Third-party integration is a fact of life for any company. External SaaS has been powering human resources, finance, software development, and other departments for years. AI is no exception, with critical functionality such as large-scale language models increasingly being provided as third-party services or components.
However, third-party integration can be difficult. Despite the great appeal and potential value proposition of third-party AI services, external AI providers come with risks. Businesses need to properly integrate AI elements with their internal data stores, applications, workflows, and technology infrastructure while ensuring reliable performance, security, and compliance. Without proper planning and governance, various vulnerabilities in third-party AI integrations can wreak havoc on your business.
Third-party AI integration vulnerabilities
Relying on external AI services reduces effort, lowers costs, and speeds time to market. This provides valuable benefits in today’s rapidly changing business environment. However, new challenges may arise as companies must carefully integrate external AI into existing software and systems. While all third-party integrations come with risks, there are three specific vulnerabilities for AI vendors.
1. Use of questionable or unclear data
AI vendors collect data every time a client uses their models or other software components. AI components may also require access to organizational data stores that the AI vendor uses for analysis and processing. It’s important to identify exactly what corporate data third-party AI components access and understand exactly how the vendor retains and uses that data. Because large amounts of corporate data are sensitive, sensitive, and protected by compliance obligations, companies must clearly monitor and control how vendors access, store, and use their data.
2. Unclear or fragmented authority
All third-party components or services require permission to access your business’ systems and data. The sheer number of third-party components and potential enterprise systems and data stores in service make it easy for AI permissions to be inaccurate, incomplete, or inconsistent, all of which can lead to security vulnerabilities and sensitive data exposure. Enforce zero trust policies and ensure third-party AI components receive accurate, complete, and consistent permissions.
3. Poor governance
Companies must ensure that they and all of their vendors share the same standards of governance and compliance. AI vendors with weak or absent AI governance may leak, share, resell, or publish sensitive data, or their AI components may perform poorly. Companies need to ensure that their AI vendors establish and maintain proper AI governance.
Best practices for third-party AI integration
Integrating third-party AI comes with extensive risks, but with careful consideration, planning, and preparation, vulnerabilities can be addressed and remediated.
Operational aspects of AI integration
Addressing third-party AI integration issues often begins with detailed component selection and careful monitoring and validation of each component.
- Selection and planning. Use proof-of-concept projects to test third-party AI components and evaluate their performance, ease of use, and compatibility with existing infrastructure, systems, and software already in operation across your enterprise. Compare the behavior of similar third-party AI components and choose the best available product. Limited third-party options may require more work to adapt and integrate external AI components into your existing environment.
- Workflow integration. Third-party AI components should work to support and even enhance existing AI workflows. Otherwise, external AI services can create unwanted bottlenecks or degrade AI performance, negatively impacting CX and limiting adoption.
- HITL verification. Consider how humans interact with third-party AI components and AI systems as a whole. Human-involved protocols may be required for complex decision-making and mission-critical results. Third-party AI components without these critical human checks can have serious consequences for businesses and AI platform users.
- Monitoring and analysis. Use monitoring to record and evaluate data sent to and from third-party AI components. Analyze the accuracy and performance of component monitoring data and the entire supported AI system. This helps determine how external AI services impact the overall AI platform and provides early warning of potential issues with third-party AI components.
Security and privacy in AI integration
Third-party AI requires data from your business. It may be something as simple as a user prompt, or it may require large amounts of data for analysis. Regardless of the type or amount of data, integrations must address data security and privacy by:
- Security adjustments. Evaluate the security capabilities of third-party AI components and ensure they align with your business security requirements. Apply techniques such as Zero Trust to third-party AI agents and other components to ensure that third-party AI components have access to only the minimum local resources needed to operate.
- Data anonymization and encryption. Protect local data stores. Use encryption to protect data sent to and from external AI services. If third-party AI needs to access sensitive or personally identifiable information (PII), consider applying techniques such as data anonymization to protect PII from potential exposure.
- Permissions. Carefully examine your permissions and consider how they map to your existing role-based access control or other local access control mechanisms. Minimize access to external services and ensure that permission changes are human reviewed and approved before implementation.
- Secure network and connectivity. Local data encryption alone may not be enough. Sensitive or critical AI systems may use secure networks and connectivity through techniques such as network segmentation, virtual private networks, and zero trust network access to enhance secure data exchange between the business and external AI components.
Compliance and governance in AI integration
Companies must meet compliance and governance requirements. They also carry the burden of ensuring that external vendors and service providers meet the same regulatory requirements. Although companies cannot control external vendors, several considerations can help validate vendors with appropriate compliance and governance.
- Vendor reviews and ratings. Understand compliance and governance issues related to third-party AI providers and work with prospective AI vendors to address them. Reconsider AI vendors that cannot directly address compliance and governance issues. Perform regular vendor reviews to ensure providers continue to meet evolving governance and compliance requirements.
- Contractual Agreement. AI providers typically require a contract or legally binding agreement. Although providers typically provide boilerplate terms and conditions documents, business clients can often pursue a contractual relationship that clearly sets out the understanding and requirements expected of the business client. For example, such agreements may specifically restrict the retention and use of data collected from the business.
- Data Usage Restrictions. AI providers typically receive and process data from client users. This ranges from LLM prompts for search queries to massive data access for analysis and decision-making. Considering that business data is often sensitive or domain-specific, no business wants to compromise sensitive information through the use of AI. Pay close attention to data usage restrictions in your AI provider’s terms of service and contractual agreements.
- AI uses monitoring and logging. AI compliance and governance includes both external and internal oversight. External monitoring includes tools to monitor data sent to and received from third-party AI providers to ensure that AI services are used as intended and function as expected. Internal monitoring typically includes AI inventory management to ensure only approved AI services are used, thereby identifying and preventing shadow AI across the business.
Approach to AI integration
Methodology determines how a company accesses external service providers and the nature of their interactions. There are several important approaches that affect access and security.
- API. APIs are the most common means of integration. For example, cloud-based AI models such as OpenAI and Google Cloud use APIs to support real-time data exchange. APIs are treated as middleware and managed as a separate software platform. For example, APIs are versioned and updated.
- rug. Search Augmentation and Generation (RAG) systems connect external models, such as LLM, to internal data stores. AI models that combine training with local content can typically provide more accurate and relevant responses while reducing the risk of hallucinations. However, accessing local data stores can increase security and regulatory risks for your business.
- AI agent. Agentic AI relies on external autonomous agents to handle complex, on-demand tasks with little human oversight. Because agents can access data and make decisions, careful security and close monitoring are required to ensure that the agent’s AI actions deliver the intended results. Important decisions may require human approval.
- Local integration. Some third-party AI components can be deployed locally to work within a company’s infrastructure. Although these components are available from third parties, they can be deployed locally to support private AI computing with low latency and superior security. This is the preferred approach for sensitive or highly regulated industries.
TechTarget’s Senior Technology Editor, Stephen J. Bigelow, has more than 30 years of technical writing experience in the PC and technology industries.
