Building AI vs. Using AI: What’s The Right Approach for Your Business Needs?

January 14, 2025

Yes, AI is a force multiplier that enables you to achieve better outcomes with fewer resources.

But should you use an existing AI tool or build your customized AI solution from scratch? This is an important decision because of the risks of wasting development effort or getting locked in with the wrong AI vendor.

In this blog, learn about the security pros and cons of using existing AI tools vs. building AI solutions to help you make a more informed choice.

Is AI the Right and Necessary Path?

Before we go any further, there is one important question to consider: just because you can, does that mean you should? Deploying AI against a problem might cause more trouble than it’s worth. The first step of your risk evaluation should include weighing the benefits and risks to determine whether any given use case needs or deserves your AI investment resources. If all parties agree that AI is required, then comes the question we help answer in this blog: Build internal? Or look for an external solution?

Resolving the AI Build vs. Third-Party AI Usage Dilemma

Understand the strategic importance, estimated resource availability plans and possible risks before building a custom AI solution.

Strategic Value

Determine the strategic value of your AI investment by understanding how the proposed AI solution aligns with your core business competency. Whether you are seeking to improve your customer service experience or improve efficiencies, make sure that you can clearly articulate how your AI solution helps you to achieve your unique goals. To ensure that your decision is not made in a silo, consider asking your team how the AI solution will impact user experience, production quality, time to market and more.

For example, an AI-powered predictive analysis feature can enhance the user experience of customer relationship management (CRM) software by adding more context-rich suggestions. Such a feature could therefore have high strategic value for CRM operations. However, the same capability may not significantly enhance the user experience for, let’s say, a collaboration software tool requiring custom-built AI features to maximize both productivity and data privacy. Thus, organizations would need to decide whether investing in a third-party tool or in-house resources to build such an AI-powered predictive feature would be the best strategy to maximize the strategic value of their investment.

Another nuance to understanding the strategic value of building an AI solution is figuring out how the solution aligns to your business’s long-term goals. For example, if our hypothetical business is in the process of launching an email tool, then an AI-powered predictive feature that offers better spam protection is a good long-term fit because it helps reduce the manual efforts and human risks involved in catching spam.

Resource Availability

Estimate how much spare in-house talent capacity, development time and budget you can allocate to build the AI solution. The answer will help you set realistic expectations.

Having the right skills in-house is often a significant deciding factor. For instance, upgrading an existing API-as-a-product offering into an AI-powered self-integrating API might be feasible. However, building an AI application from scratch to revamp your existing manual billing processes will require significantly more skills, time and budget.

In many instances, resource availability also boils down to opportunity costs, such as what other outcomes might be possible with the budgeted resources if you don’t build the proposed AI solution.

Risk Tolerance

Understand how much risk your business can take and how much uncertainty your users and industry regulations will tolerate.

Begin by investigating the extent of harm your proprietary AI/ML model may cause to your users, organization and industry ecosystem. A risk assessment framework such as NIST AI RMF may help evaluate your AI risk potential.

Consider what will happen if your AI/ ML solution faces a data poisoning attack. What are the worst possible outcomes, will you be able to reverse them and how quickly can you recover? For instance, can your poorly performing AI solution harm users physically or economically? Will it lead to lasting economic losses and reputation damage for the business? Or can it negatively impact your industry supply chain or local environment?

Your industry and users will also determine your AI risk tolerance to a large degree.

For example, the consumer technology industry is far more forgiving than, let’s say, the healthcare or finance industries. Industry regulations such as HIPAA and PCI DSS pose strict limitations on how user data is consumed. Also consider regional regulations such as the EU AI Act and local U.S. legislative acts.

Advantages of Using Third-Party AI Solutions

Using off-the-shelf AI allows your business to quickly deploy a working solution without requiring your team to develop deep AI expertise. It also offers the following advantages:

Quick Feedback

You can deploy off-the-shelf AI solutions at a fraction of the time it takes to build a custom system, meaning you will get user feedback and validation much more quickly.

Lower Costs

Buying an AI solution eliminates high upfront costs for custom development and hiring processes over a long development timeline.

Support and Maintenance

Third-party AI solutions will often include ongoing support, maintenance and security updates without needing additional resources from your end.

Challenges of Securing Third-Party AI Solutions

Securing third-party AI solutions is challenging because it is difficult to measure AI-related security risks. Furthermore, there is no straightforward way to measure how bias, ethics, fairness and data ownership affect a third-party AI solution.

Reducing Bias

AI algorithms aren’t intentionally biased. However, it is hard to avoid faulty and incomplete conclusions altogether.

