clients manage vast amounts of data. This makes it harder to:

Control access rights

Monitor data usage

Ensure consistent privacy protocols

Without proper segmentation and visibility, even a minor misconfiguration could result in data leaks or unauthorized access.

🌐 Supply Chain Risks Amplified in a Globalized World

As companies increasingly outsource to international partners, third-party risks extend beyond just data.

🚨 Key Supply Chain Risks:

Cybersecurity vulnerabilities in third-party systems

Regulatory compliance gaps across jurisdictions

Operational disruptions from weather, politics, or pandemics

Data privacy violations under laws like GDPR or CCPA

Financial instability of suppliers causing fulfillment delays

🔎 How to Identify & Assess Third-Party Risks

Before you can mitigate risks, you must identify and evaluate them. Here’s a proven framework:

✅ 1. Vendor Due Diligence Checklist

Before onboarding any third party:

Review cybersecurity posture (firewalls, encryption, incident response)

Check data protection and privacy policies

Examine historical compliance violations

Assess financial health and credit score

Conduct on-site audits or third-party assessments

✅ 2. Compliance Verification

Ensure your vendors comply with:

International laws (e.g., GDPR, DORA)

Regional laws (e.g., CCPA, UAE PDPL, Saudi PDPL)

Industry standards (e.g., ISO 27001, HIPAA, SOC 2)

💡 Tip: Create a compliance scorecard to rank each vendor’s risk level.

✅ 3. Cybersecurity Risk Assessment

Ask these key questions:

Do vendors encrypt data at rest and in transit?

Do they regularly test for vulnerabilities?

How quickly can they detect and respond to cyber threats?

Are they backed by cyber insurance?

🔄 Regularly update assessments — especially after breaches or major system changes.

✅ 4. Operational Continuity Analysis

Determine:

How vendors handle natural disasters, strikes, or geopolitical events

Their reliance on single points of failure

Backup strategies and disaster recovery plans

🚚 Example: A supplier relying on one shipping partner may be more vulnerable than one with three fallback logistics providers.

🛡️ Strategies to Mitigate Third-Party Risks

Now that you’ve assessed the risks, here’s how to reduce them:

🔁 1. Establish Clear Data Sharing Agreements

Use Data Processing Agreements (DPAs) and SLAs

Define roles (controller vs. processor)

Limit data access to only what’s necessary

🔒 2. Implement Zero Trust Architecture

Adopt a “never trust, always verify” model across your tech stack. Control:

Who accesses your data

When and why they access it

From which devices and locations

🧪 3. Continuous Monitoring

Use tools that offer real-time monitoring of third-party activity. Track:

Data movement

Access logs

Anomalies or behavioral shifts

🔁 Integrate third-party risk management (TPRM) tools with your SIEM or GRC platform.

👨‍🏫 4. Educate & Train Internally

Train employees on recognizing phishing scams and unsafe third-party tools. Human error often opens the door to vendor-related attacks.

📉 5. Limit Vendor Overload

Avoid depending on too many vendors. Streamlining your vendor list reduces your risk surface area.

📌 Final Thoughts: Trust, but Verify

Third-party vendors play an essential role in modern business. From cloud services to recruitment partners, they help streamline operations and enhance customer experiences.

But don’t mistake convenience for security.

By establishing a robust third-party risk management program, conducting regular audits, and building strong compliance protocols, your organization can benefit from outsourcing — without sacrificing data integrity or customer trust.

Thank you for reading until the end. Before you go:

Please consider clapping and following the writer! 👏

Introduction

Vehicles have transformed from a simple mode of transportation into another interconnected device in the 21st century. Modern vehicles have been compared to smartphones or computers, which are always online, always tracking, and always sharing information and personal data. From GPS navigation to voice assistants and vehicle-to-infrastructure (V2I) communication, connected vehicles generate massive amounts of personal and behavioural data. The convenience is undeniable, but so are the concerns: who owns this data? Who can access it? And what rights does the driver of the connected vehicle actually have over it?

As we cruise into this increasingly connected era, the spotlight is firmly on data privacy, particularly in countries where regulatory frameworks are catching up. And with tech companies, automakers, and even governments in the mix, the question of data privacy isn’t just theoretical, it’s urgent.

What Data Are We Talking About?

