Many SMEs assume that using ChatGPT, Copilot, or similar tools is automatically low risk. In reality, risk depends on deployment context, decision impact, and governance quality. The same tool can be low-risk in one workflow and high-impact in another.
Start with use-context mapping
Break usage into concrete scenarios:
- drafting internal content,
- customer support interactions,
- code generation for production systems,
- candidate screening support,
- policy or eligibility recommendation support.
The first two are often manageable with transparency and quality controls. The latter scenarios can become rights-sensitive if outputs materially influence people.
Practical deployer controls for GPAI users
- Define approved and restricted use cases.
- Require human review for consequential outputs.
- Log key decisions where AI was used.
- Add transparency labels where users interact with AI-generated responses.
- Train staff on prompt hygiene, data handling, and escalation.
Data and confidentiality risk
Teams often overlook data exposure. Build explicit rules for what can/cannot be submitted to external GPAI tools, including customer data, sensitive HR details, and regulated records.
Output reliability and accountability
Hallucination risk is not only technical; it's governance risk. If staff are not trained to verify outputs before action, error chains can become compliance incidents.
Final takeaway
Using GPAI tools is compatible with compliance when use is controlled, transparent, and auditable. Treat GPAI adoption as an operational program, not a plug-and-play shortcut.