- AI vendor selection is critical for enterprises - the wrong vendor can expose businesses to regulatory fines, operational risks, and reputational harm.
- Red Flag #1: Missing,weak or boilerplate policies and documentation - vendors without comprehensive policies like privacy policies, DPAs, and cybersecurity policies signal a lack of preparedness and accountability.
- Red Flag #2: Limited transparency in AI system’s data flows (subprocessors) and training data - vendors unable to explain their subprocessors, data sources, lineage, and quality pose compliance, ethical, and operational risks.
- Red Flag #3: Handling compliance manually and lack of third-party ongoing compliance monitoring - vendors relying on manual and unstructured compliance processes signal low prioritization of security and trust.
- TrustPath eliminates these red flags by providing automated compliance checks, dataset transparency validation, and a centralized framework for evaluating AI vendors.
- With TrustPath, enterprises can confidently adopt AI, knowing their vendor partnerships are secure, compliant, and aligned with their standards.
Artificial intelligence is no longer a luxury for enterprises—it’s a necessity to stay competitive in today’s market. But adopting AI comes with risks, especially when partnering with vendors who prioritize speed over compliance, transparency, and trust. As discussed in our previous article, The High-Stakes Gamble of Non-Compliant AI Vendors, enterprises face severe consequences when they work with unprepared or unreliable AI providers.
The risks are far-reaching: regulatory fines, operational disruptions, financial losses, and reputational damage. For enterprises, these risks often originate from hidden red flags that are easy to overlook during the vendor selection process. These red flags not only slow down AI adoption but also expose businesses to long-term vulnerabilities.
This article will explore the top red flags enterprises should watch for when evaluating AI vendors. From missing policies to a lack of transparency around training data, we’ll highlight the warning signs that signal risk. And, more importantly, we’ll show how we can help enterprises eliminate these risks, ensuring secure and compliant AI adoption.
Let’s dive into the red flags every enterprise business needs to know.
Red Flag #1: Missing or Weak Policies and Documentation
Implemented and working policies and documentation are the backbone of trust and accountability for any AI vendor. Yet, many vendors either lack these entirely or provide boilerplate or incomplete versions that fail to meet enterprise needs. For businesses, this is the first and most obvious red flag.
What should you look for?
When evaluating an AI vendor, ensure they have clear, up-to-date policies that outline how they handle compliance, security, and risk management in a way that is adjusted to their particular situation. Missing template language (boilerplate) clauses or weak documentation indicates a lack of preparedness a potential lack of implementation, and can expose your business to unnecessary risks. Key policies to request include:
- Privacy policy - explains how the vendor collects, processes, and protects customer data.
- Terms of use - defines the rules and conditions for using the vendor’s AI solutions.
- Data processing agreement (DPA) - outlines the vendor’s responsibilities in processing and safeguarding data.
- SOC 2 and ISO 27001 related cybersecurity policies - describe how vendor complies with cybersecurity standards and how implement appropriate safeguards.
- EU AI Act Assessment - vendor should be able to concisely evaluate its stance regarding the AI Act’s applicability and state whether and to what extent this law applies.
- AI Governance Policy - this document should govern internally how AI systems are designed, trained ,and deployed and assess whether they pose a risk, and how to mitigate risk if needed.
- AI Training & Copyright Policy - procedures should be in place to comply with applicable IP law, including Copyright Directive 2019/790.
Why does this matter?
Missing policies or poorly defined and implemented documentation are clear indicators of risk. For example:
- Without strong cybersecurity policies and program,, a vendor may not know how to protect your data and handle a potential breach, leaving your enterprise exposed.
- A lack of a Data Processing Agreement could result in improper handling of sensitive customer data, such as through subprocessor mismanagement, putting you in violation of regulations like GDPR.
Red Flag #2: Lack of transparency in AI system’s data flows (subprocessors)model datasets and training data
Transparency is very important when it comes to AI adoption. Enterprises need to understand how a vendor’s AI model was trained, including the origin and quality of the datasets used, especially when AI systems pose a significant risk. Yet, many vendors fail to provide clear or sufficient information about their training data, leaving enterprises in the dark.
What should you look for?
When evaluating vendors, ask for detailed insights into their datasets, training processes, and model cards. Key elements to verify include:
- Dataset lineage - where does the data come from? Is it sourced ethically and in compliance with privacy laws (e.g., GDPR)?
- Data quality and cleaning processes - how does the vendor ensure the data is accurate, relevant, and free from bias (in case bias is a relevant risk)?
