CyberVoices

Canada’s AI ecosystem is booming. With more than 670 startups and over 30 generative AI companies, Canada ranks fourth globally in generative AI firms per capita, according to Deloitte. The market was on track to reach USD $4.13 billion in 2024, fueled by fast-moving innovation hubs in Toronto, Montreal, Vancouver, and Calgary. However, behind the momentum lies a growing blind spot: cybersecurity. For many AI founders, the race to ship products and secure funding can overshadow security concerns. This tradeoff may seem efficient now, but it creates long-term risks that can derail even the most promising ventures.

Canadian Startups Can’t Afford to Ignore Cybersecurity

Most founders are familiar with PIPEDA, the Personal Information Protection and Electronic Documents Act, which governs how Canadian organizations handle personal data. But fewer are actively aligning with emerging global standards such as the EU AI Act, which is already shaping how AI systems must be designed, secured, and monitored worldwide. Even if your company does not operate in the EU today, your users or enterprise clients might. If you plan to scale internationally, security and ethics will become non-negotiable.

The Hidden Risks of Speed

Modern AI products are often built using large language models (LLMs) like ChatGPT, Claude, or open-source versions. Developers increasingly rely on tools like GitHub Copilot to generate code from natural language prompts. This trend is known as vibe coding. Vibe coding refers to the practice of using generative AI tools to accelerate software development, often without a deep understanding of the generated code. It enables rapid prototyping and gives non-technical team members access to development processes. However, the speed it enables comes with significant risks. These include insecure default settings, lack of input validation, poorly understood codebases, susceptibility to prompt injection and data leaks, and training data that may contain vulnerabilities or bias. These issues can accumulate quickly. One incident such as a data breach, flawed output, or regulatory violation can stall growth and erode user trust.

Security Best Practices Every Canadian AI Startup Should Adopt

1. Make Security Part of Your Product Design

Security should not be treated as an afterthought. It must be a fundamental part of product development. From day one, apply a security-first mindset when designing your AI systems. Conduct threat modeling to understand how your data flows, who interacts with it, and where potential vulnerabilities exist. This includes identifying high-risk areas such as unsecured APIs, risky third-party dependencies, and overly permissive access controls. Go beyond traditional application security by accounting for risks unique to AI systems. These include adversarial attacks, model extraction, and unpredictable behavior in fine-tuned models. Building with security in mind from the start can prevent major issues later in your product’s lifecycle.

2. Treat Your Training Data Like Code

Your model is only as trustworthy as the data it is trained on. Poor data hygiene can embed toxic language, biased assumptions, or sensitive information directly into your AI outputs. Avoid scraping random data from the web or relying on unverified synthetic content. Instead, establish a process to validate, sanitize, and document your datasets, just as you would with source code. This ensures reproducibility, transparency, and compliance with legal or ethical standards. Use scanning tools to detect anomalies, inappropriate language, or bias indicators. Store dataset documentation in a way that makes it easy to audit when needed. High-quality data is foundational to safe and effective AI.

3. Sanitize Prompts and Inputs

Prompt injection is one of the most critical threats facing LLM-based applications today. These attacks manipulate inputs to trick models into behaving in unintended ways. This can include revealing internal logic, executing hidden instructions, or leaking sensitive information. To prevent this, sanitize and validate user inputs rigorously. Avoid exposing system-level prompts to end users and implement safeguards for any feature that allows file uploads, command execution, or plugin integrations. Conduct regular AI red teaming exercises to test how well your application defends against malicious or unpredictable input. Controlling what your model sees and how it responds is essential to maintaining the integrity of your product.

4. Align Your Development with Security Standards

AI-specific regulations are still evolving in Canada, but global frameworks already provide valuable guidance. The NIST AI Risk Management Framework helps teams assess and mitigate risks associated with deploying AI systems. ISO/IEC 42001 offers a governance model for responsible AI development, while the OWASP Top 10 for LLMs identifies the most common vulnerabilities found in AI-powered applications. Adopting these standards early allows your startup to build systems that meet enterprise-grade expectations, improve credibility with investors, and prepare for future compliance obligations. 

5. Make Your Model Explainable and Traceable

The more opaque your AI model is, the harder it becomes to protect, debug, or improve. Explainability is  essential for building trust, ensuring accountability, and meeting regulatory requirements. Use tools like LIME or SHAP to help unpack how your models make decisions. Maintain detailed logs of model inputs, outputs, user interactions, and changes over time. Strong traceability supports both incident response and compliance with privacy laws such as PIPEDA and GDPR. It also provides a foundation for transparency when communicating with customers or responding to audits. When users understand your model, they are more likely to trust and adopt your product.

Closing Thoughts 

Security should not be treated as an after thought. The earlier you integrate cybersecurity into your startup’s culture and development lifecycle, the more resilient, trusted, and scalable your product becomes. By embracing secure-by-design principles now, your team can avoid costly rework, reduce risk exposure, and build the kind of AI that earns market respect and stands the test of time.Canadian AI founders have a unique opportunity in leading innovation and setting the bar for ethical, secure AI that puts users and trust at the center of technology.

Kelly Onu
is a cybersecurity consultant at EY with eight years of experience and a passion for building secure systems across various industries. She is an active community advocate, sharing thought leadership and mentoring emerging professionals through outreach and inclusion initiatives.