Comment effectuer une évaluation de l’impact sur la confidentialité des données

Start by getting the terminology correct - it’s a data PROTECTION impact assessment. If you approach it with a privacy perspective, you’ve already failed

It is easy to understand do we need DPIA or not. We should check our process/system for factors which might indicate likely high risk: 1. Evaluation or scoring 2. Automated decision-making with legal or similar significant effect 3. Systematic monitoring, tracking or observing individuals’ location or behaviour 4. Sensitive data or data of a highly personal nature 5. Data processed on a large scale 6. Matching or combining datasets 7. Data concerning vulnerable data subjects (e.g., children, employees, patients) 8. Innovative use or applying new technological or organisational solutions (e.g., IoT) 9. Preventing data subjects from exercising a right or using a service or contract If we find 2 of them, we should conduct an extensive DPIA.

Businesses should implement a process whereby all new processing activities or changes to existing processing activities (such as changing vendors, types of data collected, repositories etc), requires a preliminiary DPIA (screening checklist) to be completed by the process owner and reviewed by the DPO or person responsible for data protection prior to implementing changes or new processing activities.

Many laws requiring DPIAs may provide lists of circumstances that will guide you on whether a DPIA is, in fact, triggered. That said, these lists tend to be open-ended so there may be circumstances that will trigger a DPIA that may not be covered by them. It's usually a good idea to keep your eyes wide open for circumstances that do not fit neatly into the predefined categories but clearly involve potentially high-risk processing of personal data.

- Identify Need for a DPIA, Determine if the project involves high-risk processing of personal data. - Document the nature, scope, context, and purposes of the data processing activities. - Identify potential risks to individuals' privacy and data protection rights. - Propose measures to mitigate identified risks, ensuring compliance with data protection principles. - Record the DPIA process, findings, and decisions in a comprehensive report. - Regularly review and update the DPIA to address any changes in the processing activities or new risks.

Data Processing Principles under GDPR The GDPR outlines several principles for lawful data processing: 1. Lawfulness, Fairness, and Transparency: Processing must be lawful, fair, and transparent to the data subject. 2. Purpose Limitation: Data must be collected for specified, explicit, and legitimate purposes and not processed in a manner incompatible with those purposes. 3. Data Minimization: Processing should be adequate, relevant, and limited to what is necessary. 4. Accuracy: Personal data must be accurate and kept up to date. 5. Storage Limitation: Data should be kept in a form which permits identification of data subjects for no longer than necessary. 6. Integrity and Confidentiality: Personal data must be processed in a manner.

1. Scope: Clearly outline the purpose, scope, and boundaries of the processing activity. 2. Data Mapping: Identify and document the types of personal data involved, including its source, storage, and processing. 3. Assess Risks: Evaluate the risks to individuals’ privacy rights, considering factors like data sensitivity, potential harm, and the likelihood of occurrence. 4. Legal Compliance: Ensure compliance with relevant data protection laws and regulations, such as GDPR 5. Stakeholders: Involve relevant stakeholders, including data subjects, to gather insights and address concerns 6. Mitigation : Propose and implement measures to mitigate identified risks, such as pseudonymization or encryption. 7. Review : Regularly review the DPIA

It is also important to identify other forms of processing involved as part of the whole. For example, a database transfer may involve data cleansing, segregating, anonymisation, deletion of unnecessary data etc. It is important to have this set out in as much detail as possible, and where possible, include a data map if this is relevant, at this stage. This should also factor in the risk analysis phase as these different forms of processing may present different risks to the overall project.

Clearly outline the nature, scope, context, and purposes of the data processing activity. This includes details about the types of data collected, who will process it, how it will be stored, and for how long. Actions: Document the personal data types (e.g., names, addresses, health data), data subjects involved (e.g., customers, employees), data flows (how data moves through your system), and the purposes (e.g., marketing, fraud prevention).

Being honest and *specific* about the benefits of data processing is the only way to truly weigh them against the risks. You'll accept any risks when you let yourself believe the benefits are limitless and life-changing.

Assessing Risks:1. Identify Privacy Risks,Data Breaches,Non-compliance,Impact on Individuals,Reputational Damage 2. Evaluate Likelihood and Severity:Likelihood,Severity,Risk Matrix 3. Consider Different Perspectives:Stakeholders,Scenarios Assessing Benefits:1. Identify Potential Benefits:Improved Services,Operational Efficiency,Compliance,Trust and Confidence 2. Quantify Benefits:Cost Savings,Customer Satisfaction,Competitive Advantage 3. Balance Risks and Benefits:Mitigation Measures,Informed Decisions Risk of non-compliance with data protection regulations leading to legal penalties and fines. Enhance the quality and efficiency of services provided to individuals.

