Bringing AI agents into your workforce requires a certain level of trust, similar to the trust required in human employees. The “trust but verify” approach to evaluating AI vendors can help enable a business while still remaining secure.

Trusting AI Agents Like Human Employees

The shift to a digital workforce through the use of AI agents elicits the question “can we trust them the same way we trust human employees?”. The answer is yes—but just like with human hires, trust should be earned through continuous verification. Employers conduct background checks, enforce security policies, and establish governance frameworks for building employee trust. The same due diligence should be applied to AI agents.

At Vivun, we’ve built a rigorous security framework for evaluating AI tools, ensuring they meet enterprise-grade security standards. In this article, we’ll explore key principles for trusting AI agents as digital workers, with a focus on data security, compliance, and risk mitigation.

Security Risks in AI Agents

Just like any new piece of technology, certain new risks will arise with the onboarding of AI technology. In order for an organization to properly protect their data when introducing new AI technologies, AI-specific risks need to be defined and understood.

1. Data exposure due to model training

Unlike traditional SaaS solutions, many AI tools use customer inputs to refine and improve their models. This means that sensitive business data, intellectual property, or proprietary insights could inadvertently become part of a broader AI system, accessible by other users or even incorporated into public-facing outputs.

2. Obscure data provenance

With traditional enterprise software, companies have a clear understanding of where their data resides and how it is processed. AI, however, introduces a more complex data flow. AI agents process inputs, generate responses, and often reference external data sources, making it challenging to track how information moves through these systems.

The lack of transparency around AI data processing poses a significant security risk. If organizations don’t have visibility into how AI agents handle their data, they cannot adequately assess compliance risks, data residency concerns, or potential exposure to third parties. To mitigate this, businesses should demand full transparency from AI vendors, including detailed documentation on data handling, processing logic, and output generation. 

3. AI-specific attacks

AI introduces new attack vectors that traditional security frameworks may not fully address. One major risk is data injection attacks, where malicious inputs are designed to manipulate AI models. Attackers can introduce misleading or harmful data, causing an AI system to generate biased, incorrect, or even dangerous outputs.

4. Hidden cost of free AI tools

Many AI tools offer free versions, but if a product is free, your data is the real price. Free AI services often collect and use input data for model training, potentially exposing sensitive business information. Unlike paid enterprise-grade AI, which typically includes contractual data protection measures, free AI tools may have ambiguous policies particularly around data ownership.

Organizations using free AI tools for business-critical tasks may unknowingly grant AI vendors access to proprietary insights. While free AI tools can be useful for experimentation, they should not be used for handling sensitive company data. Instead, businesses should prioritize AI solutions with clear contractual agreements that guarantee data protection, ensuring they retain full control over their intellectual property.

AI certainly introduces new security challenges, but by making adaptations to time-tested security parameters, organizations can still protect against those risks. 

New Technology, Adaptable Security Parameters

While AI agents feel revolutionary, security principles remain the same. When thinking about data security, three fundamental questions should always be evaluated:  

• Where is my data?
• Who has access to it?
• How is it protected?

The following framework can help evaluate and mitigate risk when leveraging AI technologies.

1. Data Ownership and Protection

Organizations must ensure their data remains exclusively theirs. If a vendor does not explicitly guarantee that customer data will not be used to train AI models, it’s a major red flag. Without this assurance, sensitive business insights could be inadvertently absorbed into an AI model, potentially exposing proprietary information to other users. Companies should prioritize vendors that offer strict data protection policies and contractual safeguards against unauthorized data usage.‍

2. Transparency and Explainability

A lack of clear documentation on how an AI system processes data should be a dealbreaker. If a vendor cannot articulate its security controls, data governance policies, or compliance measures, there is no way to verify how data is being handled. Companies should require AI vendors to provide full transparency on their data processing pipelines, including whether AI models use customer inputs to refine their capabilities.

3. Vendor Security Posture

Free AI tools often come with unclear data protection policies, making them risky for enterprise use. However, even paid AI tools must be carefully evaluated for their security posture. Organizations should verify whether AI vendors conduct regular security audits, implement encryption protocols, and maintain clear governance frameworks. Choosing a vendor with a strong security posture ensures that AI solutions enhance business operations without introducing unnecessary risk.

4. Ethical AI Use

As AI-generated content becomes more sophisticated, the risk of deceptive AI use—such as deepfakes—continues to grow. Organizations must prioritize vendors that commit to ethical AI development, providing transparency around AI-generated outputs. If a company is not forthcoming about how its AI models create and source information, businesses should assume that trust and security are not priorities.

How Vivun Ensures Trustworthy AI

At Vivun, we apply those security parameters in everything we do. Our AI-powered sales solutions are designed with:

• Enterprise-grade security – Built-in compliance with SOC 2 Type 2 and ISO 27001 standards.
• Data protection guarantees – Customer data is never used to train third party models.
• Proactive compliance – We stay on top of AI regulations, ensuring our solutions align with evolving legal frameworks.

This approach empowers sales and security leaders to confidently integrate digital workers without compromising security or compliance.

By applying a trust but verify approach, organizations can confidently deploy AI agents as digital workers, ensuring they enhance business efficiency without introducing unnecessary risks.

Want to stay ahead of the evolving AI threat and regulatory landscape? Follow Vivun on LinkedIn and subscribe to our podcast, The Unexpected Lever, for expert insights on AI in Sales.

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