Data Protection Impact Assessment
Organisations across all sectors are accountable for the responsible handling of both personal and anonymous data. As such, organisations need to provide employee training to ensure staff awareness of their responsibilities when handling their organisation’s data.
Data Protection Impact Assessments (DPIAs) evaluate your project or process to ensure compliance with data protection standards and protect your orgnanisation from compliance gaps.
In some cases, DPIAs are legally required where data processing is likely to result in a high risk to the individual.
High risk processes will often include:
- Sensitive data
- Large scale processing
- Novel technology
- Children’s data
- Employee monitoring
- Denial of service
- Combining and matching data
DPIAs are essential for high-risk processes. However, while not all processes will fall under the scope of presenting a high risk to the individual, DPIAs remain good practice for any project or process to limit an oganisation’s risk exposure and to improve process documentation and accountability.
How do I know if my processing needs a DPIA?
Trilateral offers threshold assessments to determine whether it is necessary for your organisations to carry out a DPIA on the proposed system or processing activity.
If this assessment determines you need a DPIA, we recommend these are carried out as early as possible to avoid unforeseen delays and additional costs.
Step by step
| Step 1 | Determine if a DPIA is needed |
| Step 2 | Decide on a timeline for completion |
| Step 3 | Describe the data processing |
| Step 4 | Assess the potential risks and create a mitigation plan to address those risks |
| Step 5 | Review and update the DPIA as needed |
Why Trilateral?
Trilateral fosters a strong data protection and data governance culture within your organisation, enabling you to understand short- and medium-term risks so as to manage them proactively. Our training shares knowledge, best practices and tools to help your employees become confident in dealing with data independently and responsibly.
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