Why Companies Are Right to Be Cautious About AI in Software Migration (and How to Do It Right)

AI is everywhere. From generating text to automating customer service, AI-driven software is revolutionizing industries. But as the AI hype grows, so does skepticism—especially when it comes to software migration.

Lately, we’ve noticed something interesting: prospective customers are asking more questions than ever about the AI in our products. And their concerns are quite revealing. We’ve had to fill out detailed questionnaires, engage in technical conversations with security teams, and clarify misconceptions—only to find that they’re often worried about a type of AI we don’t even use.

For companies planning large-scale application modernization, AI offers the promise of automation, efficiency, and cost savings. But is it safe? Will it generate reliable and predictable results? Can you trust it with sensitive data?

These concerns are valid, and businesses should be cautious. Many software migration solutions rely on generative AI, which can produce unpredictable and inconsistent results. At Growth Acceleration Partners (GAP), however, we take a safer, more controlled approach using deterministic AI—a method that ensures security, predictability, and compliance.

Before diving into why deterministic AI is the better choice, let’s explore why companies are (rightly) hesitant to trust AI in software migration.

Photo Credit: Variant Fund

Why Companies Are Cautious About AI in Software Solutions

AI in software migration sounds promising, but businesses have several concerns:

• Security & Data Privacy Risks – Many AI models require access to proprietary source code, sensitive business logic, or customer data. If mishandled, these assets could be leaked or misused.
• Lack of Explainability – Generative AI often operates as a "black box," making it difficult to explain why a particular output was generated. This lack of transparency is a major red flag for mission-critical applications.
• Consistency & Predictability – AI-generated code isn't always stable. It may produce different results for the same input, leading to debugging nightmares.
• Regulatory & Compliance Issues – Industries such as finance, healthcare, and government have strict compliance rules (e.g., GDPR, HIPAA, SOC 2). If AI-generated code doesn’t adhere to regulations, companies could face fines or legal risks.

These concerns are amplified when third-party AI systems, especially those running on external cloud platforms, are involved.

Security Concerns of Third-Party AI Systems

Even if an AI-powered migration tool is effective, how it handles security can make or break its viability. Key risks include:

• Cloud-Based AI Risks – Some AI models store, analyze, or even train on data they process. This raises concerns about where the data resides, how long it’s retained, and who has access to it.
• On-Prem vs. Web-Based AI – On-premise AI provides greater control and security but may be harder to scale. Web-based AI is convenient but exposes data to additional risks.
• Malicious or Unintended Outputs – AI-generated code could introduce security vulnerabilities that weren’t in the original source code. Attackers could also manipulate AI systems to generate malicious code or backdoors.
• AI Model Drift – Many AI systems learn and evolve over time. While this is great for general applications, it’s a liability in software migration, where consistency is critical. A generative AI that "learns" may start producing unexpected or incompatible code over time.

And then there’s the problem of AI tarpits—hidden security risks where sensitive user input gets stuck in the AI model.

The Hidden Danger of AI Tarpits: Trapped Sensitive Data

What is an AI Tarpit?

An AI tarpit is a security vulnerability where confidential user input is stored or resurfaced unexpectedly in an AI system.

How Data Gets Trapped in AI Systems

Some AI-powered migration tools may inadvertently store or recall sensitive data. This can happen in several ways:

• Cloud-based AI models may retain data for training or debugging purposes.
• Some AI solutions log prompts and responses without clear disclosure.
• Large language models (LLMs) can unintentionally recall past inputs when generating responses.

Why This is a Security Risk

• Sensitive company data (e.g., proprietary code, credentials, trade secrets) could be retrieved by others.
• AI providers may not have clear retention or deletion policies, making it unclear whether data is truly erased.
• Compliance issues – Many regulations prohibit storing personally identifiable information (PII) or business-critical data without explicit consent.

How Deterministic AI Avoids This Risk

Unlike generative AI, deterministic AI does not retain or learn from previous inputs. It follows strict, rule-based logic to ensure:

• No probabilistic learning – AI doesn’t "guess" or "invent" code.
• No black-box retention of inputs – The system does not store or reuse sensitive data.
• Full transparency – Every step of the migration process is explainable and traceable.

This makes deterministic AI far more secure and predictable than generative AI in software migration.

Deterministic AI vs. Generative AI: Why It Matters

Not all AI is the same. The difference between deterministic AI and generative AI can mean the difference between a successful migration and a security disaster.

What is Deterministic AI?

Deterministic AI is rule-based. It follows strict logic, meaning that the same input always produces the same output.

Generative AI, on the other hand, learns patterns and generates outputs probabilistically—meaning it might produce different results for the same input.

Why Deterministic AI is Safer for Migration

How Growth Acceleration Partners Uses Deterministic AI

At GAP, we leverage deterministic AI to ensure secure, predictable, and high-quality software migrations. Unlike generative AI, which can introduce security risks and unpredictability, our approach:

• Uses predefined rules to translate legacy code into modern frameworks.
• Does not "learn" from previous migrations, eliminating security risks.
• Produces clean, structured, and maintainable code that meets compliance standards.

This ensures a safer, more efficient migration process—without the risks associated with generative AI.

AI can be a powerful tool in software migration, but companies must be cautious.

• Security, data privacy, and predictability are critical.
• Generative AI introduces risks like AI tarpits, inconsistent results, and compliance issues.
• Deterministic AI offers a safer, more controlled approach.

By choosing deterministic AI, companies can modernize their software without sacrificing security, compliance, or reliability.

At Growth Acceleration Partners, we don’t just follow AI trends—we build practical, secure, and predictable AI-powered migration solutions. Want to learn more? Contact us to see how we can help.