When you hear “vehicle data,” it does not simply refer to maps and mileage. The depth and granularity of the data points collected is staggering. It includes:

Telematics data: GPS location, route history, vehicle speed, and braking habits.

Driver behaviour analytics: How fast you accelerate, how often you brake hard, or whether you tend to speed.

In-vehicle media and communications: Calls, messages, media preferences, and even voice recordings.

Biometric identifiers: Facial recognition for unlocking, fingerprint-based ignition, or fatigue monitoring.

Vehicle diagnostics: Engine performance, tyre pressure, fuel consumption, battery health (in EVs), and more.

Third-party integrations: If you sync your phone or use in-car apps, your contact lists, calendar, and app data may be harvested as well.

This amount of data collected is alarming on its own but the real concerns arise when this data is sent to cloud servers, shared with OEMs (Original Equipment Manufacturers), insurance companies, marketing platforms, or even law enforcement.

Why Should This Worry You?

The primary concern isn’t just the amount of data, but who controls it and how transparent that relationship is with the individuals whose personal data is being collected and shared. Unlike using an app or a website where there’s a clear “accept cookies” or “terms and conditions” banner, car owners and users often have no idea what’s being collected under the hood.

The French data protection authority CNIL, in its compliance guide on connected vehicles, puts it plainly: vehicles should be designed to process as much data as possible locally (within the vehicle) and only transmit it externally with informed user consent. But that’s not how most systems are set up today.

According to a study by the Future of Privacy Forum (FPF), drivers typically lack access to the full list of third parties receiving their data. Furthermore, it was also noted that most users don’t even know if their vehicle is connected or what that implies for their privacy.

The Global Regulatory Landscape: Fragmented and Evolving

The changes in the automobile industry vis-à-vis the collection of large amounts of personal data have occurred primarily over the last decade and thus no clear regulations exist that tackle this issue solely. Oftentimes, regulatory authorities use existing data privacy laws to deal with issues arising from the collection, storage and sharing of said data.

United States: A Case of Voluntary Ethics over Binding Law

The U.S. does not have a single comprehensive federal data privacy law, especially not one tailored to vehicles. What exists instead is a patchwork of guidelines, mandates and State Level Laws such as:

Industry-led guidelines, such as the Consumer Privacy Protection Principles from the Alliance for Automotive Innovation.

Federal Trade Commission (FTC) action under its general mandate to prevent unfair or deceptive trade practices.

State-level laws like the California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA).

These provide a great starting point. For instance, under CPRA, consumers have the right to know what personal data is collected, request deletion, and opt out of its sale. But enforcement is still largely reactive, and many automakers only offer compliance based on the consumer’s state, not as a uniform policy.

European Union: GDPR

The General Data Protection Regulation (GDPR) remains the most comprehensive privacy law worldwide. It directly impacts connected vehicle ecosystems by:

Requiring explicit consent for processing personal data.

Enforcing data minimization and purpose limitation.

Granting individuals the right to access, correct, or erase their data.

Mandating Privacy by Design and by Default in Vehicle Architecture.

CNIL’s guidance supplements this by emphasizing localized data processing, limiting third-party access, and ensuring user control via in-car interfaces.

The EU also mandates that automakers disclose whether vehicle data is used for marketing, insurance profiling, or resale, giving users actionable rights in real time.

Introduction

The Digital Personal Data Protection Act (DPDPA), 2023 and the Draft DPDP Rules, 2025 have ushered in a new era of data privacy in India. This framework, with an emphasis on enforcing data privacy, has ignited a sense of regulatory urgency. While this signals a significant shift towards stronger privacy protections, it has also created a wave of uncertainty, particularly among startups and emerging technologies.

AI startups often house a wealth of sensitive data, making them prime targets for attackers seeking to exploit this information, creating an orchard for potential threats. The challenge here is understanding the needs of each startup and engaging in compliance practices that are not too resource intensive. For startups navigating lean resources and rapid scaling, compliance with the DPDP framework is a daunting challenge. AI startups specifically must make compliance a priority since AI systems depend on enormous datasets, many of which contain personal data, to operate efficiently. AI in the past has proven to have the ability to scan and analyze data to uncover sensitive facts that people might not want to disclose, this brings with it major privacy issues. The absence of adequate guardrails might result in abuse or illegal access to confidential data.

