Detailed overview
Georgia does not currently have a comprehensive AI Act. Its AI regulation is developing through digital-economy policy, financial-sector AI supervision, model-risk regulation, regulatory sandbox activity and existing laws.
The National Bank of Georgia has launched a pilot AI Sandbox within its Regulatory Laboratory. The project is designed to support safe integration of advanced technologies, including AI, into Georgia's financial and banking sectors. A sandbox allows selected projects to test innovative solutions with regulatory interaction before wider market deployment.
Georgia's financial sector also has a more concrete AI-related regulatory development: the National Bank of Georgia approved a regulation on Data-Driven Statistical, Artificial Intelligence and Machine Learning Model Risk Management. The regulation is intended to establish effective model-risk management and sets basic principles for model development, validation and application.
For financial institutions, AI compliance in Georgia should therefore include model-risk governance. This means identifying which models are used, documenting how they are developed and validated, monitoring performance, controlling operational and data risks, and ensuring that AI or machine-learning models do not create unmanaged risks in business decision-making.
Outside the financial sector, AI is mainly governed by existing law. AI systems may trigger personal-data protection, cybersecurity, consumer protection, employment law, healthcare regulation, public-sector rules, intellectual-property law, civil liability or criminal law.
Georgia does not currently have one general AI-specific penalty table. Penalties depend on the sector and the underlying legal regime breached.
Practical requirements & details
Sourced from the National Bank of Georgia regulation on Data-Driven Statistical, AI and Machine Learning Model Risk Management and the NBG Regulatory Laboratory pilot AI Sandbox.
NBG model-risk regulation
- Applies to financial institutions using data-driven statistical, AI and ML models.
- Establishes effective model-risk management and sets basic principles for model development, validation and application.
Model-risk governance — practical steps
- Identify which models are used.
- Document model development and validation.
- Monitor model performance over time.
- Control operational and data risks.
- Ensure AI/ML models do not create unmanaged risks in business decision-making.
NBG AI Sandbox
- Pilot inside the NBG Regulatory Laboratory.
- Selected projects test innovative solutions with regulatory interaction before wider market deployment.
Penalties
- No general AI-specific penalty table.
- Financial-sector breaches: supervisory measures and sanctions under NBG regulations.
- Outside finance: penalties depend on the underlying regime — data protection, cybersecurity, consumer, employment, healthcare, public-sector, IP, civil or criminal law.