Questions to Ask AI Vendors About Security

published on 28 July 2025

Choosing the right AI vendor can protect your business from costly security risks. With data breaches averaging $4.45 million in 2023 and AI-driven vulnerabilities on the rise, evaluating vendors is critical. SMEs must focus on three key areas: company security standards, data protection practices, and AI bias management.

To ensure your vendor prioritizes security, ask questions like:

  • What certifications (e.g., SOC 2, ISO 27001) do you hold?
  • How do you handle sensitive data and prevent breaches?
  • What measures address AI bias in your systems?

Investing in AI security saves money, resolves breaches faster, and builds customer trust. Regular audits, clear contracts, and continuous monitoring ensure your business stays secure and compliant.

VRM 201: Effectively Assessing Vendor AI Risk - Chris Honda

Key Security Areas to Evaluate with AI Vendors

For small and medium-sized enterprises (SMEs), ensuring the security practices of AI vendors is non-negotiable. Evaluating vendors across three key security areas provides a structured approach to assess their commitment to safeguarding your business.

Company Structure and Security Standards

The way a vendor is organized and adheres to established security standards says a lot about their dedication to protecting data. Security certifications act as independent proof that a vendor’s platform meets critical requirements. These certifications validate the risk management practices that were highlighted earlier.

When evaluating vendors, look for certifications like SOC 2 Type II, ISO 27001, and CSA STAR. These certifications indicate that the vendor has passed rigorous third-party audits and maintains strong, ongoing security measures. Interestingly, there's an 80% overlap between ISO 27001 and SOC 2 criteria, so vendors holding both demonstrate a robust commitment to security.

Other compliance standards to consider include SOC 2, GDPR, HIPAA, PCI DSS, CIS, CCPA, CSA STAR, and NIST. The right certifications depend on your industry and location. For instance, healthcare organizations must prioritize HIPAA compliance, while businesses handling credit card transactions should focus on PCI DSS.

"The cost of non-compliance is great. If you think compliance is expensive, try non-compliance." – Former U.S. Deputy Attorney General Paul McNulty.

Failing to comply with regulations can lead to lawsuits, lost business opportunities, and diminished trust. With 93% of companies admitting they fall short on compliance with data privacy regulations, it’s vital to work with vendors that take compliance seriously.

Data Privacy and Protection

Protecting data goes beyond certifications - it’s about how vendors handle sensitive information. This is crucial for SMEs, especially since 58% of data breaches in 2020 involved personal data. Understanding how vendors manage data collection, storage, processing, and transfer directly impacts your regulatory standing and customer trust.

Insider threats are a growing concern, with a 47% increase since 2018, costing businesses an average of $200,000 annually. To mitigate this, prioritize vendors that enforce strict access controls, such as least privilege access, to limit unnecessary exposure to sensitive data.

Ensure vendors use industry-standard encryption like AES-256 for storage and TLS 1.3 for data transmission, along with immutable audit logs to maintain accountability. For businesses in the U.S., compliance with regulations like CCPA is critical, especially given that 34% of data breaches involve internal actors. Vendors should also have clear policies for data retention, deletion, and cross-border transfers.

"Organizations should take preventative measures to protect personal data they maintain (including data processed by their vendors)." – Jackson Lewis P.C..

The stakes are high. In 2024 alone, over 1 billion data records were exposed, with the average data breach costing $4.9 million. Choosing vendors with strong data protection measures isn’t just about compliance - it’s about safeguarding your business’s financial health.

Bias Detection and Ethical AI

AI bias is an emerging challenge that can lead to discrimination lawsuits, regulatory penalties, and damage to your reputation. With 52% of consumers expressing concerns about AI-driven decisions, addressing bias is critical to maintaining trust.

The IEEE 7003-2024 standard for "Algorithmic Bias Considerations" offers a framework for tackling bias in AI systems. Vendors should be familiar with this standard and demonstrate how they address bias throughout the AI lifecycle.

Bias often arises from issues like unrepresentative training data, flawed decision criteria, inadequate monitoring, or model drift. Vendors should use techniques like adversarial debiasing, which trains AI models to ignore sensitive attributes such as race or gender. Additionally, Explainable AI (XAI) features can help you understand the reasoning behind AI decisions.

