5 Principles for Building Safe, Effective Enterprise AI Systems

In March 2024, the European Union passed the AI Act. This sweeping regulation reshapes how organizations deploy and manage AI systems. 

The law addresses AI risks that could affect both individuals and businesses, from hiring biases to critical infrastructure failures. Similar rules have started taking shape across the world, including several state-based regulations in the US. 

But the regulation is only the floor. We all share the responsibility of creating a safe, responsible AI future. This not only protects people, but also safeguards the bottom line for companies by building trust and preventing harmful errors. 

This blog covers what organizations need to build a safe AI future—and how to do so while still innovating.

AI moves fast. And in the agentic AI era, systems act on their own. That drives efficiency. But when AI makes bad calls on critical processes like hiring or banking, that fallout harms people as well as your organization’s reputation. 

The AI Act sets important limits on AI usage by banning AI in processes with unacceptable risks and enforcing guardrails on high-risk processes. This protects your business. But it also creates a shared ethical starting line, so everyone innovates from a level starting point. 

It aims to address such critical issues as: 

• Spam and misinformation

• Hallucinations that can cause harmful errors in critical processes

• Bias that could entrench harmful social stereotypes

• Black-box complexity that makes it difficult to keep AI accountable

In other words, it addresses many of the critical safety issues inherent in AI usage.

But the AI Act was only the starting point. And while laws may change and new ones will emerge, safe AI will remain a perennial concern. It’s tied to business value, not just compliance. So how do you build and innovate while that’s in mind?

So the stakes are clear. How do you deploy AI with safety and control? Follow these five principles.

AI agents can sometimes make poor choices due to hallucinations, data input issues, or reasoning errors. But AI decision-making can often be opaque. If hidden from view, you can’t track the AI’s reasoning (or show proof to compliance officers). 

Traceability is a principle that counters this by tracking every action a system, including AI, takes. Traceable systems help you see when a decision was made, what the reasoning was, and what data was used. This lets you: 

1. Review decisions on high-risk processes 
2. Have AI uplevel to humans when needed
3. Provide transparency to compliance officers
4. Audit and improve AI performance and accuracy over time

Plus, with process intelligence, you can track how well AI contributes to business goals like efficiency or ROI.

User permissions are already a strong cornerstone for cybersecurity across IT systems, applications, and data. This prevents data leaks from human actors. 

The same applies to AI. Think of AI agents as workers—they should stick to their own lane. And they must take care to avoid showing data to the wrong people or in the wrong process. For instance, if someone asks a chatbot about an employee’s salary, AI should deny that request (unless they work in HR and need access to that information). 

A data fabric simplifies permission management. Set permissions once on a record, then any connected system inherits the permissions. This ensures consistent access controls even across complex enterprise environments without having to maintain them across multiple enterprise systems. 

The EU AI Act is just one piece of the compliance puzzle. You already know which regulations your organization falls under. But think beyond your industry—any vendor with significant, well-trusted certifications like FedRAMP, HIPAA, or SOC II/III takes security seriously. That’s critical for the credibility of your vendor.

Most vendors also publish broad security practices on their Trust Center sites. Take these seriously. You want to look for layered, defense-in-depth practices. And think beyond preventive security. It’s often how a vendor responds to a potential breach—such as shutting down systems, tracking intrusions, and notifying users—that truly bolsters a security posture. Check for elements like continuous monitoring, incident response, and user notifications to know that the organization will be transparent and responsible with your data and systems.

Many AI models and vendors ask you to cede control of your data. They use it to perfect their own algorithms, often helping your competitors using your work and input. Beyond that, sharing data with a third-party like an LLM opens another potential attack vector if data leaks. Even if chances are small, why take the risk? 

When looking at AI vendors, make sure they emphasize private AI usage. This means they: 

1. Never share your data with third parties 
2. Keep data within your compliance boundary (so you have control over who sees it internally and who doesn’t)
3. Don’t train their own algorithms on your data

When the stakes are high, privacy isn’t optional. It’s the baseline for trust, resilience, and safety.

AI works best inside a structured process. It’s both safer and more effective. 

Process gives AI context. It defines the role of AI agents—what they should do, when to escalate to humans, and how each action gets audited and logged. AI should be part of a process, not the sole decision-maker.

With a good process platform you get the ability to: 

• Adapt quickly by adding AI easily to a process task

• Give AI a critical role, with rules on upleveling to humans for oversight

• Connect to real-time data to provide context to AI agents

• Monitor performance over time to continuously improve

• Scale across processes both easily and safely with enterprise-grade platforms

Process helps you innovate while retaining safety. If process is the car, AI is the safe, yet powerful engine. In other words, with clear structure, AI doesn’t just move faster. It moves safer.