How to choose between custom AI or off-the-shelf AI

The AI implementation decision that keeps IT leaders awake at night isn't about functionality or cost - it's about security. Take Samsung for example, which banned the use of ChatGPT among its staff after developers accidentally exposed internal, proprietary source code through prompt inputs. When choosing between custom AI and off-the-shelf solutions, a single incorrect security misjudgment like this can expose sensitive data, trigger compliance violations, or compromise competitive intelligence.

The challenge is that these security implications aren't immediately obvious. Off-the-shelf AI platforms offer convenience and proven capabilities, but they operate on shared infrastructure where your data is mingled with that of countless other organisations. Custom AI promises complete control, but requires expertise that many businesses lack internally.

As Lloyd David, AI specialist at Huon IT, puts it, "With tools like ChatGPT, all data is used for the broader AI community, but more customised models are much more secure - it's chalk and cheese in terms of data protection."

Understanding these security trade-offs is essential for making decisions that protect your business while enabling AI innovation.

Security fundamentals: Two different approaches

The core security distinction between custom AI and off-the-shelf solutions centres on data control and infrastructure ownership.

Custom AI security profile

Custom AI implementations provide complete control over your data environment. Your organisation owns the entire security stack, from data ingestion through to model outputs. This approach eliminates external data sharing risks but requires comprehensive internal security expertise.

Key characteristics include isolated data processing, customisable security protocols and full audit visibility. However, this control comes with the responsibility of implementing and maintaining enterprise-grade security measures.

Off-the-shelf AI security profile

Off-the-shelf solutions operate on shared infrastructure managed by AI vendors. While this reduces internal security responsibilities, it introduces dependencies on vendor security practices and shared-system vulnerabilities.

"Web-based AI doesn't provide control over security, whereas tools like Microsoft Copilot 365 are more locked down with better data governance capabilities,” Lloyd notes. “The difference is that enterprise solutions allow you to create isolated environments where you can categorise and protect sensitive data, while public AI tools essentially treat all input as fair game for broader model training." 

This vendor distinction is crucial because it determines whether your AI implementation introduces new attack vectors or strengthens your existing security posture.

Critical security questions for AI vendors

Lloyd emphasises the importance of these conversations: "Many businesses aren't asking the right questions around security, particularly about PII storage and data categorisation. This creates significant vulnerabilities that can be mitigated."

Before implementing off-the-shelf AI, Lloyd recommends asking vendors these essential security questions:

Data governance and control:

• Do you have controls in place for how our data is currently used?
• Are you categorising data to prevent sensitive information exposure?
• How can you ensure our data won't be used against us in training broader models?

Infrastructure and liability:

• How are you storing personally identifiable information (PII)?
• If a security incident occurs, who bears liability under your standard terms and conditions?
• What specific security measures protect our data from unauthorised access?

Risk assessment framework

Effective AI risk assessment requires a structured approach that evaluates multiple security dimensions. This framework, informed by industry best practices and Huon IT's implementation experience, helps organisations make security-informed decisions.

Data classification and governance assessment

The foundation of AI security begins with understanding your data landscape. "The first step is isolating PII and implementing data governance tools like Microsoft Purview,” Lloyd emphasises, “This creates a data shield that allows AI systems to access operational data they need to function effectively, while automatically blocking sensitive information like customer records, financial data, or proprietary business intelligence. It's about smart data categorisation rather than blanket restrictions that limit AI's usefulness."

Here are some critical data categories to evaluate:

• High-risk data: Personal identifiable information, financial records, health information, and proprietary business intelligence require the highest protection levels. AI systems processing sensitive data must implement privacy-by-design principles during development to ensure personal data is handled ethically and in compliance with the Privacy Act 1988 and other industry-specific regulations.
• Medium-risk data: Customer communications, operational metrics and non-strategic business information can often be processed by custom AI or well-governed off-the-shelf AI solutions with appropriate safeguards.
• Low-risk data: Public information, general productivity content and non-sensitive analytics typically present minimal risk regardless of the chosen AI approach.

Security maturity evaluation

Assess your organisation's current security capabilities against AI-specific requirements:

• Infrastructure readiness: Evaluate whether your current security architecture can support AI implementations. Maintaining strong security measures such as end-to-end encryption, access controls, and data anonymisation is crucial for protecting AI models and the data they handle.
• Governance frameworks: Determine if existing governance policies adequately address AI-specific risks like model bias, data drift and algorithmic accountability.
• Incident response capabilities: Implementing thorough audit trails and explainability features in AI systems helps ensure accountability and build trust, reducing the risk of legal challenges.

Making the security-informed decision

The security implications of choosing between custom AI and off-the-shelf solutions will define your organisation's AI success. Lloyd notes that “The decision often comes down to your organisation’s risk appetite, which is often very specific to your industry and your business offering." 

Your decision framework should prioritise data protection above convenience or cost savings. Custom AI offers unmatched control for organisations processing sensitive data, while off-the-shelf solutions provide enterprise-grade security for less critical applications.

Ready to make smarter decisions about AI? This AI implementation guide helps you align technology with strategy, so you can start your AI journey with expert-backed confidence.