A DPIA is not a business case - the benefits an organisation seeks to get from processing the data must be secondary to honest, accurate and evidenced identification of the risks to the interests of the data subject. The purpose of a DPIA is to identify those risks and mitigate them, or if absolutely necessary to justify them so that your DPA can determine if they feel your processing is still allowable. Leave the business benefits for the business case.

In the third step, you dive into a deep analysis of data processing, where you'll carefully balance the risks and rewards, not just for your organization but also for the people whose data you handle. Think about the possible downsides, like data breaches, discrimination, identity theft, or losing control over the data. At the same time, don't forget to shine a light on the upsides, such as boosted efficiency, happier customers, and room for innovation. To help with this assessment, consider using handy tools like a risk matrix – it's like your compass for measuring and comparing these factors, ensuring you've got robust data privacy practices in place.

1. Identify the Need for a DPIA:Screening Questions,Regulatory Requirements 2. Describe the Data Processing:Purpose,Data Types,Data Sources,Data Flow 3. Consult Stakeholders:Internal,External 4. Assess Necessity and Proportionality: Necessity,Proportionality 5. Identify Risks to Individuals:Risk Impact. 6. Identify and Implement Mitigation Measures:Technical Measures,Organizational Measures,Legal Measures 7. Document the DPIA Findings:Detailed Report,Transparency 8. Review and Approve the DPIA:Internal Review,External Review 9. Implement Mitigation Measures:Action Plan,Monitoring 10. Monitor and Review the DPIA: Continuous Monitoring, Periodic Review 1. Technical Measures: Encryption, Pseudonymization,Access Controls,Data Minimization

To ensure your measures are effective and balanced, evaluating their impact and proportionality is essential. This step is about finding the right equilibrium, where data protection doesn't hinder your goals but strengthens them. Be meticulous in documenting how these measures directly align with the risks and benefits pinpointed in the preceding stages of your data processing strategy.

In the fourth step, identify and implement measures to mitigate risks and enhance benefits in data processing. Adhere to data protection principles, incorporating practices such as data minimization, pseudonymization, encryption, and consent. Evaluate the effectiveness and proportionality of these measures and document how they address identified risks and benefits.

You have ot be technically accurate wrt the capabilities of controls such as encryption. Its often presented as a catch all by orgs who clearly don;t understand where it can and cannot be used, or where it must be applied to be an effective control. Often where the encryption must be applied/removed to achieve adequate risk mitigation will prevent use of the service you wish to use (particularly in the case of Public Cloud) In such a case you get limited beneit or risk reduction from encryption and will need to get more creative.

Based on the risk assessment, the next step is to identify measures to mitigate or eliminate the identified risks. This may involve implementing technical and organizational measures to enhance data security, such as encryption, access controls, and data minimization techniques. Additionally, it may involve implementing privacy-enhancing technologies or procedures to ensure compliance with legal requirements and protect individuals' privacy rights.

Stakeholder consultation in PIAs is not a checkbox exercise; it's an opportunity to build bridges of trust and pave the way for successful data-driven initiatives. By embracing diverse perspectives and fostering open dialogue, we can navigate the complex landscape of data privacy with confidence, knowing that trust, not technology, is the true engine of sustainable progress.

1. Identify Relevant Stakeholders: Internal Stakeholders: Identify internal stakeholders such as data protection officers (DPO), legal advisors, IT staff, HR, and any departments directly involved in data processing activities. External Stakeholders: Identify external stakeholders, which may include data subjects, third-party service providers, regulatory authorities, and privacy advocacy groups. 2. Plan the Consultation Process Objectives: Define the objectives of the consultation process, including understanding privacy concerns, gathering feedback on data processing activities, and identifying potential risks and mitigation measures. Methods: Choose appropriate methods for consultation, such meetings, workshops, surveys, or interviews.

In practice, engaging with various internal experts like the Data Protection Officer (DPO), legal counsel, and IT staff offers multifaceted insights. For instance, in a project involving customer data analysis, collaborating with legal advisors ensured compliance with regulations. Furthermore, seeking input from data subjects or their representatives, like customers or employees, proved invaluable. This collaborative approach not only enhances the DPIA process but also fosters trust among stakeholders, aligning their expectations with the data processing measures implemented.

