Removing names and other obvious identifiers is important. But it does not show that the remaining re-identification risk is acceptable for a specific AI use. That requires a separate assessment of what may have been missed, what can still be linked to an individual and the controls around the data.

The Ottawa Business Journal published an article about our funding by international medtech investor Nina Capital our work to help organizations use data for AI while protecting privacy. A few highlights: this latest investment will support the continued development of our solution, EviData, which assesses the privacy risk of data used to train AI models so organizations can use data responsibly; Woodway is growing beyond Canada as organizations around the world face the same data access and privacy challenge; and Ottawa’s combination of research, health care and post-secondary expertise gives AI startups like Woodway a strong place to grow. The article is behind a paywall and available by subscription only. 

https://obj.ca/startup-spun-out-of-cheo-lands-funding/

Woodway Assurance today announced that international healthtech venture capital firm Nina Capital has invested in the Canadian company. The investment reflects growing demand for privacy-enhancing technologies such as Woodway’s EviData^TM, which helps organizations use data responsibly for AI, analytics and data sharing.

Read the full press release

Woodway Assurance congratulates its CEO, Dr. Khaled El Emam, and the Electronic Health Information Laboratory (EHIL), which he founded and leads at the CHEO Research Institute, on receiving the prestigious 2026 Privacy and Human Rights Award earlier this week from the Global Privacy Assembly, the forum for more than 130 privacy and data protection authorities worldwide, and the international human rights group Access Now. 

Read the full statement. 

On July 21, 2026, Woodway Assurance and the Electronic Health Information Laboratory hosted a webinar focused on automated, AI-enabled anonymization of text data, helping organizations unlock the value of text data for analytics, AI and data sharing. The recording is now available, as well as the presentation slides. 

Your AI team wants the data. Your instinct says: "not so fast." Who's right? It's the question landing on every privacy desk: can we actually use this dataset to train or power AI — and can we prove it if a regulator asks? The old answer ("it's anonymized") no longer holds. Regulators now expect contextual, quantitative proof that re-identification risk is genuinely low, and "we ran it through a checklist" won't survive scrutiny. The good news: the same pressure that's raising the bar is also making it possible to clear it at scale. On July 16, 2026, OneTrust Data Guidance hosted a webinar featuring Woodway’s Khaled El Emam, where we showed how privacy teams are using LLMs to turn slow, subjective data reviews into fast, defensible, repeatable decisions. 

https://onetrustprivacy.wistia.com/s/vjskkuak65v6m3k

Some of the richest data your organization holds isn’t sitting in a database. It’s buried in text: clinical notes, call-centre transcripts, claims narratives, support tickets, survey responses, incident reports, emails, case files. Text holds the patterns, context and signal that power sharper analytics, better research and AI models that actually understand your business. But text hides personal information in plain sight: a name dropped into a sentence, a date and a location that together point to one individual, or an offhand detail that re-identifies someone the moment it’s combined with other data. That risk is why so much high-value text never gets used. Join us on July 21, 2026 at 11 am EDT for a webinar on automated, AI-enabled privacy risk assessment for text data. We’ll walk through how to put text data to work responsibly and defensibly for analytics, AI and data sharing.

On June 23, 2026, Woodway Assurance and INQ Consulting hosted a webinar focused on how new AI-assisted approaches can help teams move through the full cycle of privacy risk assessment, data transformation, reassessment and evidence generation. During the webinar, we offer a demo of EviData’s capabilities. 

Woodway Assurance today launched EviData™ 3.0, evolving EviData into a multi-agentic AI platform that helps organizations protect data privacy by assessing whether datasets are ready for use and, when needed, performing privacy-preserving data transformations so valuable data can be put to work sooner for AI model training and inference, analytics, product development and responsible data sharing. 

Read the full press release

Organizations are under growing pressure to use data for analytics, research, AI and other secondary purposes, but assessing and managing privacy risk in anonymized or de-identified data remains difficult to do well, hard to scale and challenging to document. Even when organizations understand the need to assess re-identification risk, inference and other privacy risks, they often lack a practical way to apply rigorous methods consistently across datasets and decisions. The challenge becomes even greater when a risk assessment identifies areas of concern: teams then need to specialized expertise to understand what is driving the risk, decide which transformations may reduce it, apply those transformations without destroying the value of the data, reassess the dataset to determine whether the risk has been reduced enough for the intended use, and automatically produce the necessary robust evidence to support the assessment results. Join us for a fireside chat-style webinar on June 23rd at 11 am EDT. Dr. Khaled El Emam, CEO and Founder of Woodway Assurance, and Carole Piovesan, Co-Founder and Principal of INQ Consulting, will discuss and showcase how new AI-assisted approaches can help teams move through the full cycle of privacy risk assessment, data transformation, reassessment and evidence generation. Such AI agents make it easier for data analysts and business users to understand the risks in their data and to be guided through the steps to reduce and to manage that risk. This kind of AI sidekick democratizes re-identification risk assessments that work across jurisdictions.