Michael Ogbu

When people think about data privacy, they often imagine firewalls, policies, or breach responses. But if you ask any privacy professional, they’ll tell you this: real privacy leadership shows up before the first byte of personal data is ever collected. It shows up in the Privacy Impact Assessment (PIA).
And here is the truth – conducting a PIA isn’t a checkbox exercise. It’s a craft, an art.

What Exactly Is a PIA?

At its core, a Privacy Impact Assessment (PIA) is a structured process designed to identify potential privacy risks in a new or substantially modified system, project, or initiative, assess the impact those risks could have on individuals’ personal information, and recommend controls to mitigate the risks, before harm occurs.
In Canada, PIAs are not just good practice. They are embedded in public sector operations under the Privacy Act and under some Provincial and Sectoral legislation (e.g. Ontario’s FIPPA and Alberta’s HIA) and are increasingly becoming standard for private-sector entities governed by PIPEDA. The proposed Bill C-27 even strengthens this further by placing more onus on organizations to demonstrate accountability upfront – especially for high-impact technologies like AI.

The Process in a Nutshell

A well-run PIA typically includes the following stages:
Initiation & Scoping
Understand the system or project being assessed and determine if it will involve personal information. Define what personal information is being collected, why, how, and from whom.
Data Mapping
Visualize the flow of data through systems, from collection and storage to sharing and disposal. Identify third parties, vendors, and cross-border elements.
Privacy Risk Analysis
Evaluate the likelihood and severity of risks such as over-collection, unauthorized access, lack of transparency, or secondary use without consent.
Consultation
Engage stakeholders from IT, legal, HR, product teams, and, in public sector cases – sometimes the public.
Recommendations & Mitigation
Propose design changes, policy tweaks, access controls, or staff training to reduce risk.
Approval, Report & Ongoing Monitoring
Document everything, secure sign-off, and revisit the PIA if the project evolves.

To support privacy professionals in this journey, I’ve also created a free downloadable PIA Checklist that walks through these steps. You can download it below and adapt it to your organization’s context.

The Real-World Challenges
Here’s the catch – while the framework sounds straightforward, in practice, PIAs can be messy, political, and layered with nuance:
Access to information: Privacy teams often struggle to get complete answers from technical or business units. Data flows may be undocumented or misunderstood.
Late involvement: In most cases, by the time privacy teams are brought in, systems are already built – and no one wants to “slow down innovation.”
Conflicting priorities: What legal calls high-risk, engineering may see as “just metadata.” Negotiating these tensions, therefore, requires diplomacy and technical fluency.
Emerging technologies: AI, biometrics, and smart sensors present new challenges – from algorithmic transparency to data provenance.
Lack of support: A PIA is only as effective as the commitment from leadership. If risk recommendations are ignored, you’re left with mere paper shields.

Tools and Tips for Success
If you’re a privacy professional navigating the PIA process, consider these:

Use a standardized template aligned with regulatory guidance (e.g. from OPC or provincial commissioners).
Incorporate threat modeling – partner with security and data architecture teams.
Prioritize high-risk activities like profiling, automated decision-making, and international data transfers.
Educate stakeholders early on: a short “PIA 101” session can reduce friction later.
Create a risk register for visibility and continuity across projects.

PIAs in the Age of AI
As AI systems become more embedded in decision-making (especially in HR, healthcare, and finance), PIAs need to evolve too. The risks are no longer limited to data leakage, they now include:

Bias and discrimination
Opacity in decision logic
Inability to obtain meaningful consent
Lack of explainability

That’s why Algorithmic Impact Assessments (AIAs) are emerging as a complementary practice and will likely be formalized under Canada’s AI and Data Act (part of Bill C-27) when passed into law. Privacy professionals need to stay agile, because risk isn’t static – it’s contextual, cultural, and constantly shifting.

Final Thoughts
Doing a PIA well isn’t about filling out forms. It’s about reading between the lines, asking hard questions, and sometimes being the voice in the room that says, “Just because we can, doesn’t mean we should.”
The truth is, PIAs are where privacy meets ethics, design, and strategy. They’re how we build not just compliant systems, but respectful ones.
And in a world hungry for trust, that’s the edge organizations can’t afford to miss.