Page 38 - Red Hat PR REPORT - OCTOBER 2025

Article

Understanding and Bridging the Gap

Public or hosted AI models offer speed and convenience, but they come with significant
trade-offs. These uses proprietary Large Language Models (LLMs) today such as ChatGPT,,
Gemini, Claude, or are trained on the open internet, not enterprise-specific datasets.

While impressive in general tasks, they often falter when applied to highly specialized or
regulated use cases. More importantly, uploading confidential or sensitive business data into
third-party systems raises serious privacy, compliance, and data sovereignty concerns -
particularly in sectors like government, healthcare, banking, and finance.They come with
significant trade-offs:

1. Data Security & IP Leakage: Sending proprietary data to third-party models risks
exposing sensitive information. Public models are trained on general datasets — not
enterprise-specific data — and offer little visibility or control over where data flows
or how it’s used.

2. Compliance & Governance Challenges: Regulatory environments like GDPR,
HIPAA, and industry-specific mandates require strict controls over data location,

processing, and access. Hosted models often operate in black-box environments that
can’t satisfy audit and compliance requirements.

3. Lack of Personalization: Foundation models are trained on broad, public datasets.
Without access to internal knowledge bases, customer records, or domain-specific
data, their responses remain generic — limiting their usefulness in enterprise
workflows.

That’s why to mitigate these risks and future-proof their operations, companies must
recognize the need to take ownership of their AI infrastructure. They must shift their
position from ‘consuming tools’ to ‘building solutions.’ By developing in-house solutions,
they can maintain full control over their data, customize AI systems to reflect business-
specific contexts, and ensure scalability and governance from the ground up.

The RoleOpen-Source Models and Private AI