From appointing Data Protection Officers to managing consent, under the DPDP Framework, startups are grappling with questions about operationalizing compliance in a manner that aligns with their business needs.

Informed Consent

Under Section 4 of the DPDPA, data fiduciaries are required to secure explicit consent from individuals before processing their personal data. This consent must be freely given, specific, informed, and unambiguous. The Draft DPDP Rules emphasize that consent notices must be clear, specific, and easy to understand independently. This means that startups must now establish systems to securely collect, manage, and document user consent, ensuring compliance with these requirements. This starts with providing standalone notices outlining the types of personal data collected, the purposes for data collection, and the uses to be enabled by such processing activity.

These notices must also detail how users can withdraw consent and exercise their rights under the DPDPA. The Rules provide for a consent manager framework to facilitate user consent management. Startups must implement systems that enable users to transparently give, review, and withdraw consent at any time. AI Startups can ensure compliance with consent requirements by developing a user-friendly consent mechanism, creating sufficient granular consent options and ensuring transparency in AI training and decision-making.

Data Security Measures

AI startups handle large-scale, structured, and unstructured data, making them prime targets for cyber threats. The DPDPA requires organizations to implement robust organizational and technical safeguards to protect personal data against unauthorized access, use, disclosure, alteration, or destruction.

Startups must prioritize the adoption of advanced security measures. This includes implementing encryption to secure sensitive information during storage and transmission, as well as deploying strong access controls to ensure that only authorized personnel can access personal data.

Some of the reasonable security measures under the Draft DPDP Rules include implementing measures like encryption, obfuscation, masking or the use of virtual tokens mapped to specific personal data.

Further regular security audits, vulnerability assessments, and penetration testing to identify and address potential risks form a part of the organizational measures that may be undertaken.

Ensuring that sufficient security measures are taken by AI startups to secure their AI model is crucial.

Apart from the security measures, it is also important for organizations to have a strong breach response plan. Since AI systems continuously learn and process data, breach response strategies must be tailored to dynamic AI models.

The draft rules also lay down certain timelines for intimation of breach that must be adhered to. In case of any breach, the AI startup as data fiduciaries must ensure that they take timely action and notify the Data Protection Board as well as the affected Data Principals. Data fiduciaries must provide further information, such as facts of the event, circumstances and reasons behind the breach, remedial actions and report on notifications given to affected Data Principals.

Cross-border Data Transfer

The global nature of tech and AI operations adds another layer of complexity to the establishment. Different countries enforce varying regulations concerning data protection, making it challenging for organizations to ensure compliance while operating across international jurisdictions. AI Startups with global operations, engaging in cross-border data transfers must ensure compliance with these regulations by adhering to the prescribed standards. Data Fiduciaries must ensure that they adhere to the requirements and standards prescribed by the Central Government under Section 16 for the transfer of data outside the territory of India. Additionally, AI Startups must also comply with sectoral regulations governing cross-border transfer of personal data.

Data Retention and Deletion

The Act requires organizations to retain personal data only for as long as necessary to fulfil the purposes for which it was collected. They must establish and implement clear policies for data retention that align with these guidelines. The draft DPDP Rules provide for specific data retention periods based on the purpose for which the data is being collected and processed. Once the data is no longer needed, they should ensure its secure deletion or anonymization to prevent unauthorized access or misuse. Data Principals must be informed 48 hours before their data is to be erased. This process can include automated systems for tracking data lifecycles, conducting regular audits to identify redundant data, and securely erasing it in compliance with industry best practices. By adopting these measures, startups can reduce data-related risks and demonstrate accountability in handling personal information.

What is Stalkerware?

Stalkerware is software that allows an individual to monitor another’s phone or computer without their awareness. While some vendors market the software as a parental monitoring tool, it is usually misused by insecure partners or malicious individuals. The ethical and legal consequences of stalkerware usage are dire, as it largely constitutes illegal surveillance.

A Violation of Trust and Security

Despite the sensitive data that these companies work on, a record number of stalkerware companies have experienced data breaches. In one TechCrunch report, up to 25 stalkerware companies have been hacked or leaked sensitive customer and victim details since 2017. The most recent example was of SpyX, which revealed almost 2 million victims’ personal information. This attack is one of a disturbing pattern, with other businesses like Spyzie, Cocospy, and mSpy also being hit with significant data breaches.

