Securing the ML Lifecycle
PubNews Data Privacy provides expert technical advisory on securing machine learning pipelines and protecting sensitive data assets for Canadian corporate infrastructure.
Technical Pillars
Our advisory is built on absolute transparency and data sovereignty within Canadian borders.
Integrity
Pipeline Privacy Audit
We verify existing machine learning training pipelines in production to identify and remediate data leakage risks and PII exposure. Our focus centers on residency and algorithmic transparency.
Standardization
Innovation
Differential Privacy
Implementing mathematical noise mechanisms to protect individual data points during model training without compromising intelligence output.
Strategy
Synthetic Data
Advisory for teams needing to train models on high-sensitivity tabular datasets while maintaining zero-visibility of real personal identifiers.
Federal Alignment
Direct mapping of ML architecture to Bill C-27 and Artificial Intelligence and Data Act (AIDA) requirements for Canadian enterprises.
The Ottawa Standard
A localized adaptation of global frameworks for corporate governance.
Intake & Infrastructure Mapping
Identifying all data ingress and egress points in the training landscape. We document the lineage from ingestion through processing layers, ensuring every node is classification-aware.
Sensitivity Stress Test
Defending through structured attacks. We attempt to reconstruct PII from model outputs using inversion and membership inference techniques to prove the resilience of implemented masking.
Governance Deployment
Establishing continuous monitoring protocols. We deliver technical documentation tailored for both the Ethics Committee and the IT Security Lead, closing the gap between compliance and engineering.
Protocol FAQ
Initiate Scoping
Contact Dossier
Corporate Address
400 Laurier Ave W,
Ottawa, ON K1R 7X7, Canada
Direct Intake
+1-613-553-5742Encryption Key
[email protected] PGP PKResponse Expectation: <24h (Mon-Fri)
Data Residency: On-Shore Canada