Most discussions of AI tools for data engineering focus on code generation. I want to talk about a different kind of productivity gain: regulatory and compliance research.
Anyone building data systems in regulated industries — healthcare, financial services, government — spends a significant amount of time reading legislation, guidance documents, and regulatory updates. PIPEDA amendments, provincial health information act changes, OSFI guidance updates, CRA data requirements — staying current is part of the job. Perplexity has become my starting point for most of this research, and it’s worth explaining why.
What Makes Perplexity Different
Perplexity is, at its core, a search engine with an LLM layer. Unlike ChatGPT or Claude, which have fixed training cutoffs, Perplexity pulls current sources and provides citations alongside its answers. For regulatory research, this is critical.
When I ask “what are the current PIPEDA requirements for health data transferred between provinces,” I don’t want an answer based on training data from 18 months ago. I want current guidance with links to the actual OPC documents so I can verify and dig deeper. Perplexity delivers that.
How I Use It for Compliance Research
Initial Orientation on a New Regulatory Area
When a project takes me into a new area — say, understanding Ontario’s Personal Health Information Protection Act for a new client — I start with Perplexity to get oriented. A few queries give me the key concepts, the relevant legislation sections, and pointers to the primary sources. This replaces 30–45 minutes of searching and reading with 10 minutes of directed exploration.
Checking for Recent Updates
Regulations change. Before finalising a compliance approach, I use Perplexity to check for recent amendments or guidance. A query like “PIPEDA amendments 2024 2025 health data” surfaces recent regulatory news with sources, which I then verify directly.
Cross-Referencing Provincial Variations
Canada’s health privacy landscape is genuinely complex — PIPEDA, PHIPA, PIPA, HIA, and more, with different rules in Quebec under Law 25. Perplexity handles comparative queries well: “how does Quebec Law 25 differ from PIPEDA for health data organisations” produces a useful summary with citations to primary sources.
What Perplexity Is Not
Perplexity is a research starting point, not a compliance advisor. The answers are generally accurate but not always complete, and citations occasionally link to outdated versions of documents. For anything consequential — a formal privacy impact assessment, legal advice, regulatory submissions — you verify directly from primary sources and involve legal counsel.
The value is speed and orientation, not authoritative guidance.
The Limitation: Depth
For deep technical regulatory questions — specific exemptions, enforcement history, case-by-case interpretations — Perplexity’s answers are shallower than I’d like. It’s better at breadth than depth. For nuanced questions, I still go directly to the OPC website, legal databases, and where appropriate, formal legal counsel.
Practical Setup
I use Perplexity Pro, which gives access to more powerful models and allows for follow-up questions that maintain context. For regulatory research specifically, I set it to use “Academic” or “Writing” focus modes when available, which tends to prioritise authoritative sources over news articles.
One habit I’ve developed: for any compliance-relevant Perplexity answer, I open the top 2–3 cited sources directly and read them. The AI summary is the navigation layer; the primary source is what I actually rely on.
Net Assessment
For data engineers working in regulated industries, Perplexity is a legitimate productivity tool for the research component of compliance work. It’s not a replacement for legal expertise or careful primary source reading. But as a way to orient quickly, check for recent developments, and understand the landscape of a new regulatory area, it’s genuinely useful.
The cited, current-source model is the right design for this use case. I’d take it over ChatGPT or Claude for regulatory research specifically, despite those being more capable models for most other tasks.