For example, suppose the third-party AI solution you use has used training data with very few representations of women or ethnic groups. In that case, the model may likely be biased against these minority groups. Because you have no control or information over these limitations, you can only hope and rely on the service provider to fix the bias.

Maintaining Ethical Boundaries

Establishing AI governance through your organization will demand a significant time commitment and constant vigilance.

Existing references such as MITRE ATLAS (TM) or the OWASP AI Security Privacy Guide may offer broad guidelines. But you still need to customize AI policies based on your business goals, industry regulations and user demands. You also need to ensure that the AI model does not inadvertently misuse user data in unethical ways.

Promoting Transparency and Fairness

Third-party AI tools are not always adequately documented with information on model intent, potential harm risks and bias factors.

Third-party AI tools may deliberately keep their AI/ML models as black boxes for competitive reasons. The lack of transparency means you will not be able to judge the fairness of the results.

Understanding Data Privacy and Ownership

Third-party, open-source, public AI tools may divulge some user information to third-party sources. More often than not, the service terms of third-party software tools will contain data-sharing clauses. So, read any agreements carefully to understand whether your data will be shared, with whom, and the guardrails you may need to deploy or adjust to ensure confidentiality of your data.

Likewise, data breaches and information stealing are always possible, even if an AI solution’s service terms align with your security requirements. So, building your own secure AI solution is a better option if your users are willing to pay a premium for their privacy.

Understand if the third-party AI solution will provide a copy of the user’s data upon request. Be conscious of any significant data privacy changes the service provider may make.

Advantages of Building Custom AI Solutions

It no longer takes several months to develop custom AI/ ML models. You can build a custom AI solution that addresses your business needs without sacrificing time-to-market (TTM). Regarding quicker AI development processes, Andrew Ng, the Founder of Landing AI, says, “What used to take good AI teams months to build, today you can build in maybe ten days.”

The other advantages of building a custom AI solution include:

Differentiation

A custom solution with unique capabilities will differentiate your offering from competitors and help you attract your ideal set of users.

Scalability

The cost of pay-per-use AI SaaS quickly escalates as your usage grows. However, over time, custom-built solutions can scale and result in a lower total cost of ownership.

Knowledge

Developing an in-house AI solution will help your teams focus on scalable projects requiring deep, valuable technical expertise and upskilling.

Control

Building in-house will give you complete control over data privacy and security, making it the right choice for tightly regulated business efforts.

Challenges of Securing Custom-Built AI Solutions

To secure your custom-built AI solution, you must create processes to monitor and govern a wide range of AI use cases.

Security

Adopting security-by-design principles into your custom AI solution requires ongoing effort, especially before your AI solution is deployed and as iterative improvements are needed throughout the AI product lifecycle. For instance, you must assess where your organization is today with AI security and predict future requirements. You must also constantly protect your custom-built models from AI cybersecurity incidents, theft and misuse. Investing in your AI security tool stack is also key.

Talent

Hiring the right talent is a significant challenge, particularly when assembling and managing a SOC team that has to continually upskill in order to effectively tackle evolving threats.

An effective security team must be able to investigate various threat scenarios and identify possible vulnerabilities. They also need to assign risk levels to each threat and prioritize threat mitigation strategies. Lastly but also most importantly, the team must develop an analysis and feedback structure for continuous improvement.

Unless AI cybersecurity is part of your core value proposition, achieving all these functions in-house is challenging. Without automation, manual threat detection, triage and analysis work requires extensive internal resources. Every second counts when it comes to quickly identifying, containing, mitigating and remediating a threat in order to minimize the impact on business operations.

Monitoring and Maintenance

Building a custom AI solution sows the seeds of ongoing monitoring and maintenance. You must constantly monitor the hosted environment for AI and security risks with a stack of advanced cybersecurity tools and services.

For example, you may need an AI integration for managed detection and response (MDR). Unlike static rule-based software, advanced AI tools constantly learn to combat new attack patterns and identify unknown threats. However, an AI-powered MDR can also overwhelm your SOC team because it scans terabytes of logs, user activity and network data points and detects hundreds of anomalies. So, maintaining and constantly finetuning a baseline of regular activity takes some effort.

Securing Your AI Development Process with Optiv

Ad-hoc and cobbled-together point solutions can’t fully secure your custom-built or third-party AI solutions. Our team of experts can help you implement sound security by design principles by co-creating a robust AI governance framework along with your security, privacy, risk and legal teams. Based on your needs and threat profile, we’ll help you select and invest in a stack of appropriate AI risk management tools.

Download our field guide today to start your journey of securing AI.