The effect of the breaches is profound. Personal messages, photos, call logs, and even GPS locations of unsuspecting victims have been leaked online, sometimes unbeknownst to them. This not only violates privacy but can also result in real harm, particularly in the case of domestic abuse.

The Motivation for the Hacks

The routine hacking of companies that make stalkerware raises questions about the ethics of the industry as well. The majority of hackers claim that their aim is to expose and dismantle what they see as a toxic and unethical business model. The stalkerware business has been termed a “soft target” by Electronic Frontier Foundation cybersecurity researcher Eva Galperin, meaning the parties who sell these apps tend to not be high on scruples when protecting their users’ data.

A History of Breaches

The timeline of stalkerware breaches is daunting. The first of the big breaches occurred in 2017, when hackers invaded Retina-X and FlexiSpy, showing their records for over 130,000 customers. From then on, the list of breached firms has grown, including Mobistealth, SpyFone, and a number of others. Not only do each of these breaches expose the vulnerabilities in these apps, but also show the ethical dilemmas of using them.

The Risks of Using Stalkerware

Not only is the use of stalkerware immoral, in a number of instances it is illegal as well. Unapproved monitoring has been outlawed in the majority of jurisdictions. Even when used in the pretext of monitoring children, the repercussions can be scary. Parents are asked to discuss privacy and security openly with children rather than relying on secret monitoring.

Besides, the security risks that are part of stalkerware software pose a significant danger. Users are left exposed to the same dangers they were attempting to escape. The data breaches that have been a bane to this industry are a stark reminder that the monitoring tools that are supposed to be used can easily come back to haunt them.

Conclusion

As awareness of the risks of stalkerware grows, individuals need to reconsider their behavior. The risks of using such programs are much greater than any potential benefit. Instead of intrusive monitoring, communication and trust have to be cherished in relationships.

For privacy-minded individuals, it would be advisable to employ legitimate parental control software designed with security in mind. The software operates discreetly and takes into account the privacy of all parties involved.

Introduction:

Recently, Uber was fined €290 million by the Dutch Data Protection Authority (AP) for violating the General Data Protection Regulation (GDPR), the primary law in the European Union (EU) designed to safeguard personal data. The case revolves around Uber’s failure to comply with GDPR regulations concerning the transfer of personal data from Europe to the United States. According to Aleid Wolfsen, chairman of the Dutch Data Protection Authority, “Uber did not meet the requirements of the GDPR to ensure the level of protection to the data with regard to transfers to the US. That is very serious.” Uber, however, considers the decision and the fine to be “flawed and unjustified” and has announced its plans to appeal.

The decision follows a Court of Justice of the European Union (CJEU) ruling in 2020 that found an agreement known as a ‘privacy shield’ which allowed companies to transfer data to the US was invalid as the US government has the ability to tap into the transferred personal data. In a globalized world where companies operate across borders, the appropriate handling of personal data is crucial. This case underscores the severe consequences of failing to manage data transfers properly.

Background of the Case:

Uber B.V., a company based in the Netherlands, is part of the global Uber network, with Uber Technologies Inc. (UTI) as its parent company in the United States. Uber drivers in the European Economic Area (EEA) use the Uber Driver App to provide ride services. To use this app, drivers must create an account, which involves sharing personal information like their name, location, and sometimes even sensitive details like criminal records or health data. This data is then stored on servers in the U.S., where UTI manages it. The cross-border transfer of this data, especially after the invalidation of the Privacy Shield by the CJEU) in 2020, became the focus of legal scrutiny.

Uber’s trouble began when a French human rights group, representing over 170 Uber drivers, filed a complaint with the French Data Protection Authority (CNIL). The complaint was later transferred to the Dutch DPA, as Uber’s main European office is in the Netherlands. The complaint raised concerns about how Uber was handling the personal data of drivers in the EEA, especially regarding its transfer to the U.S. without the necessary legal safeguards in place, as required by the GDPR.

