Automating Data Privacy Confidence with a PIA

What is a PIA?

A Privacy Impact Assessment (PIA) is a process that helps identify and manage any privacy risks that may arise from taking on new projects or systems that involve personally identifiable information (PII). PIAs are recommended by the EU’s General Data Protection Regulation (GDPR) and required for government agencies to perform under the U.S. E-Government Act.

Who is a PIA for and Why?

A PIA is primarily used by government agencies or organizations with access to large amounts of PII. While government agencies are required to perform PIAs, other organizations can also reap the significant benefits of PIAs:

1. Privacy By Design: By documenting how PII will be handled, organizations will have early insights into potential privacy problems before any investment and can build defenses from the outset.

2. Staying Out of the Headlines: Taking the time to identify security controls around PII will help organizations prevent any costly or embarrassing data privacy mistakes.

3. Trust and Transparency: PIAs demonstrate to customers and to the public that the organization takes privacy seriously. The ability to quickly produce a proper PIA could be the difference between winning and losing an enterprise contract.

The benefits are clear, but gathering the level of information about PII that PIAs require is no small feat.

Is Manual Collection of PII for a PIA Sufficient?

PIAs require a full inventory of every type of PII that will be collected, maintained, or disseminated for a new project or system and where that PII is going. Many organizations rely on manual processes to create and update their PII inventory in PIAs. These methods typically involve spreadsheets, questionnaires, and interviews with stakeholders. While this approach can initially provide a snapshot of data processing activities, it is inherently flawed for several reasons:

1. Error-Prone Documentation: Human errors, such as incomplete or inconsistent entries, are common in manual PII generation.

2. Time-Intensive Processes: Gathering and validating information from various teams and departments consumes significant time and resources.

3. Static Nature: A PII inventory that was manually created is quickly outdated as data flows, applications, and business processes evolve. For example:
   1. Applications are updated or replaced.
   2. New third-party integrations are introduced.
   3. Business units adopt new data processing practices.

4. Blind Spots: Manual processes often fail to capture shadow IT, undocumented workflows, or data exchanges occurring outside official channels.

In today’s dynamic enterprise environments, where changes occur frequently and data interactions are increasingly complex, manual PII inventory collection for PIAs is no longer sufficient.

Automation is the Key for a PII Inventory in a PIA

To address the challenges of manual PII inventory collection for a PIA, organizations need automated systems capable of continuously updating a PII inventory. These systems leverage advanced technologies such as network traffic analysis, application code scanning, and machine learning to identify, document, and monitor data processing activities in real time.

Automated PII Inventory Collection Benefits

1. Real-Time Accuracy: Automated tools ensure that the PII inventory reflects the current state of data processing activities, minimizing the risk of outdated or incomplete records.

2. Comprehensive Visibility: By scanning network traffic and application code, these systems uncover all data exchanges, including those that occur in shadow IT or between third-party APIs.

3. Scalability: Automated solutions can handle the complexity and scale of large enterprises, documenting thousands of data flows and applications without manual intervention.

4. Audit-Readiness: Real-time updates make it easier to demonstrate compliance during audits, reducing the stress and resources needed for last-minute preparations.

5. Risk Reduction: Continuous monitoring helps identify unauthorized data exchanges or changes in processing activities that could lead to compliance violations.

The Role of Network Traffic and Application Code Analysis

A robust automated PII inventory for a PIA should incorporate both network traffic analysis and application code scanning to achieve comprehensive data flow visibility:

1. Network Traffic Analysis:
   1. Captures data as it moves across the enterprise, identifying sources, destinations, and types of data being exchanged.
   2. Detects interactions with third-party systems and services, which are often overlooked in manual PII Inventory processes for PIAs.
   3. Flags unusual or unauthorized data flows that may indicate potential compliance risks.

2. Application Code Scanning:
   1. Analyzes software applications to uncover hardcoded API calls, database interactions, and data processing logic.
   2. Identifies changes introduced through updates, ensuring that the PII inventory in the PIA reflects the latest application behavior.
   3. Bridges the gap between IT and compliance teams by providing actionable insights into how applications handle data.

By combining these approaches, organizations can ensure that the PII inventory in their PIA remains a living document that evolves with their business.

Conclusion

PIAs are an essential tool for U.S. government agencies and an excellent tool for other organizations to ensure that their data privacy posture is secure from the start of implementing any new process or system, but also on an ongoing basis. However, it is clear that in order for a PIA to be effective the organization creating it cannot simply rely on the error-prone process of manually gathering a PII inventory.