Understanding Risk in PIAs and DPIAs: When Is It Real Enough to Act?
Privacy Impact Assessments (PIAs) and Data Protection Impact Assessments (DPIAs) are not mere check-the-box exercises in compliance, they are strategic risk management tools designed to uphold the rights and freedoms of individuals in today’s data-driven world. With global privacy laws such as the EU’s General Data Protection Regulation (GDPR) and India’s Digital Personal Data Protection Act (DPDPA) emphasizing the importance of safeguarding personal data, organizations must be able to accurately assess whether a particular data processing activity poses a real risk to individuals—and if so, whether that risk requires mitigation or can be considered acceptable.
But this raises a complex and often misunderstood question: what exactly does “risk to the rights and freedoms of individuals” look like in practice? More importantly, when does that risk become significant enough to require action?
Risk: More Than a Legal Metric
Under both GDPR and other global frameworks, the term “risk” refers specifically to potential adverse effects on individuals not organizations. This is a crucial distinction. Risk in this context includes both physical, material, and non-material harm. These can range from identity theft, financial loss, and discrimination to emotional distress, social exclusion, or loss of autonomy.
For example, A mental health app collecting and processing “high-risk” sensitive data like mood logs, behavior patterns, and location. Despite pseudonymization, retained IP addresses and the vulnerable nature of users trigger multiple GDPR Article 35 criteria. Ethically, users may be unaware of the extent of data use, posing contextual risks. Business-wise, skipping a DPIA exposes the startup to legal, reputational, and investor risks. A DPIA helps document risks, apply safeguards, and ensure compliance. Using a DPIA checklist confirms its necessity. It’s not just advisable—it’s essential for responsible, lawful, and ethical data processing.
Risk assessment is not only about likelihood, but also impact severity. A low-probability event could still be high risk if the potential harm is serious enough. Consider a healthcare platform handling genetic data. Even if the chance of a breach is small, the potential consequences such as loss of privacy or discrimination based on health traits are severe enough to warrant proactive mitigation.
How to Identify “High Risk” in a DPIA
A Data Protection Impact Assessment (DPIA) is a structured process used to identify, evaluate, and minimize the risks to individuals' rights and freedoms that arise from processing their personal data especially in high-risk activities. It is a requirement under Article 35 of the EU GDPR and increasingly referenced in global privacy laws, including India’s DPDPA. It simply means where processing operations are likely to result in a high risk to the Rights and Freedoms person; the organization should conduct DPIA to evaluate nature, severity and origin of the risk.
Under Article 35 of the GDPR, organizations are required to carry out a Data Protection Impact Assessment when processing is “likely to result in a high risk” to individuals. The regulation outlines scenarios that typically trigger a DPIA, including:
- Systematic and extensive profiling or automatic decision making
- Processing of special categories of data (e.g., health, biometrics)
- Large-scale monitoring of public areas e.g., CCTV in public places
- Use of innovative technologies such as AI or facial recognition
- Processing that may result in individuals being denied rights and services.
If any of these conditions are met, a DPIA is not optional. It becomes a legal obligation. Moreover, the DPDPA in India also requires “Significant Data Fiduciaries” to undertake DPIAs before processing high-risk personal data. While not identical to GDPR, the underlying principle remains the same i.e., preventing harm before it occurs.
Measuring Risk: The Likelihood-Severity Matrix
A practical way to evaluate whether a risk is significant enough to require mitigation is the likelihood-severity matrix. This model helps organizations gauge both how likely a risk is to occur and how severe the consequences would be if it did.
Risk scoring helps quantify the overall risk of a processing activity by evaluating:
- Likelihood – How probable is it that harm will occur?
- Impact (Severity) – If harm occurs, how serious would the consequences be?
Using a risk matrix, you can plot risks as combinations of low, medium, or high likelihood and severity. For example:
- High likelihood + high impact = unacceptable risk
- Low likelihood + low impact = negligible risk
To justify a decision under regulatory scrutiny:
- Document your assessment process clearly in the DPIA.
- Show why a full DPIA was or wasn’t conducted (include a threshold assessment).
- Explain the mitigation measures applied.
- Justify residual risk levels and demonstrate ongoing monitoring or review
- If applicable, show evidence of consultation with a DPO or supervisory authority.
Regulators expect not perfection, but a reasoned, documented, and risk-based approach.
Proceeding After a DPIA: Is High-Risk Processing Allowed?
Yes, an organization can still proceed with high-risk processing after completing a DPIA, but only if:
- It identifies the risks clearly.
- Implements effective mitigation measures (e.g., encryption, human oversight, access controls), and
- Demonstrates that the residual risk is acceptable or manageable.
However, if the residual risk remains high even after applying safeguards, under GDPR (Article 36), the organization must consult the supervisory authority before proceeding. Failure to mitigate or notify could result in legal violations.
The DPIA itself is not a blocker, it is a decision support tool. Its purpose is to ensure that the risks are visible, justified, and proportionately addressed before data processing begins
DPIAs in laws like GDPR, DPDPA, and others. Do they match or conflict?
There is general alignment in intent but some variation in scope and terminology:
GDPR (EU): As discussed above in this blog DPIAs are mandatory for high-risk processing involving profiling, sensitive data, large-scale surveillance, or automated decisions with significant effects. Article 35 outlines these conditions clearly and also allow supervisory authorities to publish DPIA trigger lists.
DPDPA (India): The law introduces Significant Data Fiduciaries, who may be required to conduct DPIAs for high-risk processing, especially involving sensitive personal data, children’s data, or profiling. While similar in spirit to GDPR, DPDPA does not yet specify detailed trigger criteria, leaving it to future rule-making.
Other laws (e.g., UK GDPR, Brazil’s LGPD, Singapore’s PDPA): Most have adopted GDPR-like principles but differ in enforcement, consultation requirements, and definitions of high risk.
In short, while the core principles are aligned, the implementation differs; organizations operating across jurisdictions must harmonize their risk frameworks accordingly.
Conclusion
Understanding when a risk is real enough to act on is central to meaningful privacy governance. PIAs and DPIAs are more than regulatory hurdles—they are proactive instruments that help organizations align innovation with responsibility. In today’s data economy, the real risk isn’t just failing to comply with the law—it’s failing to respect the individuals whose data you process.
Being able to discern between acceptable and unacceptable risk is not just a privacy function, it is a strategic capability.
References
2. Secuiti.ai-https://securiti.ai/blog/pia-vs-dpia/
3. EU-GDPR-https://gdpr.eu/data-protection-impact-assessment-template/
4. Intersoft Consulting-https://gdpr-info.eu/issues/privacy-impact-assessment/
5. Digital Personal Data Protection Act, 2013.
