Artificial intelligence has revolutionized the way businesses operate, enhancing productivity, automating mundane tasks, and driving data-driven decisions. However, as AI’s role in the business world grows, so does its potential to introduce risks—such as data security breaches, compliance violations, and unintended biases.

These risks can severely affect an organization’s reputation and operations if not addressed properly. To harness AI’s benefits without jeopardizing your business, it’s essential to adopt a proactive approach to AI safety. Rather than simply focusing on AI’s capabilities, it’s equally important to prioritize its safe use.

Read on to discover six practical ways you can make AI safer for your business and ensure it serves you reliably and responsibly.

1. Work With Vendors Who Prioritize AI Security

If your business uses third-party AI tools, vendor choice can shape your entire risk profile. Some providers cut corners. Others build security into every layer of their product. Knowing which is which makes a difference.

To protect your data and reduce potential exposure, the following are key areas to focus on when evaluating AI vendors:

• Ask about data storage and access control: It’s crucial to understand where your data is being stored and who can access it. The vendor should implement robust authentication protocols to restrict unauthorized access. Additionally, ensure they have clearly defined roles and permissions for their employees, limiting access to sensitive data based on necessity. Ask for detailed policies on how they manage and monitor access to your data to ensure its security.
• Check their encryption standards: Make sure the vendor employs end-to-end encryption, ensuring that your data is protected during both transmission and storage. Encryption should be robust, using up-to-date encryption algorithms that comply with industry standards. Don’t accept vague assurances—request specific details about the encryption methods they use, including key management practices, to guarantee your data is securely encrypted at every stage.
• Review their data retention practices: Clarify how long the vendor will retain your data and under what conditions it will be stored. They should be transparent about their data retention policies and offer clear timelines. Make sure they don’t use your business’s data to improve or refine their models without your consent. Also, ask about their procedures for securely deleting data once it is no longer needed.

Working with vendors who offer AI red teaming—simulated attacks by security professionals—adds an extra layer of confidence. It shows they’re actively looking for weaknesses before someone else does.

2. Set Clear Internal AI Use Policies

Even with protective measures in place, employees may unknowingly misuse AI tools. To reduce the risks of mishandling, it’s important to establish clear guidelines for internal AI use.

These policies should clearly define what’s acceptable and what isn’t. Here are some key points to consider:

• Define what AI can and can’t be used for: Be explicit about which AI tools are appropriate for different tasks. Make sure employees understand the boundaries of acceptable use, whether it’s for customer service, analysis, or content creation.
• Include real-world examples: Demonstrating proper and improper use of AI tools through real-world scenarios can help employees grasp the limitations. For example, showing examples of when AI can assist in automating repetitive tasks versus when human oversight is required ensures clarity.
• Train your team: Simply having a policy is not enough; it’s vital to actively train your team. Offer regular workshops or brief tutorials on the best practices for using AI tools responsibly. Equip your staff with the knowledge to use these systems safely and effectively.
• Monitor AI interactions: Establish a process for reviewing and auditing AI interactions. Periodic checks can help identify any misuse or gaps in the understanding of internal guidelines. This can be an opportunity to refine the policy and offer additional training if necessary.

A clear policy not only minimizes misuse but also helps prevent accidental breaches of critical data. As a final note, avoid feeding any sensitive data or proprietary information into AI tools. Once the data is entered, it could potentially be used to train the model or be retained by the platform, creating risks that are outside your control.

3. Limit Access to AI Tools Within Your Team

Managing access to AI tools within your team is critical for safeguarding both your data and AI models. If too many employees have unrestricted access to AI systems, sensitive data could be mishandled, intentionally or unintentionally. Limiting access helps ensure that AI tools are used safely and appropriately.

Here are the best practices for controlling AI access within your organization:

• Use role-based permissions: Restrict access to AI tools based on job relevance. Only grant admin-level access to employees who truly need it for their responsibilities. This helps limit potential risks from those without adequate understanding or training in AI systems.
• Restrict input capabilities: Ensure that employees can’t input sensitive or proprietary data into AI platforms unless absolutely necessary. If input is not needed for their work, limit this capability entirely to reduce the risk of accidental exposure or misuse.
• Audit AI usage regularly: Regular audits of who is using AI tools, and how they are being used, help identify potential issues before they escalate. Keeping a close eye on usage patterns enables the team to spot misuse, whether intentional or not, and take corrective actions promptly.

