As the regulatory landscape tightens around data privacy, many healthcare advertisers are forced to react, limiting functionality, scaling back targeting, or navigating consent challenges. BranchLab took a different path. From day one, our platform was designed with privacy at the core, not as a constraint, but as a catalyst for innovation. This architectural choice does more than mitigate risk, it enables a new class of AI-native capabilities that legacy, ID-dependent systems simply can’t support.
To understand why this matters, consider the three categories of data that most often introduce legal and ethical complexity: sensitive, personal and inferred data. Our approach turns each one into an opportunity for smarter, scalable, privacy-resilient AI.
1. Sensitive Data
What it is: Information related to an individual’s health, biometrics, genetics, mental health, sexual orientation, or medical treatments.
Why it matters: HIPAA, MHMD, and emerging laws like New York’s S929 impose strict limits on how this data can be used, especially if it can be linked back to an individual, even indirectly.
2. Personal Data
What it is: Any information that can identify or be linked to an individual, from names and addresses to IPs, device IDs, and pseudonymous identifiers.
Why it matters: Persistent identifiers often fall under regulatory scrutiny, even when names are removed. Laws like MHMD clarify that data must be protected against re-identification, or it loses its de-identified status and triggers consent requirements.
3. Inferred Data
What it is: Assumptions or predictions about an individual’s behavior or health status, often derived from browsing habits, location, or other indirect signals.
Why it matters: Even when direct identifiers are removed, making assumptions at the individual level, especially about health, can carry significant regulatory and reputational risk.
BranchLab’s Approach
At BranchLab, our AI-native audience models are built on de-identified, aggregate-level data, never on persistent identifiers or sensitive information at the individual level. This deliberate design choice allows us to harness a broader spectrum of health, demographic, and behavioral data, much of which is inaccessible in ID-dependent systems. By applying predictive modeling across population-level statistics, we derive probabilistic insights that power targeting and measurement without ever crossing the line into personal inference. The result is a future-proof methodology that respects consumer privacy while unlocking precision and scale in healthcare marketing.
By eliminating the use of sensitive, personal, and inferred data at the architectural level, BranchLab doesn’t just ensure compliance; we enable entirely new AI-native solutions that consolidate existing workflows and ultimately improve performance. In an ecosystem where platforms and publishers restrict health data usage, and traditional identifiers are on the decline, BranchLab reimagines audience design, activation and measurement with one goal: improving real-world health outcomes.
In today’s market, privacy is a requirement. At BranchLab, it’s also a competitive edge.
Want to find out how BranchLab can help you future-proof your strategy? Get in touch
Michael Parkes is BranchLab’s Co-Founder, President & CRO