To stay ahead, vendors should maintain a "bias profile" that tracks their efforts to mitigate bias throughout the AI lifecycle. Regular bias audits should examine data sources, algorithm transparency, and bias detection across different demographic groups. Vendors should also prioritize diverse development teams and implement "human-in-the-loop" systems, allowing human oversight of AI-driven decisions.

As regulations evolve from voluntary guidelines to enforceable laws, particularly around algorithmic fairness, partnering with vendors who address bias proactively will help your business remain compliant and uphold ethical AI practices.

Key Questions to Ask AI Vendors

When evaluating potential AI vendors, it's crucial to ask the right questions. These inquiries will help you determine whether a vendor genuinely prioritizes security or just claims to. Based on the security areas discussed earlier, here are some essential questions to guide your evaluation.

Top 10 Security Questions

1. What specific security certifications and compliance standards does your AI solution adhere to?
This is a fundamental question that quickly identifies vendors who prioritize security. Look for certifications like SOC 2, ISO 27001, and GDPR compliance. Each standard highlights different aspects of security, so understanding them is key. For context, in 2024, over 1.7 billion individuals had their personal data compromised, with the average data breach costing $4.88 million. It’s worth noting that only 54% of companies pursuing ISO 27001 certification succeed on their first attempt.

"If you spend more on coffee than on IT security, you will be hacked. What's more, you deserve to be hacked."
– Richard Clarke, former U.S. Cybersecurity Advisor

2. How does your AI system ensure data privacy, and what measures prevent unauthorized access to sensitive business information?
Vendors should explain their privacy-by-design approach, data minimization techniques, and encryption methods. Transparency is vital, especially since only 27% of consumers feel they understand how companies use their data. AI systems can infer sensitive details - like political views or sexual orientation - with up to 80% accuracy from seemingly harmless data. Ask what advanced privacy measures they use to avoid such unintended inferences.

3. Can you provide details on how your AI solution detects, mitigates, and addresses biases?
Bias in AI can lead to ethical and financial consequences. Vendors should outline their methods for testing and mitigating bias, such as stratified sampling, specialized detection tools, and fairness audits. For example, fairness audits have been shown to improve accuracy for underrepresented groups.

"If your data isn't diverse, your AI won't be either."
– Fei-Fei Li, Co-Director of Stanford's Human-Centered AI Institute

4. What is your incident response plan for AI-related security breaches?
Given that security incidents are inevitable, vendors must have a clear incident response plan. This plan should include detection, notification, and remediation processes. With 89.4% of IT leaders expressing concerns about AI-related security risks, a robust response strategy is non-negotiable.

5. How do you handle data used for AI training and inference?
Understanding how a vendor manages data is critical. Ask about their policies on data retention, deletion, and international transfers. This is especially important because data collected for one purpose is often repurposed for broader model training, potentially violating privacy principles.

6. What access controls and authentication measures protect your AI systems?
Strong access controls are vital for preventing unauthorized access. Look for measures like multi-factor authentication, strict user permissions, and continuous monitoring.

7. How do you ensure transparency and explainability in your AI decisions?
AI systems shouldn’t operate as mysterious black boxes. Vendors should provide Explainable AI (XAI) features that clarify how decisions are made. This is important for regulatory compliance and building trust with stakeholders.

8. What regular security assessments and audits do you perform?
Ongoing security requires constant evaluation. Vendors should share their schedules for penetration testing, vulnerability assessments, and third-party audits. Regular AI privacy impact assessments are also essential to ensure compliance and identify new risks.

9. How do you protect against adversarial attacks on your AI models?
AI models face unique threats from adversarial attacks designed to manipulate outputs. Vendors should explain their strategies for safeguarding model integrity against such manipulations.

10. What documentation and reporting do you provide for security and compliance purposes?
Detailed documentation is essential for compliance and regulatory reviews. Vendors should provide security reports, audit logs, and compliance attestations to support your oversight efforts.

These questions are the backbone of a thorough vendor evaluation process. Vendors who provide clear, detailed answers backed by certifications and tangible examples demonstrate their commitment to security. On the other hand, vague or evasive responses should raise concerns. By asking these critical questions, you’ll be better equipped to identify vendors that can safeguard your business effectively. Up next, we’ll explore how these queries tie into broader security audits and risk management practices.