By applying these practices, you ensure that access to AI tools is controlled and monitored, minimizing the chances of data breaches or unintentional misuse of AI capabilities. Keeping access tightly restricted fosters a safer and more secure AI environment for your business.

4. Keep AI Systems Transparent and Explainable

AI can be a powerful asset, but it’s important to trust how the systems make decisions. Without clarity on how models work, businesses risk relying on outputs that may be biased, flawed, or hard to understand. Ensuring AI systems are transparent and explainable not only improves trust but also enhances their reliability and accountability.

Below are ways to achieve this:

• Choose explainable AI models: Opt for AI systems that can provide clear reasoning or explanations for their decisions. Knowing the “why” behind an AI’s output is vital, especially for critical tasks or compliance requirements. This approach helps ensure that decisions made by AI are understandable to non-experts.
• Validate outputs manually: Even the most sophisticated AI models can make mistakes or produce unintended results. Always have human oversight to validate the decisions or recommendations made by AI, especially in high-risk areas like financial analysis or healthcare diagnostics.
• Document model behavior: Keep a detailed record of how your AI behaves in various scenarios. This documentation should track how the model processes different inputs and the resulting outputs, which can help you detect any recurring errors or biases early on.

Maintaining transparency in AI systems isn’t just about making the models more understandable—it’s about building trust and ensuring that AI contributes to informed, accurate decisions.

5. Update and Monitor AI Systems Continuously

AI systems require ongoing attention to ensure they remain effective and secure. As AI technology evolves, so do the data and variables it interacts with. Regular updates and monitoring are vital for maintaining accuracy, security, and compliance with company policies.

Below are the essential practices to keep your AI systems in check:

• Patch systems regularly: Ensure that updates and security patches are applied as soon as they are available. This helps protect against vulnerabilities and ensures that the system operates smoothly.
• Track performance metrics: Measure the output accuracy, response time, and user feedback regularly. Monitoring these metrics helps identify early signs of error or performance issues.
• Monitor for bias or policy violations: Use tools to identify any discriminatory patterns or deviations from your intended usage guidelines. Keeping bias in check is crucial to ensure fairness in AI-generated outcomes.

AI systems can drift over time, which is why continuous monitoring and updates are essential. Staying proactive with these practices will help ensure your AI remains a reliable and secure asset for your business.

6. Conduct Regular AI Security Audits

Regular AI security audits are critical for identifying and addressing vulnerabilities in your AI systems before they can be exploited. These audits assess the effectiveness of your AI security protocols and provide insights into areas that may need improvement.

Here’s how to perform an effective AI security audit:

• Assess AI risk areas: Start by identifying which parts of your AI systems pose the greatest security risks. This could include models that handle sensitive customer data, financial transactions, or proprietary business information.
• Evaluate compliance with security standards: Ensure that your AI tools meet industry security standards and legal compliance requirements. Check for compliance with regulations such as GDPR, HIPAA, or any other relevant legislation that may apply to your business and its data processing activities.
• Conduct penetration testing: Use penetration testing to simulate potential cyberattacks on your AI systems. This helps uncover weaknesses in the system that might be exploited by malicious actors. It’s also an opportunity to test your defenses and response capabilities.
• Audit for data integrity: Verify that the data fed into your AI models is clean, accurate, and free from manipulation. Poor data integrity can lead to unreliable AI outputs that compromise your business operations.

Regular audits help ensure your AI systems remain resilient to emerging threats and continue to operate securely and efficiently.

Final Thoughts

By taking a proactive approach to AI safety, businesses can unlock the full potential of these advanced technologies while minimizing risks. Prioritizing security, transparency, and continuous monitoring ensures that AI tools remain reliable, effective, and aligned with your business objectives. As AI evolves, staying vigilant helps protect your data, secure your operations, and promote responsible use across your organization.