Protecting Canadians’ health information from internal and external risks

Canada’s healthcare sector is one of the most vulnerable areas in cyberspace. Personal health information (PHI) is among the most sensitive data, with a value far exceeding financial data on the dark web. Unlike a credit card, stolen PHI cannot be recovered or replaced.

While external threats like ransomware dominate headlines, organizations must urgently address the ongoing risk of accidental data loss by users.

A solid Data Loss Prevention (DLP) strategy is essential for maintaining cyber resilience.

DLP technology provides critical visibility into how data is used and moves within your organization.

It dynamically applies security policies based on the content and context of data, whether it’s in use, in transit, or at rest.

The cornerstone of any effective DLP solution is a robust data classification program. This program is fundamental to both security and compliance. Clear classification defines data confidentiality and serves as a basis for assessing risks.

Once PHI is identified and classified, the DLP solution enforces strict policy controls. In the event of a violation, the system can trigger monitoring, alerts, blocking, or quarantining measures.

In today’s interconnected digital health ecosystem, DLP addresses three critical points of vulnerability:

Human error remains a leading cause of data breaches in healthcare. DLP provides tools to manage both negligence and malicious intent.

• Malicious or negligent behaviour: Modern DLP solutions integrate with insider risk management (IRM) platforms. This approach enriches DLP events with insights into anomalous user behaviour, helping security teams distinguish between malicious actions and simple mistakes.
• Cloud protection: As services migrate to cloud environments, DLP ensures PHI remains protected. Organizations adopting a cloud-first strategy often choose cloud-native DLP solutions for robust protection.

Healthcare relies on interconnected systems, from electronic medical records (EMRs) to specialized operational technologies (OT) like dialysis machines.

DLP ensures that:

• Data protection throughout the lifecycle: Sensitive data is safeguarded at every stage, using encryption for data in transit and at rest.
• OT data security: Data collected by OT and medical devices is protected during storage and transit to other environments, following encryption practices that meet compliance standards.
• Controlled test environments: Sensitive production data used in test environments is anonymized wherever possible to minimize exposure.

The use of generative AI in triage support and documentation is rapidly expanding. Without strict safeguards, PHI can leak into queries, logs, or third-party tools. AI-based attacks are increasingly seen as a major potential threat.

DLP acts as a key defense mechanism against these risks:

• PHI Redaction: DLP techniques can be applied to automatically remove patient identifiers (names, numbers, dates of birth), preventing accidental exposure to external AI platforms.
• Model Integrity: DLP helps defend against data poisoning, which degrades AI model performance, and model inversion attacks, which attempt to extract sensitive information from trained models.

Implementing DLP enables proactive remediation, such as automatic blocking of unauthorized data transfers.

For healthcare organizations with limited IT security resources, deploying an Enterprise DLP (EDLP) solution is recommended if users handle sensitive information across multiple channels. Engaging consulting and managed services can also supplement internal teams and accelerate ROI.

To effectively protect PHI, healthcare leaders must align their DLP strategy with strong data security governance and secure their information before it is lost or exposed.