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Best Practices for Security Audits and Risk Management

To stay ahead in the ever-changing AI landscape, it's crucial to implement thorough security audits and maintain continuous risk management practices. Once you've chosen an AI vendor, the work doesn't stop there - regular audits and ongoing monitoring are essential. Here's why: 60% of enterprises use GenAI tools without formal governance or audit processes. On the flip side, companies with structured AI risk frameworks report 35% fewer AI-related incidents, while those conducting detailed vendor assessments see a 40% reduction in AI-related risks.

Continuous Security Monitoring

Periodic assessments just don't cut it anymore. AI evolves too quickly, and threats can emerge in real time. That's where continuous security monitoring comes in. Unlike traditional reviews, this approach offers up-to-the-minute insights into your vendors' cybersecurity posture, keeping you one step ahead.

Start by mapping out where third-party AI tools are already integrated into your supply chain. Many businesses discover untracked AI tools introduced by various departments. Build a detailed inventory that includes vendor names, security measures, compliance statuses, and risk evaluations.

Next, assign a risk level to each tool based on factors like data access, automation authority, and the sensitivity of its use case. For instance, tools handling sensitive customer data or making automated decisions require closer scrutiny than basic productivity apps. This prioritization ensures your monitoring efforts are focused where they matter most.

Key elements of continuous monitoring include automated risk scans, real-time intelligence feeds, breach detection, AI-driven risk scoring, and compliance tracking. These tools help identify security incidents, regulatory changes, and AI model updates that could impact your operations.

Also, track metrics like drift, performance degradation, and fairness issues. AI models can shift over time, sometimes in ways that undermine their effectiveness or fairness. Regularly reviewing vendor update logs can reveal how often models are modified and whether those changes align with your risk tolerance.

Today, 57% of organizations use specialized third-party risk management software to automate these processes. These platforms continuously monitor vendor risk profiles and issue alerts when red flags arise, making them a valuable part of your security toolkit.

Adding Security Checks to Vendor Management

Security checks shouldn't be an afterthought - they need to be baked into every stage of vendor management. With 87% of organizations expressing deep concerns about AI-specific risks in vendor relationships, it's clear that this is a priority.

Start by embedding security checks into the vendor onboarding process. Tailored due diligence checklists can help evaluate a vendor's security practices, financial stability, and compliance with regulatory standards. Incorporating frameworks like FAIR, NIST, ISO 27001, or SOC 2 ensures your assessments meet industry benchmarks.

AI vendors also require contracts that address their unique risks. Include AI-specific clauses in vendor agreements, such as requirements for model explainability, audit rights, restrictions on data reuse, and escalation plans for handling model errors.

Here are some key components to include in your AI security audits:

AI Security Audit Component Description
Map data flows Trace every step of the AI process, from the initial prompt to the generated output.
Verify sensitivity labels Ensure sensitivity tags match how the AI uses content in context.
Test prompts Use sequences designed to challenge the system's safeguards against oversharing or prompt injection.
Review monitoring coverage Confirm you have visibility into when and how AI systems respond to inputs.
Confirm explainability logs Require evidence showing how AI decisions link back to prompts, policies, and data sources.

To maintain accountability, assign a dedicated model owner for each critical AI service. This person will oversee the tool's performance, understand its functionality, and act as the go-to contact if issues arise.

Additionally, schedule quarterly vendor oversight meetings with teams from compliance, IT, and procurement. These sessions provide a chance to review risks, discuss vendor performance, and address any concerns. Regular communication ensures everyone stays aligned on vendor-related priorities.

Remember, using third-party AI tools doesn't absolve your organization of responsibility. As highlighted in the FCA Guidance Note 2024, boards can't assume that outsourcing limits their accountability.

Finally, build vendor-specific response protocols into your incident management plan. If a vendor experiences a security breach or model failure, you need clear steps for assessing the situation, communicating with stakeholders, and mitigating damage. These protocols should include escalation paths, pre-drafted communication templates, and decision trees for various scenarios.

Don't forget to require vendors to disclose their subcontractors and evaluate how they impact your risk management program. Many AI vendors rely on cloud providers and other third parties, so understanding these relationships is vital for assessing your full risk exposure.

