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Ecommerce

Key obligations in brief

Ecommerce

TMT companies must obtain clear and informed consent from users before collecting and processing their personal data. This includes explaining the purpose of data collection and offering users the option to opt in or opt out.

Transparency in data processing is essential. Companies should provide clear privacy policies, inform users about data practices, and be open about how data is used.

If data is transferred across borders, companies must ensure that the data remains protected in compliance with relevant data protection laws.

TMT companies must promptly notify authorities and affected individuals in case of a data breach, as required by data protection laws.

Demonstrating accountability means maintaining records of data processing activities and having a clear understanding of how data protection regulations are applied within the organization.

Conducting Data Protection Impact Assessments (DPIAs) is often required for high-risk data processing activities to evaluate and mitigate privacy risks.

Users have the right to access their data, correct inaccuracies, and, in some cases, request its deletion. TMT companies must provide mechanisms for users to exercise these rights.

Business problem

  • Data Security

    With the increasing amount of sensitive customer data collected and stored, TMT companies must invest heavily in robust data security measures to protect against breaches and cyberattacks. The consequences of data breaches can be severe, including financial penalties and damage to their reputation.

  • Data Monetization

    TMT companies often want to monetize user data by using it for targeted advertising and personalized services. Balancing data monetization with user privacy concerns is a challenge. Striking the right balance between data collection and respecting user privacy is a delicate task.

  • Cross-Border Data Transfer

    Many TMT companies operate globally, making it essential to deal with cross-border data transfer regulations and data sovereignty laws. These regulations can restrict the flow of data between countries, complicating data management.

  • User Consent

    Obtaining clear and informed consent from users for data collection and processing is a challenge. Users must understand what data is being collected, for what purpose, and how it will be used. Ensuring transparency in data practices is vital.

  • Data Anonymization

    Even when companies aim to protect user privacy, the risk of re-identification of anonymized data remains. Advances in data analytics and machine learning make it increasingly challenging to truly anonymize data while still deriving meaningful insights.

  • Data Governance

    Establishing effective data governance frameworks to manage and protect data throughout its lifecycle is a significant challenge. This includes data classification, access controls, and data retention policies.

  • Consumer Trust

    Maintaining consumer trust is essential in the TMT industry. Any perception of mishandling user data can lead to reputational damage and loss of customers. Building trust through transparent privacy practices is an ongoing challenge.

  • Third-Party Data Sharing

    TMT companies often collaborate with third-party vendors, which can introduce additional privacy risks. Managing the data practices of these partners and ensuring they comply with privacy regulations is a challenge.

Ecommerce

Solutions

Ecommerce

Ardent Privacy’s patented technology product “TurtleShield” is an ML and AI-powered enterprise software platform, that helps businesses discover, identify, inventory, map, minimize, and securely delete personal data.

  • Know your sensitive data first, to secure it

    This is achieved by a nimble and oil drilling-like approach to discovery: We created a global map of organizational data, which is subject to “data protection / privacy regulations” and critical to business.

  • Monitor Third Party data sharing

    Often there are silos within entities or business and IT teams, and it is challenging to secure a holistic view of the data flow outside the organization and the data flow into the organization, especially when the data is shared with the third parties, like vendors, business partners and many more. We can create a data map, based on the data sharing, to facilitate you to take remedial actions, on the same.

  • Minimize data, reduce business and legal risk

    This is data hygiene control and we are approaching it from a risk reduction and compliance perspective. We scan large data sets for excess data, using Machine Learning and find out excess data including personal data. This can eliminate operational inefficiencies and save cost by removing the redundant data and legal cost pertaining to regulatory compliance.

  • Right to Be Forgotten or Assured Deletion

    Give organizations the ability to comply with obligatory erasure of personal data by allowing them to erase the data on request and validate the deletion.

  • Enable and automate data subject rights

    Search capability in large datasets to fulfill data subject requests and enable it efficiently. The assumption that data only exists in databases and nowhere else is often not a reality, as customer data exists in many sources. Using ML & AI we crawl across data sources and find where PII exists.

Other Case Studies / Use Cases

Learn how Ardent is helping customers helping their privacy compliance and data centric security journey.

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Share with Care KYC Protection for Fintech

Data Protection is the focus point of fintech businesses, due to customer data breaches and government regulations relating to personal data protection, introduced across India and the globe. With the emergence of fintech, the data is available in digital format, which makes the data more vulnerable to data breaches. The fintech world is evolving and it has ushered in a lot of challenges on the front of data security and data privacy.

View Full use case
healthcare case study healthcare band

Data Protection / Security - Insurance Sector

Data privacy is a significant concern in the insurance sector due to the sensitive nature of personal and financial information handled by insurance companies. Insurance providers collect and store vast amounts of data from policyholders, including personal details, medical records, financial information, and claims history. Safeguarding this data is crucial to protect customer privacy and prevent unauthorized access or data breaches.

View Full use case

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