Step 3: Assess risk, security, and integrations

Protecting performance, trust, and scalability in a mission-critical AI system.

Why risk must be addressed upfront

When AI becomes part of your customer-facing infrastructure, the margin for error shrinks dramatically. A single misrouted action, a broken API call, or a compliance slip can damage trust within minutes. That’s why evaluating risk isn’t a late-stage task, it’s the foundation of any scalable AI program.

Most AI failures in production aren’t caused by the model itself. They stem from everything around it: inconsistent integrations, missing guardrails, weak monitoring, or insufficient fallbacks. If the surrounding architecture isn’t engineered for enterprise reliability, even the best AI will behave unpredictably under real-world load.

Effective AI leaders start by defining the conditions under which failure is unacceptable. This mindset turns risk management into a design principle, not an afterthought.

A real-world example: CorVel operates in a highly regulated, risk-sensitive domain and relies on complete visibility into every customer interaction. By partnering instead of building internally, they achieved 100% analysis of all customer calls, ensuring full auditability, predictable behavior, and reliable oversight across complex workflows, a level of observability and compliance that is difficult to achieve in a DIY environment.

Begin by defining what must never happen

Before deciding what AI should do, outline what it must not do. This provides a boundary for safe operation and informs your architecture and governance model.

Imagine situations such as:

• An interaction continues without required authentication
• The system shares incorrect or unauthorized information
• Latency spikes force customers to abandon the experience
• AI repeats itself or loses state mid-conversation
• A workflow “silently fails” without escalating to a human
• A generated response contradicts policy or regulatory requirements
  

These risks are not hypothetical, they’re the real-world scenarios enterprise systems must avoid. By articulating them early, you design with prevention and detection in mind.

Engineering predictability through deterministic guardrails

LLMs are powerful, but they are also inherently unpredictable. They can hallucinate, skip steps, or take actions that violate policy if left unguided. For enterprise leaders, this unpredictability is the core risk: you cannot rely on a generative model to remember rules — you must enforce them.

Many teams attempt to control LLM behavior using only prompts or secondary models that “check” the output. But this is like telling someone not to touch a hot stove, it relies on cooperation rather than control. You’re hoping the model behaves correctly instead of guaranteeing it.

Deterministic guardrails remove that uncertainty. They bake the rules into the architecture itself, ensuring the AI cannot bypass required steps, perform restricted actions, or access sensitive information without the proper conditions being met.

These guardrails function as the AI’s operating system:

• They enforce mandatory workflow sequences
• They validate data and action prerequisites
• They block unsafe or unauthorized behaviors outright
• They ensure the AI cannot improvise where precision is required
  

With guardrails in place, compliance and accuracy are not dependent on model behavior, they are enforced by the system. This eliminates the risk of hallucinations, prevents policy violations, and creates predictable, repeatable behavior even as underlying models evolve.

Guardrails don’t advise the AI. They constrain it. And constraint is what makes enterprise-grade AI safe, stable, and trustworthy.

Security must start at zero trust

AI adds new entry points into your environment, often faster than traditional software. With that speed comes increased exposure if security isn’t fully integrated.

A zero-trust approach ensures every interaction, system call, and data exchange is verified, permissioned, and logged. Security best practices include:

• Encryption of all data in transit and at rest
• Automatic redaction of PII/PCI/PHI across both text and audio
• Comprehensive access control and role-based permissions
• Logging and audit trails that support compliance investigations
• Monitoring for adversarial prompts or anomalous input patterns
  

Security isn’t a feature. It’s the ongoing guarantee that every customer interaction remains protected, regardless of scale.

Treat integrations as a first-class risk surface

AI rarely fails because “the AI didn’t understand.” It fails because an external system responded slowly, a schema changed unexpectedly, or a service timed out. The deeper your integration surface, the more disciplined your architecture must be.

Your AI must integrate cleanly with your CRM, CCaaS, telephony, ticketing systems, scheduling tools, and knowledge bases. But clean integration doesn’t just mean connecting, it means creating:

• Clear contracts and schema validation
• Retry logic and timeout handling
• State management that persists across systems
• Version control across all dependencies
• Consistent behavior across channels
  

Every integration either increases confidence or increases fragility.

Why this makes DIY approaches unsustainable

Once you consider guardrails, redundancy, observability, security, and integration stability, the operational lift becomes clear. Building AI internally means owning:

• Uptime
• Drift detection
• Security posture
• Compliance
• Incident response
• Multi-vendor management
• Continuous integration and regression testing
  

This is a heavy burden for most IT teams already managing complex infrastructures.

A DIY approach diverts engineering resources away from high-value initiatives and turns AI into a perpetual maintenance obligation, not an accelerant.

‍The goal of this step is simple: to show that AI adoption isn’t about models, it’s about the systems that keep them safe, stable, and compliant. And those systems are rarely cost-effective to build alone.

Let's move on to Step 4 - The first use case (and bringing stakeholders along).