Legal violations in the case:

At the core of this case is a violation of the GDPR, particularly its provisions on cross-border data transfers. The GDPR, which came into effect in 2018, is one of the world’s most stringent data protection regulations. It applies to any company processing the personal data of individuals within the EU, regardless of where the company is based. The regulation has specific rules governing the transfer of personal data to countries outside the EU, such as the United States, which are outlined in Chapter V of the GDPR.

This lays out the conditions under which personal data can be transferred to third countries or international organizations. These transfers are only allowed if certain protections are in place to ensure that the data will be treated with the same level of care and security as it would be within the EU. The most common methods to ensure this are through adequacy decisions by the European Commission, Standard Contractual Clauses (SCCs), or Binding Corporate Rules (BCRs).

Uber’s Legal Argument:

Uber’s defense, in this case, was primarily based on the interpretation of the application of the GDPR. Uber argued that since both Uber B.V. and UTI were subject to the GDPR, the rules for transferring data outside the EU should not apply. Uber claimed that Article 3 of the GDPR, which determines the regulation’s territorial scope, was sufficient to cover their operations, and therefore, the specific rules in Chapter V on cross-border data transfers should not apply. They also argued that the transfer was necessary for fulfilling contracts with the drivers, which they believed exempted them from the need for additional safeguards.

Uber pointed to Article 49(1)(b) and © of the GDPR, which allows exceptions for data transfers that are necessary for the performance of a contract or are in the interest of the data subject. Uber claimed that the transfer of personal data to the U.S. was essential for their global operations, as they needed a centralized IT system to manage their services effectively.

With the increasing frequency of cyberattacks and data breaches, companies are struggling like never before to protect their sensitive data. This has resulted in the widespread adoption of data protection services — cloud-based offerings that provide data security, compliance, and effortless disaster recovery. As more companies depend on digital infrastructure, the market for data protection services is poised for exponential growth, driven by cybersecurity risks, compliance regulations, and affordability.

Market Expansion and Outlook

The market for data protection services is experiencing unprecedented growth. Worth USD 26.04 billion in 2024, it is expected to grow to USD 74.91 billion by 2030, with a stupendous CAGR of 19.3%. This growth is mainly driven by the increasing incidence of cyberattacks, such as ransomware attacks, phishing attacks, and insider attacks.

Digital Transformation Driving Adoption

As companies embark on digital transformation, data volumes are growing exponentially. With companies creating and storing massive amounts of data, there is a pressing need for reliable backup, recovery, and storage. Data protection services provide an effective, scalable, and secure solution to traditional data protection approaches, making them the go-to option for companies of all sizes.

Key Drivers of Data Protection Services Adoption

Cybersecurity Threats

As cyberattacks evolve, companies can no longer depend on conventional security solutions. Data protection services combine AI and machine learning to detect threats in real-time and implement proactive mitigation techniques. By utilizing automated security measures, organizations can reduce risks and enhance their cybersecurity stance.

Regulatory Compliance

Governments and regulatory agencies across the globe are imposing more stringent data protection regulations, forcing companies to implement robust security measures. Rules require stringent compliance standards, and data protection services assist organizations in complying with regulations by offering secure, compliant, and auditable data storage facilities.

Cost Efficiency

For small and medium-sized businesses (SMEs), establishing an in-house cybersecurity framework can be budget-busting. Data protection services provide a budget-friendly option, eliminating the necessity of costly hardware and specialized IT personnel. With pay-as-you-go pricing structures, companies can grow their data protection measures based on their requirements without unnecessary initial investments.

Challenges in the Data Protection Services Market

Loss of Control Over Data

Among the top issues that companies have when they implement data protection services is their perceived loss of control over their sensitive information. Outsourcing critical information to third-party vendors exposes organizations to security and compliance threats, raising concerns about potential data mismanagement.

Vendor Lock-in

Most data protection service providers offer proprietary solutions, resulting in vendor lock-in — a scenario where companies cannot easily change vendors because of compatibility and contractual restrictions. This inflexibility can lead to higher long-term costs and reliance on one vendor.

Skills Gap in Cybersecurity

The cybersecurity sector is currently confronting a lack of talented experts, making it difficult to optimize and manage data protection services. Without proper expertise, organizations risk failing to use effective security measures, exposing their data to online threats.