Ask the same question in different ways. Remember that those of us who work in Data Privacy are trained to think in terms of wider context, whereas people in other job roles may be more accustomed to a black and white approach. If their answers still seem too straightforward and lacking in scope, have them walk you through a hypothetical scenario. An informal red team-blue team chat may prompt them to offer details they otherwise wouldn't have deemed relevant. Central to this is reassuring them that you're on their side. That you're not looking for problems to make their job difficult, but you're looking for problems so you can offer solutions and make their job easier.

Consult with internal and external stakeholders, including experts like DPOs and legal counsel, as well as data subjects or their representatives, to gather feedback on the data processing and implemented measures.

Part of doing your DPIA right is knowing it is a data PROTECTION impact assessment and not data privacy... Focussing only on privacy in your assessment means you will have too narrow a scope, because a DPIA asks you to look at risks to all FUNDAMENTAL RIGHTS AND FREEDOMS of the data subject. Not just the ‘right to privacy’ but also the right not to be discriminated against, have freedom of speech and thought etc. The fact that the AI model used to generate this is unable to take the exact wording from the GDPR and also missed the fact that entire discussions have been held (even on this very platform) about not using data privacy as a synonym for data protection means it’s still pretty bad…

This is critical given the ever changing legal regime and technological uses. Nothing stands still and DPIAs done once should be reviewed when there are changes and no less than annually. Don't be stagnant.

In my experience many organizations put together a DPIA at the outset of a data project but then don't regularly review their DPIA. - How can you create a cadence or cycle for reviews? - Do you check you DPIA when processes change? - What is stopping you from regularly reviewing your DPIA?

Data privacy isn't static, nor should your DPIA be. In a world of evolving technology and regulations, maintaining an agile, living DPIA is the cornerstone of accountability and trust. Imagine a DPIA not as a dusty document on a shelf, but as a dynamic dialogue. Regular reviews, especially in response to changes like new features, regulations, or security incidents, ensure your risk assessments remain relevant and responsive. Keeping your DPIA alive isn't a solo act. Foster a culture of open communication, DPIA and risk ownership where monitoring, reporting, and updating become shared responsibilities. This not only improves internal resilience but also ensures relevant authorities and stakeholders remain informed.

In the final step, regularly review and update the DPIA with changes in data processing or the environment. Monitor measures, report issues, and keep a documented record accessible to authorities or the public upon request, ensuring ongoing compliance and transparency.

Important part is to read the GDPR where 'data privacy' isn't defined and neither is the term 'data privacy impact assessment'. People and so-called 'experts' already struggle to understand there is a difference between data protection and privacy. The DPIA of the GDPR is a risk assessment with regards to the related processing activity. Not cyber risks. A risk to the Rights & Freedoms of people whose data will be processed. Security of data will be a risk (several actually), but other principles (art 5) need to be taken into account.

In my view DPIA plays a very vital role be it for Controller or processor. Understanding the geography of the Data, applicable regulations, data flows in detail, Risks involved and ensuring the necessary controls with key stakeholders accountability would make the Impact assessment effective.

In my view, it is essential to emphasize that for most companies DPIA is a project in itself and must be seen that way. More over, the actions, risks and concerns one finds in DPIA must also be followed up via a Project with a governance in place. Not doing so means the effort in DPIA is not serving its original purpose.

Approach DPIAs with a multidisciplinary perspective. Involving experts from various fields such as legal, IT, and business ensures a comprehensive assessment. This process shouldn't be rushed or treated as a mere formality to attract clients. It requires detailed attention to all aspects of data processing and potential impacts. Consider the lessons learned from major data breaches and how a well-conducted DPIA might have prevented or mitigated the damage. Sharing stories from different industries can highlight the importance of tailored approaches and continuous improvement in data privacy practices. DPIA is not just a regulatory checkbox but a crucial tool for building trust and safeguarding privacy in today's data-driven world.

In addition to the outlined steps, considering data breach response protocols can prove important. Establish a clear plan outlining actions in case of a breach, including notification procedures for affected parties and regulatory bodies. For instance, a healthcare organization conducting a DPIA should incorporate protocols for notifying patients if sensitive medical data is compromised. Moreover, periodic training and awareness programs for staff on data protection measures would complement the DPIA process, reinforcing a proactive approach to mitigate risks effectively.