- Permission and licensing - does the vendor have legal rights to use the data for training their AI models?
- Training data updates - how often is the training data refreshed to reflect changing conditions, and is this process documented?
- Overfitting - can the AI system output 1:1 copyrightable material?
Why does this matter?
A lack of transparency in datasets introduces major risks:
- Bias and ethical risks - hidden biases in training data can lead to discriminatory outcomes, damaging your brand’s reputation and exposing you to legal action.
- Compliance risks - vendors using unauthorized or non-compliant data can leave your enterprise vulnerable to lawsuits.
- Operational risks - poor data quality can result in inaccurate or unreliable outputs, reducing the effectiveness of your AI adoption.
For example, imagine deploying an AI-powered hiring solution only to discover it was trained on biased datasets. The fallout could range from potential hires, or employee and contractor backlash to costly lawsuits.
As we can see, accountability and transparency are another crucial factor in selecting AI vendors, but it’s only part of the picture. How vendors handle compliance processes is another red flag enterprises need to watch for. Let’s explore why manual compliance processes can slow down innovation and increase risk.
Red Flag #3: Handling compliance manually and lack of third-party ongoing compliance monitoring
Well-monitored and up-to-date compliance should be the cornerstone of any enterprise AI adoption strategy. Yet, many vendors still rely on manual compliance processes that are slow, prone to errors, or operationally insufficient.
What should you look for?
When evaluating AI vendors, look for evidence of trustworthy, third-party provided automated compliance systems, that monitor compliance on an ongoing basis. Key indicators of manual processes include:
- Outdated, template/boilerplate or inconsistent compliance documentation - AI vendors who can’t have such type of documents likely don’t have them implemented, especially at technical level.
- No automated compliance monitoring - vendors without systems to track real-time controls monitoring.
- Lack of regulatory awareness- if a vendor can’t adapt to new laws quickly, it puts your enterprise at risk of non-compliance.
- Lack of compliance dashboards or transparency tools - enterprises should have visibility into a vendor’s compliance status at all times.
Why does this matter?
Manual and insufficient compliance processes introduce significant risks:
- Delayed adaptation - vendors relying on manual checks are often not really prepared for regulatory requirements, exposing your enterprise to operational delays, and manifold other risks
- Error-prone systems - manual workflows increase the likelihood of compliance gaps, creating vulnerabilities in your AI ecosystem.
- Operational inefficiencies - enterprises relying on such vendors may face disruptions as such vendors maybe more prone to cybersecurity breaches.
For instance, imagine working with a vendor that doesn’t use trusted third=party compliance tool. It is hard to assess whether they comply at all.
These red flags highlight the hidden risks enterprises face during vendor selection. The good news is that TrustPath eliminates these risks, allowing you to adopt AI confidently. Let’s explore how TrustPath simplifies vendor evaluation and protects your business.
How TrustPath Helps You Eliminate Red Flags
Navigating the complexities of AI vendor selection can be daunting, but TrustPath streamlines this process, ensuring your enterprise partners with vendors who meet the highest standards of compliance, transparency, and trust.
Comprehensive Vendor Profiles
TrustPath provides in-depth AI vendor profiles, granting instant access to critical information such as:
- Vendor policies
- Data privacy and security measures
- AI models and datasets utilized
- Exclusive product insights
This transparency empowers your procurement and compliance teams to make informed decisions efficiently.
AI Vendor Assessment Framework
Our AI Vendor Assessment Framework offers a pre-defined set of questions and tests, allowing your teams to easily assess and compare AI vendors on aspects like data privacy, model transparency, and AI regulatory compliance.
Risk Mitigation and Security Assurance
TrustPath enables you to:
- Access comprehensive existing vendor’s documentation and policies
- Check for compliance with specific AI Act’s requirements
- Properly evaluate vendor’s AI-related risk
This ensures that your chosen AI solutions are robust, resilient, and reliable under any circumstances.
Enhanced Data Security Confidence
Data security is paramount. TrustPath offers thorough insights into each vendor’s AI compliance with key regulations, such as GDPR, privacy policies, and data processing agreements. This level of transparency allows you to confidently engage with vendors, knowing that your data will be handled securely, minimizing risks and maintaining stakeholder trust.
Accelerated Procurement Process
By consolidating all critical information into a centralized database, TrustPath reduces complexity, saves valuable time, and accelerates the procurement process, ensuring you stay ahead in a competitive market.
By addressing these red flags proactively with TrustPath, your enterprise can confidently adopt AI solutions that are secure, compliant, and aligned with your organizational standards.