ELP GPT
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ELP GPT/Safety approach

ELP GPT / SAFETY

Security and safety by design

ELP GPT couples proprietary intelligence with bounded authority, independent validation and controls appropriate to each task.

OUR APPROACH

Safety approach: principles in practice

01

Require explicit authority for consequential actions

Risk evaluation covers model content, prompt injection, tool permissions, data leaks, provider failure, human misuse and rollback readiness. Organizational controls depend on implementation and deployment.

02

Evaluate model and tool risks together

ELP GPT connects proprietary engineering to responsible, task-specific customer outcomes.

03

Prioritize transparency when limitations are material

ELP GPT connects proprietary engineering to responsible, task-specific customer outcomes.

THE ELP GPT PERSPECTIVE

Safety approach — engineering and execution

Security controls are part of the architecture, not a blanket claim of invulnerability.

The safety architecture distinguishes generating a suggestion from executing a consequential action. Access to connected systems is permissioned; systems require appropriate identity checks, scope checks and review for higher-impact actions.

Technology, ownership & transparency

ELP GPT is developed by ELP Ventures. ELP-owned software and model research are distinct from independently owned inference services, third-party libraries and intellectual property. Specifications, availability and comparative performance depend on the deployed configuration.

Research and safety reference frameworks

Research and governance methods can be assessed against public resources such as the NIST AI Risk Management Framework and the European Commission guidance on general-purpose AI. These references inform evaluation and do not mean ELP GPT is certified or endorsed.