5 ways your AI solution may not be secure and how to fix it

You implemented AI to help your service teams, but did you accidentally expose internal confidential information to distributors ? Is your AI surfacing pricing data meant for sales to warranty teams?

Is your AI securing your knowledge or exposing it?

Take this quick AI security check to see if your AI solution is protecting your sensitive knowledge:

✅ Does your AI solution restrict access based on user roles? (Or can any employee access any response?)

✅ Does your AI segregate the knowledge it’s trained on? (Or does it pull from one massive knowledge base?)

✅ Do you have logs to track who accessed what AI-generated information?

✅ Can you control what different teams see when they interact with AI?

✅ Does your AI solution meet enterprise security and compliance standards?

👉 If you answered no to any of these or aren’t sure, your AI solution might be putting sensitive knowledge at risk.

Many AI solutions fail to provide enterprise-grade security or leave knowledge too open, too centralized, and too vulnerable. Service teams, sales reps, and support staff need different information, and one-size-fits-all access can lead to costly mistakes, compliance violations, or even data leaks. Here are five security pitfalls you should watch out for and how to fix them:

1. No role-based access control

The problem:
Not everyone in your organization should have access to the same information. AI-powered solutions that don’t enforce strict role-based access controls (RBAC) can accidentally surface information not meant for certain users.

For example, a field technician asks their AI solution for a troubleshooting guide. The AI provides the correct answer but also surfaces an internal sales document listing product pricing and dealer discounts. The technician wasn’t meant to see this information, but because the AI didn’t have RBAC, it provided an answer anyway.

Why this matters:

• A field technician shouldn’t see proprietary pricing structures.
• A dealer shouldn’t access back-office warranty processes.
• A customer service rep shouldn’t retrieve confidential supplier agreements.

The fix: Circuitry.ai uses RBAC to control who can access what information in an AI Advisor’s knowledge base. This ensures that knowledge is segmented based on roles, so different teams only see what they need.

2. Unsegregated knowledge bases

The problem:
Even if an AI solution has RBAC, it can still create security risks if it relies on a single, centralized knowledge base. Many AI systems rely on a single, centralized knowledge base. This means that a user asking a question could retrieve any stored data, whether they should have access to it or not.

For example, a sales rep asks their AI solution for product specifications. The AI retrieves the correct information but also pulls in an internal service bulletin about a known product defect intended only for warranty teams. The rep now has access to sensitive service data they weren’t meant to see. If they misunderstand or accidentally share it, this could create unnecessary concerns for distributors and impact sales negotiations.

Why this matters:

• A technician troubleshooting a product shouldn’t be able to see internal financial data.
• A sales rep requesting a feature list shouldn’t receive confidential service updates that impact warranty policies.
• A dealer should access relevant part numbers but not warranty back-office details.

The fix: Circuitry.ai’s approach is different. In addition to offering RBAC, Circuitry.ai allows companies to create separate AI Advisors with distinct knowledge bases. This ensures that each department gets AI-powered guidance based only on the information it needs.

3. Weak authentication and permissions

The problem:
AI solutions without layered authentication and permissions can become an access free-for-all. If there’s no way to verify who is accessing AI-generated knowledge, anyone with a login could surface restricted data.

Why this matters:

• Employees might accidentally expose proprietary product designs or service documentation.
• Third-party users (e.g., dealers and suppliers) could gain access to internal-only knowledge.
• A lack of audit trails makes it hard to track who accessed sensitive information.

The fix: Circuitry.ai enforces secure authentication, access permissions, and monitoring, ensuring that only approved users interact with specific AI Advisors and their knowledge bases. Access settings can be configured based on job function, role, and organizational structure.

4. Inability to track and monitor data access

The problem:
Without clear usage logs, organizations have no way of knowing who accessed what information, which can create compliance risks and make it harder to prevent knowledge misuse.

Why this matters:

• Compliance-heavy industries need to track knowledge access for audits.
• Companies dealing with intellectual property need visibility about who is viewing critical data.
• If knowledge leaks occur, having a record of AI interactions helps identify security gaps.

The fix: Circuitry.ai provides detailed analytics and logs to track every interaction. This ensures complete visibility into how AI Advisors are used, what data is retrieved, and who accessed it.

5. Lack of compliance standards in AI security

The problem:
Some AI solutions might prioritize accessibility over security, but manufacturers need strict data protection to safeguard proprietary knowledge. If your AI solution doesn’t have a dedicated infrastructure and has weak encryption and shared data environments, your information could be vulnerable.

Why this is matters:

• Data breaches from unsecured AI interactions.
• Compliance failures in regulated industries.
• Loss of competitive advantage if sensitive knowledge is exposed.

The fix: Circuitry.ai is built with enterprise-grade security in mind and stringent data privacy protocols to ensure your data is always protected.

Choose an AI solution with security built into every layer

Circuitry.ai enables manufacturers to optimize outcomes with AI-powered Decision Intelligence. Every layer of our AI as a Service platform is designed with security in mind. We leverage secure, reliable, and scalable cloud services from AWS and MS Azure and use strong authentication and encryption practices to keep your data private.

See how Circuitry.ai keeps AI-powered knowledge secure and compliant—schedule your free AI/ROI assessment today.

FAQ: Securing Your AI Solution

1. Why is role-based access control (RBAC) critical for AI security?

RBAC limits access to sensitive information based on user roles, ensuring that only authorized team members can retrieve specific data. Without it, there's a higher risk of data leaks or misuse.

2. How can an AI system expose sensitive information unintentionally?

If the AI draws from a shared, unsegregated knowledge base, it might surface internal-only documents or irrelevant data to the wrong users—leading to potential compliance risks or customer confusion.

3. What’s the value of having access logs in place?

Detailed access logs let you track who viewed what and when. They’re essential for identifying unusual behavior, auditing data usage, and maintaining accountability across your organization.