5 Ways to Address AI Security Challenges During the PoC Phase

While GenAI offers transformative outcomes for business, it also presents a series of technical, data and skill challenges, described below.

Privacy and compliance concerns

Data privacy and regulatory compliance are the primary business barriers, especially for organizations in regulated industries. There is a significant concern about exposing sensitive data during AI model training and operationalization, which hinders broader adoption.

As per the Canadian Cybersecurity Study, 63.6 percent of respondents view data security and regulatory compliance as barriers to full GenAI production.

GenAI skill gaps

A critical obstacle is the lack of skilled resources required to develop, deploy and manage GenAI models. Without in-house expertise, many organizations may struggle to operationalize AI initiatives effectively, leading to stalled projects.

Unavailability of high-quality data

AI models need organizational data that’s free of biases and impurities to deliver value at scale. IT teams often face challenges in segregating data into clean, labelled chunks that can be consumed by AI models, leading to unsatisfactory outcomes with their GenAI PoCs.

The following ways explain how organizations can address AI security issues to build a stronger, more secure foundation for their GenAI initiatives.

In AI development, technical debt refers to the gaps left behind when security isn’t built in from the start. If the IT environment has such gaps, they may adversely affect the AI PoC, making AI systems slow to scale and more vulnerable to attacks.

In the PoC stage, security controls may be overlooked when you move quickly. But unresolved security gaps often go on to create weaknesses that stall AI progress later when the model needs to scale into production.

“At scale, a lot of foundational security practices are still relevant to AI-driven initiatives. For instance, organizations that struggled with zero-trust initiatives a few years ago may also struggle with AI projects,” noted Roshan Abraham, Principal – CISO Advisory Services, CDW Canada.

Resolving this security technical debt begins with the following practices.

Asset visibility

Visibility can be improved by building a complete inventory of data, devices and systems as a first step in controlling risk. It helps to map out what needs protection or where vulnerabilities exist, so that risky areas can be targeted.

Vulnerability management

A vulnerability management program keeps a record of known vulnerabilities and constantly scans the system for new ones. This helps fix critical vulnerabilities throughout the model, infrastructure and data workflows so that the production version stays secure.

Robust data governance

Governance plays a central role in establishing rules around data ownership, access and usage to prevent accidental misuse or leaks. It ensures AI models only train on and access approved data sources.

Protecting the privacy of business data not only helps meet compliance requirements but also builds trust in AI systems.

In the PoC phase, organizations often work with diverse datasets that may contain personal, proprietary or regulated information. If privacy is overlooked, the PoC can expose the business to reputational damage, regulatory fines or stakeholder mistrust, stalling adoption before the project matures.

“Without privacy guardrails, most organizations are going to struggle with protecting their data and keeping their models from ingesting the information they shouldn't,” Abraham said.

Therefore, weaving privacy into the design from the start sets a strong foundation for the GenAI PoC.

The following privacy-preserving techniques can be employed to achieve this.

Federated learning

Instead of pooling all data into a single location, federated learning trains AI models locally on devices or within secure environments. This way, only the model updates (not raw data) are shared back to a central server, reducing the risk of sensitive information being leaked or misused.

Differential privacy

This technique introduces statistical noise into datasets or outputs, making it nearly impossible to trace results back to individual records. It allows organizations to draw insights from data while protecting the identities and details of the people or entities involved.

Encryption methods

Techniques such as homomorphic encryption (performing operations on encrypted data directly) or secure multiparty computation keep data encrypted even during analysis. This ensures sensitive information remains confidential while still being usable for AI training and inference.

By proving security and privacy at the PoC stage, organizations are better positioned to scale the AI solution into production without reworking or retrofitting critical protections.

The Canadian Cybersecurity Study found that 56.7 percent of organizations said a lack of skilled resources to operationalize GenAI models is a key barrier to GenAI development.

This points to a significant AI skills gap among Canadian organizations, making internal training a must for fueling PoC development.

AI training programs should emphasize real-world skills such as AI model governance, secure data handling, ethical AI deployment and threat detection. This broader skillset benefits the organization's overall security posture beyond just AI initiatives.

Abraham strongly recommends comprehensive training, particularly regarding ethical AI deployment. “There needs to be comprehensive training on what AI is, how it should be used and what the building blocks of ethical AI are,” he emphasized. “These concerns must be addressed within organizations before they can really move beyond the PoC.”

CDW Canada houses AI and cybersecurity experts with hands-on experience in transitioning PoCs to full-scale AI applications. We offer a range of capabilities and services that can help resolve security challenges for Canadian organizations.

• Best practices risk assessments: We conduct comprehensive risk assessments against frameworks like the ISO 27001, CIS Top 18 and NIST CSF to evaluate your foundational security building blocks.
• AI governance readiness assessments: Utilizing the NIST AIRMF and ISO 42001 frameworks, we assess your governance function to ensure you have the necessary processes in place for AI adoption and training.
• Expert guidance: We provide organizations with the necessary guidance to tackle foundational challenges, which ultimately strengthens their overall security and IT foundations.

By leveraging CDW’s expertise, organizations can bring their AI ambitions to life, without overlooking security in the process. Learn more about the evolving cybersecurity landscape in Canada by downloading the latest Canadian Cybersecurity Study.