Ongoing communication is also key. Regularly update vendors on your latest security policies, compliance expectations, and risk assessment procedures. This keeps them informed and ensures they're aligned with your standards.

The ultimate goal? Treat vendor management as a dynamic, ongoing process. Form a dedicated VRM committee to oversee strategy and ensure your practices evolve alongside your business and the AI landscape. This proactive approach can help you stay prepared for whatever challenges lie ahead.

Conclusion

Choosing the right AI vendor involves more than comparing features and pricing - security should always take center stage. With cybercriminals constantly evolving their tactics, small and medium-sized enterprises (SMEs) are particularly vulnerable due to their limited resources. Conducting thorough security evaluations can be the difference between thriving as a business and suffering significant setbacks.

The financial impact of an average data breach highlights why neglecting security evaluations is a costly mistake. Beyond monetary losses, businesses risk reputational harm and even closure. As investment in generative AI grows, adopting a security-first mindset when selecting vendors becomes even more critical.

Prioritizing security doesn’t just mitigate risks - it creates opportunities. SMEs that focus on rigorous security assessments during vendor selection can gain long-term advantages, such as improved operational efficiency, a stronger competitive position, and better protection for both the organization and its customers.

"Investing in cybersecurity is not just a technical necessity; it's a form of risk management that can ensure the longevity and trustworthiness of a business in the digital economy. Allocating funds for AI security is akin to investing in your company's future - a strategic move that goes beyond protection, enhancing operational efficiency and ultimately bolstering your competitive edge." - Stephen McClelland, ProfileTree's Digital Strategist

For SMEs navigating the complexities of vendor selection, platforms like AI for Businesses offer valuable support. These tools simplify the process by helping businesses align their goals, define use cases, and evaluate essential security features. This is particularly helpful considering that 55% of small businesses cite financial constraints as a barrier to adopting AI tools.

To make informed decisions, start with a checklist of must-have features, conduct pilot tests to verify capabilities, and prioritize vendors offering enterprise-grade security, compliance, and scalability. Security isn’t just a safeguard - it’s a critical foundation. In fact, 80% of cybersecurity professionals agree that AI’s benefits outweigh its risks when security is prioritized from the outset.

FAQs

How do I confirm that an AI vendor's certifications are valid and relevant to my industry?

When evaluating an AI vendor's certifications, it's crucial to ensure they're issued by reputable accreditation bodies or respected industry organizations. You can usually verify their authenticity by visiting the official website of the issuing organization or checking trusted online databases.

Equally important is confirming that these certifications are both up-to-date and relevant to your specific industry and the vendor's services. For example, certifications such as ISO standards are widely acknowledged and often applicable across multiple industries. Focus on certifications that demonstrate adherence to established security and operational standards.

What should I do if an AI vendor's system suffers a data breach that impacts my business?

If an AI vendor's system is hit by a data breach that impacts your business, it's crucial to act immediately to limit the fallout. Start by containing the breach - coordinate with the vendor to block any unauthorized access and secure your sensitive information. Once that's under control, evaluate the full extent of the breach. Determine what data was exposed and consider the possible consequences for your operations and your customers.

You’ll also need to inform law enforcement and notify any affected individuals or organizations, such as customers or business partners, based on legal requirements. Make sure to meet all legal and regulatory obligations, including any mandatory reporting. Lastly, use this as an opportunity to bolster your security measures. This might involve updating internal policies, renegotiating vendor contracts to include stricter security terms, or scheduling regular security audits. Acting quickly and thoughtfully not only minimizes damage but also helps preserve trust with your stakeholders.

How do AI vendors handle bias in their models, and what steps can I take to ensure fairness?

AI vendors address bias through a combination of advanced detection tools, algorithms designed with fairness in mind, and rigorous testing protocols. Many also emphasize the importance of diverse teams and datasets to reduce unintended biases during the development process.

As a business, you play a crucial role too. Start by supplying diverse and representative data, which helps create more balanced models. Ask for clear documentation detailing how the AI models are trained, and make it a habit to review their outputs regularly to ensure they align with your ethical and fairness standards. Working closely with your vendor is essential to uphold accountability and build trust in the AI systems you rely on.

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