Future Trends in Data Protection Services

Generative AI for Improved Data Security

The infusion of Generative AI in data protection services is set to transform data protection strategies. AI-powered security solutions are capable of analyzing patterns, anticipating potential cyberattacks, and providing proactive countermeasures. This will greatly enhance real-time threat detection and strengthen cybersecurity architectures.

Greater Emphasis on Disaster Recovery

As companies prioritize business continuity planning, there will be increased demand for automated disaster recovery software in data protection services. Sophisticated data protection platforms will provide instant backups, quick data recovery, and cloud failover capabilities, allowing for minimal disruption in the case of cyberattacks.

Regional Insights: Global Expansion

Though North America currently leads the data protection services market with its developed IT infrastructure and strict cybersecurity regulations, the Asia-Pacific region is growing fast. Governments in many countries are adopting data security laws, and businesses are investing in data protection solutions. The rise in cyberattacks in these countries further boosts the demand for robust data protection services.

Conclusion

With cyber threats ever-changing, organizations need to embrace proactive data security measures. Data protection services are becoming game-changers, providing an affordable, scalable, and compliance-focused solution. By combining AI-driven security controls, emphasizing disaster recovery, and overcoming vendor lock-in issues, data protection service providers can define the future of cybersecurity.

So, which one is best for your business? Let’s weigh the advantages and disadvantages of both methods to make a decision.

What Does a Data Protection Officer (DPO) Actually Do?

A Data Protection Officer is charged with the management of an organization’s data protection and privacy services. Some of their primary responsibilities are:

Ensuring adherence to regulations such as GDPR, CCPA, and Saudi PDPL

Carrying out data protection impact assessments

Creating and implementing data security policies

Reporting data breaches to regulatory agencies

Delivering training and counsel on data privacy service compliance

The Benefits of an In-House Data Protection Officer

Having an in-house DPO means bringing someone on board full-time to manage data protection compliance. Here’s why some companies prefer this approach:

1. Deeper Understanding of Company Operations

An in-house DPO has immediate access to company systems, and it is easier to detect and solve compliance issues before they turn into serious problems.

2. Quicker Response to Cybersecurity Threats

Since they belong to the company, in-house DPOs can respond instantly to data breaches or regulatory issues.

3. Improved Coordination with Internal Teams

Having a resident DPO facilitates easier interdepartmental collaboration, making it easier to integrate privacy best practices into day-to-day operations.

The Challenges of an In-House DPO

While having an in-house DPO has its perks, it’s not always the perfect solution. Here are some challenges:

1. Higher Costs

Salaries, benefits, training, and operational expenses can make hiring an in-house DPO an expensive commitment.

2. Limited External Exposure

Unlike outsourced experts who work with multiple clients, an internal DPO might not have the same exposure to the latest data security consulting trends and best practices.

3. Risk of Internal Bias

An in-house DPO may sometimes face pressure from management, which could create conflicts of interest when providing compliance advice.

Why Companies Are Choosing to Outsource Their DPO

More businesses are now outsourcing data protection consulting services to get expert guidance without the high costs of a full-time DPO. Here’s why:

1. Cost Savings

Outsourcing eliminates recruitment, salary, and training expenses, making it a budget-friendly option.

2. Access to Specialized Expertise

Third-party data privacy consultants work across industries and stay updated on the latest regulations, ensuring businesses receive top-tier compliance advice.

3. Flexibility and Scalability

Companies can scale their data privacy service needs up or down based on compliance requirements, making outsourcing a flexible and efficient choice.

4. Unbiased Compliance Oversight

An external DPO provides an objective assessment of a company’s data protection consulting practices, helping ensure transparency and regulatory adherence.

Which Option Works Best in 2025?

Deciding between an in-house and outsourced DPO depends on several factors, including company size, industry, compliance requirements, and budget.

Large Enterprises: Companies handling vast amounts of personal data may benefit from having an in-house DPO who can provide real-time oversight.

Small and Medium-Sized Businesses (SMBs): Businesses with limited resources might find data privacy consulting services more practical and cost-effective.

Highly Regulated Sectors: Industries like finance, healthcare, and technology may require a combination of both — leveraging internal expertise while also working with external data security consulting firms.

Final Thoughts

Both in-house and outsourced DPO solutions have their advantages in 2025. Businesses must carefully assess their specific needs, regulatory obligations, and financial constraints before making a decision.