Security, compliance, and control differences between public AI services and private AI deployments. Includes a decision framework.
Best for: IT leaders, compliance officersPractical guide for business decision-makers
Who this is for
IT leaders, compliance officers, and business owners evaluating AI deployment options for data-sensitive environments.
Question this answers
Should we use public AI services (like ChatGPT, Azure OpenAI) or deploy AI privately on our own infrastructure?
What you'll leave with
What public and private AI actually mean in practice
Security and compliance implications of each approach
Cost and capability tradeoffs
When hybrid approaches make sense
The core difference
When we say "public AI" and "private AI," we're talking about where the AI runs and where your data goes, not whether the technology is open-source or proprietary.
Public AI: Your data is sent to a third-party provider's servers for processing. Examples: ChatGPT, Google Gemini, Azure OpenAI Service.
Private AI: The AI model runs on your infrastructure (or a dedicated cloud instance). Your data never leaves your control.
Public AI: what you get
Advantages:
Access to the most powerful models (GPT-4o, Claude, Gemini)
No infrastructure to manage
Pay-per-use pricing with a low barrier to entry
Latest capabilities automatically available
Enterprise plans include data processing agreements and security certifications
Concerns:
Data is processed on third-party servers
Data may be used for model training (free tiers). Enterprise plans usually opt out
No control over model updates that might change behaviour
Vendor lock-in risk
May not comply with data residency requirements (Australian data staying in Australia)
Private AI: what you get
Advantages:
Complete data control. Nothing leaves your environment
Compliance with strict data residency and privacy requirements
No vendor dependency on model availability or pricing
Customisable, with the ability to fine-tune models to your specific domain
Predictable cost at scale (no per-token pricing)
Concerns:
Smaller models. Open-source models are capable but not yet at GPT-4o level for all tasks
Infrastructure cost and management overhead
Requires technical expertise to deploy and maintain
GPU compute costs for running large models can be significant
Public vs private AI deployment
Criterion
Public AI
Private AI
Data location
Provider's servers
Your infrastructure
Model quality
Best available (GPT-4o, Claude)
Good and improving (Llama, Mistral)
Setup cost
Low ($0-$5K)
Higher ($15K-$50K)
Running cost
Per-token (scales with usage)
Fixed infrastructure (predictable)
Data privacy
Provider-dependent
Full control
Compliance
Enterprise plans offer compliance
Strongest compliance position
Maintenance
Provider handles it
You manage it (or your vendor does)
Customisation
Limited (prompting, fine-tuning via API)
Full (fine-tuning, custom training)
Decision framework
Use public AI when
✓
Data is non-sensitive (public information, general queries)
✓
You need the best model quality available
✓
Volume is low to moderate (per-token pricing is affordable)
✓
Fast implementation is important
✓
No data residency requirements apply
Use private AI when
✓
Data includes personal, health, or financial information
✓
Regulatory compliance requires data to stay in your control
✓
Volume is high enough that per-token pricing is expensive
✓
You need full control over model behaviour and updates
✓
Data residency requirements apply (data must stay in Australia)
Hybrid options
Most businesses benefit from a hybrid approach:
Public AI for general tasks: Content drafting, research, brainstorming, analysis of non-sensitive data
Private AI for sensitive tasks: Customer data processing, internal knowledge Q&A, compliance-related workflows
Key takeaways
Public AI sends your data to third-party servers. Even with enterprise plans, data leaves your control
Private AI keeps data on your infrastructure but costs more and requires more technical capability
Most Australian businesses can use public AI for non-sensitive tasks with appropriate policies
Regulated industries (healthcare, finance, government) should default to private AI for sensitive data
Hybrid approaches work best for most businesses: public AI for general tasks, private AI for sensitive data
