AICPA AI Implementation Guidance

The AICPA Technology Strategic Advisory Group published a document: “Step-by-Step Guide to Evaluating and Selecting AI Models for Business.”

If your organization is considering AI adoption, this guide is a solid, vendor-neutral starting point, because it focuses on how to make a defensible business decision rather than getting lost in technical hype.

Photo: Freepik

What I like about it is the structured approach: it acknowledges the reality that many AI initiatives fail or stall, and it proposes a repeatable evaluation path that reduces selection risk and helps align stakeholders.

What’s inside

The document is organized as a phase-based framework that takes you from “why AI?” to “ready for production,” including templates and scoring methods:

Define business requirements first

It warns that starting with technology (instead of a business problem) is a common failure pattern, and it pushes for clear success metrics and current-state assessment.

Data readiness

It emphasizes that data quality and readiness are often the biggest blockers, recommending a structured data audit.

The right model type

It compares model types and highlights tradeoffs like cost, hallucinations, dataset needs, and interpretability.

Use an evaluation matrix

It proposes an evaluation matrix (accuracy, performance, reliability, scalability, integration, cost) and adds business criteria such as vendor stability, support, compliance and security.

Pilot testing

It stresses that lab results rarely match reality and recommends staged pilots (PoC → limited pilot → expanded testing → final validation).

Plan implementation

It moves into implementation planning, including training, change management, rollback planning, and continuous monitoring.

Overall: it’s a business-friendly roadmap that helps organizations move from “AI curiosity” to an accountable decision trail.

Cybersecurity risks in AI

This is where many AI initiatives underestimate risk. Even with a strong business case, AI introduces new attack surfaces and amplifies existing ones.In practice, the most common cybersecurity risk areas include:

➡️ Data leakage and over-sharing

➡️ Prompt injection and tool abuse

➡️ Model supply chain risk

➡️ Identity and secrets exposure

➡️ Monitoring gaps

Depending on what you deploy (LLM, RAG, AIagents, autonomous workflows), security teams should incorporate AI-specific threat and control references such as MITREATLAS, OWASP Top 10 for LLM / Agentic Applications, and Cloud Security Alliance publications like Securing Autonomous AI Agents and the AI Control Matrix.

AI selection is step one.

SecureAI implementation is where the real risk management begins.

Link

Author: Sebastian Burgemejster