Hidden AI Risks in Legacy MSAs – How to Quickly Address Them with Your Vendors (Yes, with AI)
By Erwann Couesbot in collaboration with The Procurement Compass
AI adoption by traditional IT vendors creates overlooked risks in legacy contracts. While companies thoroughly vet new AI vendor agreements, they often neglect existing Master Service Agreements that weren't designed for AI challenges. These outdated contracts lack essential protections now that your established IT vendors are integrating AI capabilities into their services.
The Evolving IT Vendor Landscape
The transformation is happening across the IT vendor spectrum. CRM platforms now offer AI-powered insights. Cloud storage providers implement AI for content analysis. Project management tools utilize AI for resource allocation and predictive timelines. Even basic office productivity suites now include AI assistants.
This evolution creates a critical problem: your existing MSAs likely have significant gaps when it comes to governing how these newly integrated AI capabilities interact with your organization's data, who owns the outputs, and what responsibilities each party has in this new paradigm.
Let's examine the most significant risks created by these overlooked gaps in legacy MSAs.
Risk #1: Proprietary Data Becoming Training Fodder
Perhaps the most significant risk in outdated MSAs is the lack of clear boundaries around how vendors can use your data to train their AI systems.
The Problem
Legacy MSAs typically include broad data usage rights that were written when data was primarily used for system operation, troubleshooting, and service improvements. Now these same clauses might inadvertently permit vendors to use your sensitive business data to train their AI models that serve other customers, including your competitors.
For example, a standard clause might state that a vendor can use your data to "improve their services." In the pre-AI era, this meant basic analytics to enhance functionality. Today, this same language could permit using your proprietary data as training material for generative AI systems that power services for all their customers.
The Consequences
Without specific AI data protection clauses, your proprietary information, customer data, financial metrics, and business strategies could become part of the AI's underlying knowledge base. This potentially exposes your competitive advantages and sensitive information to other users of the same AI system.
Even more concerning, once your data becomes part of an AI training corpus, it's virtually impossible to fully remove, creating permanent exposure beyond the term of your agreement.
Risk #2: Uncertain Ownership of AI-Generated Outputs
The second critical gap in legacy MSAs relates to the ownership of outputs created using your data.
The Problem
Traditional MSAs generally address intellectual property (IP) ownership for deliverables created directly by humans. However, they rarely contemplate scenarios where a system autonomously generates new content based on your data inputs.
When you use a vendor's AI feature, the system might generate reports, content, code, or insights derived from your proprietary information. Without specific AI IP clauses, it's unclear whether these outputs belong to you, the vendor, or exist in a legal gray area.
The Consequences
This ambiguity creates serious risks. Your vendor might claim ownership of valuable AI-generated insights derived from your data. They may incorporate these outputs into their products or share them with others. Without clear provisions, you might be inadvertently surrendering rights to potentially valuable intellectual property.
Furthermore, if the AI creates something problematic using your data (like biased analyses or content that infringes on third-party rights), legacy MSAs fail to establish liability frameworks for these novel scenarios.
Risk #3: Inadequate Security and Privacy Safeguards
Legacy MSAs typically lack AI-specific security and privacy provisions that address the unique risks associated with these technologies.
The Problem
AI systems often require different security approaches than traditional IT services. They may analyze data in new ways, use federated learning techniques, or create synthetic data based on your information. Traditional security clauses in MSAs typically don't account for these specialized scenarios.
Additionally, older agreements rarely address concepts like model inversion attacks, where malicious actors can potentially extract training data from AI models, potentially exposing your sensitive information even if the raw data is protected.
The Consequences
Without AI-specific security provisions, you lack contractual protections against these novel threats. Your data might be adequately protected in storage but vulnerable during AI processing or susceptible to extraction through inference attacks.
This creates compliance risks under regulations like GDPR, CCPA, and industry-specific frameworks that require appropriate technical safeguards for all data processing activities—including AI analysis.
Risk #4: Lack of Transparency and Control Over AI Processes
The fourth major gap in legacy MSAs is the absence of provisions ensuring visibility into how AI systems use your data and mechanisms to control these processes.
The Problem
Traditional IT service agreements rarely require vendors to provide transparency into algorithmic decision-making or give customers control over how AI features interact with their data. Without specific provisions, vendors aren't obligated to disclose:
- How AI models are trained using your information
- What data points influence AI decisions affecting your business
- Whether human review occurs before AI outputs are delivered
- What biases might exist in the underlying models
The Consequences
This lack of transparency prevents you from performing proper risk assessments, validating AI outputs, or ensuring alignment with your ethical standards and compliance requirements.
Without contractual rights to audit or control these processes, you effectively surrender oversight of how your data shapes and is shaped by increasingly influential AI systems that may directly impact your business decisions.
The Solution: Autonomously Modernizes Legacy MSAs with FlipThrough
Schedule a Live Demo & Analyze 2 MSAs Free
The complexity of addressing these risks across dozens or hundreds of existing agreements may seem overwhelming. This is where FlipThrough's automated agreement evaluation and management platform provides a transformative solution.
FlipThrough enables you to rapidly analyze your legacy MSAs to identify AI-related vulnerabilities and generate customized addendums that address each of the critical risks discussed above. Our platform:
- Scans your existing agreements to identify missing AI provisions related to data usage, ownership, security, and governance
- Generates customized AI addendums tailored to your specific industry requirements and risk profile
- Provides clear guidance on negotiation strategies for implementing these updates
- Ensures comprehensive coverage across your entire vendor ecosystem to eliminate blind spots
Rather than waiting for problems to emerge or undertaking a massive manual contract review, FlipThrough enables you to proactively address these risks at scale with minimal resource investment.